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GitHub - Heerozh/spectre: GPU-accelerated Factors analysis library and Backtester
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22.4362C9.07125 22.5325 9.2775 22.2025 9.2775 21.9137C9.2775 21.6525 9.26375 20.7862 9.26375 19.865C6.5 20.3737 5.785 19.1912 5.565 18.5725C5.44125 18.2562 4.905 17.28 4.4375 17.0187C4.0525 16.8125 3.5025 16.3037 4.42375 16.29C5.29 16.2762 5.90875 17.0875 6.115 17.4175C7.105 19.0812 8.68625 18.6137 9.31875 18.325C9.415 17.61 9.70375 17.1287 10.02 16.8537C7.5725 16.5787 5.015 15.63 5.015 11.4225C5.015 10.2262 5.44125 9.23625 6.1425 8.46625C6.0325 8.19125 5.6475 7.06375 6.2525 5.55125C6.2525 5.55125 7.17375 5.2625 9.2775 6.67875C10.1575 6.43125 11.0925 6.3075 12.0275 6.3075C12.9625 6.3075 13.8975 6.43125 14.7775 6.67875C16.8813 5.24875 17.8025 5.55125 17.8025 5.55125C18.4075 7.06375 18.0225 8.19125 17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 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flex-auto rounded rounded-lg-0"> <nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon 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href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-issue-opened color-fg-subtle mr-3"> <path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 2 0 0 1 12 14Z"></path> </svg> <div> <div class="color-fg-default h4">Issues</div> Plan and track work </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"code_review","context":"product","tag":"link","label":"code_review_link_product_navbar"}" href="https://github.com/features/code-review"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-review color-fg-subtle mr-3"> <path d="M10.3 6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 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data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M2 3.75C2 2.784 2.784 2 3.75 2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="product-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="product-explore-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"all_features","context":"product","tag":"link","label":"all_features_link_product_navbar"}" href="https://github.com/features"> All features </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"documentation","context":"product","tag":"link","label":"documentation_link_product_navbar"}" href="https://docs.github.com"> Documentation <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 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id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" 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6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="Sb5mUN1zuGDMVsG2ocYzIEdSOKmvVkk7GoOvTOa02MGKUsl8zyAuS2mROgfmNTxOuFNAaMdlAVhCkow10ucwBg==" /> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <input type="hidden" 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src=\"https://camo.githubusercontent.com/e8780bfcf770a304370aef53317d651295a584fe9cb3a6db13eb3c004037a134/68747470733a2f2f636f766572616c6c732e696f2f7265706f732f6769746875622f486565726f7a682f737065637472652f62616467652e7376673f6272616e63683d6d6173746572\" alt=\"Coverage Status\" data-canonical-src=\"https://coveralls.io/repos/github/Heerozh/spectre/badge.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e||spectre\u003c/h1\u003e\u003ca id=\"user-content-spectre\" class=\"anchor\" aria-label=\"Permalink: ||spectre\" href=\"#spectre\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003espectre is a \u003cstrong\u003eGPU-accelerated Parallel\u003c/strong\u003e quantitative trading library, focused on \u003cstrong\u003eperformance\u003c/strong\u003e.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFast GPU Factor Engine, see below \u003ca href=\"#benchmarks\"\u003eBenchmarks\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePure python code, based on PyTorch, so it can integrate DL model very smoothly.\u003c/li\u003e\n\u003cli\u003eCompatible with \u003ccode\u003ealphalens\u003c/code\u003e and \u003ccode\u003epyfolio\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003ePython 3.7+, PyTorch 1.3+, Pandas 1.0+ recommended\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install --no-deps git+git://github.com/Heerozh/spectre.git\"\u003e\u003cpre\u003epip install --no-deps git+git://github.com/Heerozh/spectre.git\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDependencies:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch\nconda install pyarrow pandas tqdm plotly requests\"\u003e\u003cpre\u003econda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch\nconda install pyarrow pandas tqdm plotly requests\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBenchmarks\u003c/h1\u003e\u003ca id=\"user-content-benchmarks\" class=\"anchor\" aria-label=\"Permalink: Benchmarks\" href=\"#benchmarks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eMy Machine：\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ei9-7900X @ 3.30GHz, 20 Cores\u003c/li\u003e\n\u003cli\u003eDDR4 3800MHz\u003c/li\u003e\n\u003cli\u003e3090: GIGABYTE GeForce RTX 3090 GAMING OC 24G\u003c/li\u003e\n\u003cli\u003e2080Ti: RTX 2080Ti Founders\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eRunning on Quandl 5 years, 3196 Assets, total 3,637,344 bars.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003espectre (CUDA/3090)\u003c/th\u003e\n\u003cth\u003espectre (CUDA/2080Ti)\u003c/th\u003e\n\u003cth\u003espectre (CPU)\u003c/th\u003e\n\u003cth\u003ezipline.pipeline\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSMA(100)\u003c/td\u003e\n\u003ctd\u003e87.9 ms ± 3.35 ms (\u003cstrong\u003e33.9x\u003c/strong\u003e)\u003c/td\u003e\n\u003ctd\u003e144 ms ± 974 µs (\u003cstrong\u003e20.7x\u003c/strong\u003e)\u003c/td\u003e\n\u003ctd\u003e2.68 s ± 36.1 ms (1.11x)\u003c/td\u003e\n\u003ctd\u003e2.98 s ± 14.4 ms (1x)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEMA(50) win=229\u003c/td\u003e\n\u003ctd\u003e166 ms ± 3.25 ms (\u003cstrong\u003e50.5x\u003c/strong\u003e)\u003c/td\u003e\n\u003ctd\u003e270 ms ± 1.89 ms (\u003cstrong\u003e31.0x\u003c/strong\u003e)\u003c/td\u003e\n\u003ctd\u003e4.37 s ± 46.4 ms (1.74x)\u003c/td\u003e\n\u003ctd\u003e8.38 s ± 56.8 ms (1x)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e(MACD+RSI+STOCHF).rank.zscore\u003c/td\u003e\n\u003ctd\u003e184 ms ± 7.83 ms (\u003cstrong\u003e77.7x\u003c/strong\u003e)\u003c/td\u003e\n\u003ctd\u003e282 ms ± 1.33 ms (\u003cstrong\u003e50.7x\u003c/strong\u003e)\u003c/td\u003e\n\u003ctd\u003e6.01 s ± 28.1 (2.38x)\u003c/td\u003e\n\u003ctd\u003e14.3 s ± 277 ms (1x)\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe CUDA memory used in the spectre benchmark is 1.8G, returned by cuda.max_memory_allocated().\u003c/li\u003e\n\u003cli\u003eBenchmarks excluded the initial run (no copy data to VRAM, about saving 300ms).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuick Start\u003c/h1\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDataLoader\u003c/h2\u003e\u003ca id=\"user-content-dataloader\" class=\"anchor\" aria-label=\"Permalink: DataLoader\" href=\"#dataloader\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFirst of all is data, you can use \u003ca href=\"#csvdirloader\"\u003eCsvDirLoader\u003c/a\u003e read your csv files.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003espectre also has built-in Yahoo downloader, \u003ccode\u003esymbols=None\u003c/code\u003e will download all SP500 components.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from spectre.data import YahooDownloader\nYahooDownloader.ingest(start_date=\u0026quot;2001\u0026quot;, save_to=\u0026quot;./prices/yahoo\u0026quot;, symbols=None, skip_exists=True)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eYahooDownloader\u003c/span\u003e\n\u003cspan class=\"pl-v\"\u003eYahooDownloader\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eingest\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003estart_date\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"2001\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esave_to\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"./prices/yahoo\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esymbols\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eskip_exists\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can use \u003ccode\u003espectre.data.ArrowLoader('./prices/yahoo/yahoo.feather')\u003c/code\u003e load those data now.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactor and FactorEngine\u003c/h2\u003e\u003ca id=\"user-content-factor-and-factorengine\" class=\"anchor\" aria-label=\"Permalink: Factor and FactorEngine\" href=\"#factor-and-factorengine\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from spectre import factors\nfrom spectre.data import ArrowLoader\nloader = ArrowLoader('./prices/yahoo/yahoo.feather')\nengine = factors.FactorEngine(loader)\nengine.to_cuda()\nengine.add(factors.SMA(5), 'ma5')\nengine.add(factors.OHLCV.close, 'close')\ndf = engine.run('2019-01-11', '2019-01-15')\ndf\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eArrowLoader\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eloader\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eArrowLoader\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'./prices/yahoo/yahoo.feather'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eFactorEngine\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eloader\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eto_cuda\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eadd\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eSMA\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e), \u003cspan class=\"pl-s\"\u003e'ma5'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eadd\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'close'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erun\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'2019-01-11'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'2019-01-15'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003ema5\u003c/th\u003e\n\u003cth\u003eclose\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003edate\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003cstrong\u003easset\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2019-01-14 00:00:00+00:00\u003c/td\u003e\n\u003ctd\u003eA\u003c/td\u003e\n\u003ctd\u003e68.842003\u003c/td\u003e\n\u003ctd\u003e70.379997\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eAAPL\u003c/td\u003e\n\u003ctd\u003e151.615997\u003c/td\u003e\n\u003ctd\u003e152.289993\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eABC\u003c/td\u003e\n\u003ctd\u003e75.835999\u003c/td\u003e\n\u003ctd\u003e76.559998\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eABT\u003c/td\u003e\n\u003ctd\u003e69.056000\u003c/td\u003e\n\u003ctd\u003e69.330002\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eADBE\u003c/td\u003e\n\u003ctd\u003e234.537994\u003c/td\u003e\n\u003ctd\u003e237.550003\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003ctd\u003e...\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e2019-01-15 00:00:00+00:00\u003c/td\u003e\n\u003ctd\u003eXYL\u003c/td\u003e\n\u003ctd\u003e68.322006\u003c/td\u003e\n\u003ctd\u003e69.160004\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eYUM\u003c/td\u003e\n\u003ctd\u003e91.010002\u003c/td\u003e\n\u003ctd\u003e90.000000\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eZBH\u003c/td\u003e\n\u003ctd\u003e102.932007\u003c/td\u003e\n\u003ctd\u003e102.690002\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eZION\u003c/td\u003e\n\u003ctd\u003e43.760002\u003c/td\u003e\n\u003ctd\u003e44.320000\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003eZTS\u003c/td\u003e\n\u003ctd\u003e85.846001\u003c/td\u003e\n\u003ctd\u003e84.500000\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactor Analysis\u003c/h2\u003e\u003ca id=\"user-content-factor-analysis\" class=\"anchor\" aria-label=\"Permalink: Factor Analysis\" href=\"#factor-analysis\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from spectre import factors\nimport math\n\nrisk_free_rate = 0.04 / 252\nexcess_logret = factors.LogReturns() - math.log(1 + risk_free_rate)\nuniverse = factors.AverageDollarVolume(win=120).top(100)\n\n# Barra MOMENTUM\nema126 = factors.EMA(half_life=126, inputs=[excess_logret])\nrstr = ema126.shift(11).sum(252)\nMOMENTUM = rstr\n\n# Barra Volatility\nema42 = factors.EMA(half_life=42, inputs=[excess_logret])\ndastd = factors.STDDEV(252, inputs=[ema42])\nVOLATILITY = dastd\n\n# run engine\nfrom spectre.data import ArrowLoader\nloader = ArrowLoader('./prices/yahoo/yahoo.feather')\nengine = factors.FactorEngine(loader)\n\nengine.set_filter( universe )\nengine.add( MOMENTUM, 'MOMENTUM' )\nengine.add( VOLATILITY, 'VOLATILITY' )\n\nengine.to_cuda()\n%time factor_data, mean_return = engine.full_run(\u0026quot;2013-01-02\u0026quot;, \u0026quot;2018-01-19\u0026quot;, periods=(1,5,10,))\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emath\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003erisk_free_rate\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.04\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e252\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eexcess_logret\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eLogReturns\u003c/span\u003e() \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emath\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003elog\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erisk_free_rate\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003euniverse\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eAverageDollarVolume\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e120\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003etop\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Barra MOMENTUM\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eema126\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eEMA\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ehalf_life\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e126\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003eexcess_logret\u003c/span\u003e])\n\u003cspan class=\"pl-s1\"\u003erstr\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eema126\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eshift\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e11\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003esum\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e252\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003eMOMENTUM\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erstr\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Barra Volatility\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eema42\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eEMA\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ehalf_life\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e42\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003eexcess_logret\u003c/span\u003e])\n\u003cspan class=\"pl-s1\"\u003edastd\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eSTDDEV\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e252\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003eema42\u003c/span\u003e])\n\u003cspan class=\"pl-c1\"\u003eVOLATILITY\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edastd\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# run engine\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eArrowLoader\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eloader\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eArrowLoader\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'./prices/yahoo/yahoo.feather'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eFactorEngine\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eloader\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eset_filter\u003c/span\u003e( \u003cspan class=\"pl-s1\"\u003euniverse\u003c/span\u003e )\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eadd\u003c/span\u003e( \u003cspan class=\"pl-c1\"\u003eMOMENTUM\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'MOMENTUM'\u003c/span\u003e )\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eadd\u003c/span\u003e( \u003cspan class=\"pl-c1\"\u003eVOLATILITY\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'VOLATILITY'\u003c/span\u003e )\n\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eto_cuda\u003c/span\u003e()\n\u003cspan class=\"pl-c1\"\u003e%\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etime\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor_data\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emean_return\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efull_run\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"2013-01-02\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"2018-01-19\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eperiods\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e,\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e,))\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Heerozh/spectre/raw/media/full_run.png\"\u003e\u003cimg src=\"https://github.com/Heerozh/spectre/raw/media/full_run.png\" width=\"800\" height=\"600\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDiagram\u003c/h3\u003e\u003ca id=\"user-content-diagram\" class=\"anchor\" aria-label=\"Permalink: Diagram\" href=\"#diagram\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can also view your factor structure graphically:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"factors.BBANDS(win=5).normalized().rank().zscore().show_graph()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eBBANDS\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003enormalized\u003c/span\u003e().\u003cspan class=\"pl-c1\"\u003erank\u003c/span\u003e().\u003cspan class=\"pl-c1\"\u003ezscore\u003c/span\u003e().\u003cspan class=\"pl-c1\"\u003eshow_graph\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Heerozh/spectre/raw/media/factor_diagram.png\"\u003e\u003cimg src=\"https://github.com/Heerozh/spectre/raw/media/factor_diagram.png\" width=\"800\" height=\"360\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe thickness of the line represents the length of the Rolling Window, kind of like \"bandwidth\".\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf \u003ccode\u003eengine.to_cuda(enable_stream=True)\u003c/code\u003e, the calculation of the branches will be performed\nsimultaneously, but the VRAM usage will increase proportionally.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCompatible with alphalens\u003c/h3\u003e\u003ca id=\"user-content-compatible-with-alphalens\" class=\"anchor\" aria-label=\"Permalink: Compatible with alphalens\" href=\"#compatible-with-alphalens\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe return value of \u003ccode\u003efull_run\u003c/code\u003e is compatible with \u003ccode\u003ealphalens\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import alphalens as al\n...\nfactor_data, _ = engine.full_run(\u0026quot;2013-01-02\u0026quot;, \u0026quot;2018-01-19\u0026quot;)\nclean_data = factor_data[['{factor_name}', 'Returns']].droplevel(0, axis=1)\nal.tears.create_full_tear_sheet(clean_data)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ealphalens\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eal\u003c/span\u003e\n...\n\u003cspan class=\"pl-s1\"\u003efactor_data\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003e_\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efull_run\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"2013-01-02\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"2018-01-19\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eclean_data\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor_data\u003c/span\u003e[[\u003cspan class=\"pl-s\"\u003e'{factor_name}'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'Returns'\u003c/span\u003e]].\u003cspan class=\"pl-c1\"\u003edroplevel\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eaxis\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eal\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etears\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecreate_full_tear_sheet\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eclean_data\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBack-testing\u003c/h2\u003e\u003ca id=\"user-content-back-testing\" class=\"anchor\" aria-label=\"Permalink: Back-testing\" href=\"#back-testing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBack-testing uses FactorEngine's results as data, market event as triggers.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can find other examples in the \u003ccode\u003e./examples\u003c/code\u003e directory.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from spectre import factors, trading\nfrom spectre.data import ArrowLoader\nimport pandas as pd, math\n\n\nclass MyAlg(trading.CustomAlgorithm):\n def initialize(self):\n # your factors\n risk_free_rate = 0.04 / 252\n excess_logret = factors.LogReturns() - math.log(1 + risk_free_rate)\n universe = factors.AverageDollarVolume(win=120).top(100)\n\n # Barra MOMENTUM Risk Factor\n ema126 = factors.EMA(half_life=126, inputs=[excess_logret])\n rstr = ema126.shift(11).sum(252)\n MOMENTUM = rstr.zscore(mask=universe)\n\n # Barra Volatility Risk Factor\n ema42 = factors.EMA(half_life=42, inputs=[excess_logret])\n dastd = factors.STDDEV(252, inputs=[ema42])\n VOLATILITY = dastd.zscore(mask=universe)\n\n # setup engine\n engine = self.get_factor_engine()\n engine.to_cuda()\n engine.set_filter( universe )\n engine.add( (MOMENTUM + VOLATILITY).to_weight(), 'alpha_weight' )\n\n # schedule rebalance before market close\n self.schedule_rebalance(trading.event.MarketClose(self.rebalance, offset_ns=-10000))\n\n # simulation parameters\n self.blotter.capital_base = 1000000\n self.blotter.set_commission(percentage=0, per_share=0.005, minimum=1)\n # self.blotter.set_slippage(percentage=0, per_share=0.4)\n\n def rebalance(self, data: 'pd.DataFrame', history: 'pd.DataFrame'):\n data = data.fillna(0)\n self.blotter.batch_order_target_percent(data.index, data.alpha_weight)\n\n # closing asset position that are no longer in our universe.\n removes = self.blotter.portfolio.positions.keys() - set(data.index)\n self.blotter.batch_order_target_percent(removes, [0] * len(removes))\n\n # record data for debugging / plotting\n self.record(aapl_weight=data.loc['AAPL', 'alpha_weight'],\n aapl_price=self.blotter.get_price('AAPL'))\n\n def terminate(self, records: 'pd.DataFrame'):\n # plotting results\n self.plot(benchmark='SPY')\n\n # plotting the relationship between AAPL price and weight\n ax1 = records.aapl_price.plot()\n ax2 = ax1.twinx()\n records.aapl_weight.plot(ax=ax2, style='g-')\n\nloader = ArrowLoader('./prices/yahoo/yahoo.feather')\n%time results = trading.run_backtest(loader, MyAlg, '2014-01-01', '2019-01-01')\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etrading\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eArrowLoader\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epandas\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003emath\u003c/span\u003e\n\n\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMyAlg\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrading\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eCustomAlgorithm\u003c/span\u003e):\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003einitialize\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n \u003cspan class=\"pl-c\"\u003e# your factors\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003erisk_free_rate\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0.04\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e252\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eexcess_logret\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eLogReturns\u003c/span\u003e() \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emath\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003elog\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erisk_free_rate\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003euniverse\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eAverageDollarVolume\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e120\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003etop\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e)\n\n \u003cspan class=\"pl-c\"\u003e# Barra MOMENTUM Risk Factor\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eema126\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eEMA\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ehalf_life\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e126\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003eexcess_logret\u003c/span\u003e])\n \u003cspan class=\"pl-s1\"\u003erstr\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eema126\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eshift\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e11\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003esum\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e252\u003c/span\u003e)\n \u003cspan class=\"pl-c1\"\u003eMOMENTUM\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erstr\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ezscore\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emask\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003euniverse\u003c/span\u003e)\n\n \u003cspan class=\"pl-c\"\u003e# Barra Volatility Risk Factor\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eema42\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eEMA\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ehalf_life\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e42\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003eexcess_logret\u003c/span\u003e])\n \u003cspan class=\"pl-s1\"\u003edastd\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eSTDDEV\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e252\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003eema42\u003c/span\u003e])\n \u003cspan class=\"pl-c1\"\u003eVOLATILITY\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edastd\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ezscore\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emask\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003euniverse\u003c/span\u003e)\n\n \u003cspan class=\"pl-c\"\u003e# setup engine\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eget_factor_engine\u003c/span\u003e()\n \u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eto_cuda\u003c/span\u003e()\n \u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eset_filter\u003c/span\u003e( \u003cspan class=\"pl-s1\"\u003euniverse\u003c/span\u003e )\n \u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eadd\u003c/span\u003e( (\u003cspan class=\"pl-c1\"\u003eMOMENTUM\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eVOLATILITY\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003eto_weight\u003c/span\u003e(), \u003cspan class=\"pl-s\"\u003e'alpha_weight'\u003c/span\u003e )\n\n \u003cspan class=\"pl-c\"\u003e# schedule rebalance before market close\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eschedule_rebalance\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrading\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eevent\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eMarketClose\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erebalance\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoffset_ns\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10000\u003c/span\u003e))\n\n \u003cspan class=\"pl-c\"\u003e# simulation parameters\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eblotter\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecapital_base\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1000000\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eblotter\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eset_commission\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003epercentage\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eper_share\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.005\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eminimum\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n \u003cspan class=\"pl-c\"\u003e# self.blotter.set_slippage(percentage=0, per_share=0.4)\u003c/span\u003e\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erebalance\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'pd.DataFrame'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ehistory\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'pd.DataFrame'\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efillna\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eblotter\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ebatch_order_target_percent\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eindex\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ealpha_weight\u003c/span\u003e)\n\n \u003cspan class=\"pl-c\"\u003e# closing asset position that are no longer in our universe.\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eremoves\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eblotter\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eportfolio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epositions\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ekeys\u003c/span\u003e() \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eset\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eindex\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eblotter\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ebatch_order_target_percent\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eremoves\u003c/span\u003e, [\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-en\"\u003elen\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eremoves\u003c/span\u003e))\n\n \u003cspan class=\"pl-c\"\u003e# record data for debugging / plotting\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erecord\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eaapl_weight\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eloc\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'AAPL'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'alpha_weight'\u003c/span\u003e],\n \u003cspan class=\"pl-s1\"\u003eaapl_price\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eblotter\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eget_price\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'AAPL'\u003c/span\u003e))\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eterminate\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003erecords\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'pd.DataFrame'\u003c/span\u003e):\n \u003cspan class=\"pl-c\"\u003e# plotting results\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eplot\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ebenchmark\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'SPY'\u003c/span\u003e)\n\n \u003cspan class=\"pl-c\"\u003e# plotting the relationship between AAPL price and weight\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eax1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erecords\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eaapl_price\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eplot\u003c/span\u003e()\n \u003cspan class=\"pl-s1\"\u003eax2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eax1\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etwinx\u003c/span\u003e()\n \u003cspan class=\"pl-s1\"\u003erecords\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eaapl_weight\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eplot\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eax\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eax2\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003estyle\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'g-'\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003eloader\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eArrowLoader\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'./prices/yahoo/yahoo.feather'\u003c/span\u003e)\n\u003cspan class=\"pl-c1\"\u003e%\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etime\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eresults\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etrading\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erun_backtest\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eloader\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eMyAlg\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'2014-01-01'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'2019-01-01'\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Heerozh/spectre/raw/media/backtest.png\"\u003e\u003cimg src=\"https://github.com/Heerozh/spectre/raw/media/backtest.png\" width=\"800\" height=\"630\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIt awful but you get the idea.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe return value of \u003ccode\u003erun_backtest\u003c/code\u003e is compatible with \u003ccode\u003epyfolio\u003c/code\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import pyfolio as pf\npf.create_full_tear_sheet(results.returns, positions=results.positions.value, transactions=results.transactions,\n live_start_date='2017-01-03')\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epyfolio\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epf\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003epf\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecreate_full_tear_sheet\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eresults\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ereturns\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003epositions\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eresults\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epositions\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003evalue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etransactions\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eresults\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etransactions\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003elive_start_date\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'2017-01-03'\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAPI\u003c/h1\u003e\u003ca id=\"user-content-api\" class=\"anchor\" aria-label=\"Permalink: API\" href=\"#api\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNote\u003c/h2\u003e\u003ca id=\"user-content-note\" class=\"anchor\" aria-label=\"Permalink: Note\" href=\"#note\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDifferences to zipline:\u003c/h3\u003e\u003ca id=\"user-content-differences-to-zipline\" class=\"anchor\" aria-label=\"Permalink: Differences to zipline:\" href=\"#differences-to-zipline\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIn order to GPU optimize, the \u003ccode\u003eCustomFactor.compute\u003c/code\u003e function calculates the results of all bars\nat once, so you need to be careful to prevent Look-Ahead Bias, because the inputs are not just\nhistorical data. Also using \u003ccode\u003eengine.test_lookahead_bias\u003c/code\u003e do some tests.\u003c/li\u003e\n\u003cli\u003espectre's normally using float32 data type for GPU performance.\u003c/li\u003e\n\u003cli\u003espectre FactorEngine arranges data by bars, so \u003ccode\u003eReturn(win=10)\u003c/code\u003e means 10 bars return, may\nactually be more than 10 days if some assets not open trading in period. You can change this\nbehavior by aligning data: filling missing bars with NaNs in your DataLoader, please refer to the\n\u003ccode\u003ealign_by_time\u003c/code\u003e parameter of \u003ccode\u003eCsvDirLoader\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDifferences to common chart:\u003c/h3\u003e\u003ca id=\"user-content-differences-to-common-chart\" class=\"anchor\" aria-label=\"Permalink: Differences to common chart:\" href=\"#differences-to-common-chart\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIf there is adjustments data, the prices is re-adjusted every day, so the factor you got, like MA,\nwill be different from the stock chart software which only adjusted according to last day.\nIf you want adjusted by last day, use like 'AdjustedColumnDataFactor(OHLCV.close)' as input data.\nThis will speeds up a lot because it only needs to be adjusted once, but brings Look-Ahead Bias.\u003c/li\u003e\n\u003cli\u003eFactors that uses the close data will be delayed by 1 bar.\u003c/li\u003e\n\u003cli\u003espectre's \u003ccode\u003eEMA\u003c/code\u003e uses the algorithm same as \u003ccode\u003ezipline\u003c/code\u003e and \u003ccode\u003eDataframe.ewm(span=...)\u003c/code\u003e, when \u003ccode\u003espan\u003c/code\u003e is\ngreater than 100, it will be slightly different from common EMA.\u003c/li\u003e\n\u003cli\u003espectre's \u003ccode\u003eRSI\u003c/code\u003e uses the algorithm same as \u003ccode\u003ezipline\u003c/code\u003e, for consistency in benchmarks.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactors\u003c/h2\u003e\u003ca id=\"user-content-factors\" class=\"anchor\" aria-label=\"Permalink: Factors\" href=\"#factors\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilt-in Technical Indicator Factors list\u003c/h2\u003e\u003ca id=\"user-content-built-in-technical-indicator-factors-list\" class=\"anchor\" aria-label=\"Permalink: Built-in Technical Indicator Factors list\" href=\"#built-in-technical-indicator-factors-list\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"Returns(inputs=[OHLCV.close])\nLogReturns(inputs=[OHLCV.close])\nSimpleMovingAverage = MA = SMA(win=5, inputs=[OHLCV.close])\nVWAP(inputs=[OHLCV.close, OHLCV.volume])\nExponentialWeightedMovingAverage = EMA(span=5, inputs=[OHLCV.close])\nAverageDollarVolume(win=5, inputs=[OHLCV.close, OHLCV.volume])\nAnnualizedVolatility(win=20, inputs=[Returns(win=2), 252])\nBollingerBands = BBANDS(win=20, inputs=[OHLCV.close, 2])\nMovingAverageConvergenceDivergenceSignal = MACD(12, 26, 9, inputs=[OHLCV.close])\nTrueRange = TRANGE(inputs=[OHLCV.high, OHLCV.low, OHLCV.close])\nRSI(win=14, inputs=[OHLCV.close])\nFastStochasticOscillator = STOCHF(win=14, inputs=[OHLCV.high, OHLCV.low, OHLCV.close])\n\nStandardDeviation = STDDEV(win=5, inputs=[OHLCV.close])\nRollingHigh = MAX(win=5, inputs=[OHLCV.close])\nRollingLow = MIN(win=5, inputs=[OHLCV.close])\"\u003e\u003cpre\u003e\u003cspan class=\"pl-en\"\u003eReturns\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])\n\u003cspan class=\"pl-en\"\u003eLogReturns\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])\n\u003cspan class=\"pl-v\"\u003eSimpleMovingAverage\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eMA\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eSMA\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])\n\u003cspan class=\"pl-en\"\u003eVWAP\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003evolume\u003c/span\u003e])\n\u003cspan class=\"pl-v\"\u003eExponentialWeightedMovingAverage\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eEMA\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003espan\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])\n\u003cspan class=\"pl-en\"\u003eAverageDollarVolume\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003evolume\u003c/span\u003e])\n\u003cspan class=\"pl-en\"\u003eAnnualizedVolatility\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-en\"\u003eReturns\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e), \u003cspan class=\"pl-c1\"\u003e252\u003c/span\u003e])\n\u003cspan class=\"pl-v\"\u003eBollingerBands\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eBBANDS\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e])\n\u003cspan class=\"pl-v\"\u003eMovingAverageConvergenceDivergenceSignal\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eMACD\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e12\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e26\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e9\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])\n\u003cspan class=\"pl-v\"\u003eTrueRange\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eTRANGE\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ehigh\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003elow\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])\n\u003cspan class=\"pl-en\"\u003eRSI\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e14\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])\n\u003cspan class=\"pl-v\"\u003eFastStochasticOscillator\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eSTOCHF\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e14\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ehigh\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003elow\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])\n\n\u003cspan class=\"pl-v\"\u003eStandardDeviation\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eSTDDEV\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])\n\u003cspan class=\"pl-v\"\u003eRollingHigh\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eMAX\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])\n\u003cspan class=\"pl-v\"\u003eRollingLow\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eMIN\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactors Common Methods\u003c/h2\u003e\u003ca id=\"user-content-factors-common-methods\" class=\"anchor\" aria-label=\"Permalink: Factors Common Methods\" href=\"#factors-common-methods\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Standardization\nnew_factor = factor.rank(mask=filter) \nnew_factor = factor.demean(mask=filter, groupby: 'dict or column_name'=None)\nnew_factor = factor.zscore(mask=filter)\nnew_factor = factor.to_weight(mask=filter, demean=True) # return a weight that sum(abs(weight)) = 1\n\n# Quick computation\nnew_factor = factor1 + factor1\nnew_factor = factor.abs()\nnew_factor = factor.sum()\n\n# To filter (Comparison operator):\nnew_filter = (factor1 \u0026lt; factor2) | (factor1 \u0026gt; 0)\nnew_filter[n_features] = factor.one_hot() # one-hot encoding\nnew_filter = factor.any(win=5)\nnew_filter = factor.all(win=5)\n# Rank filter\nnew_filter = factor.top(n)\nnew_filter = factor.bottom(n)\n# Specific assets\nnew_filter = StaticAssets({'AAPL', 'MSFT'})\n\n# Local filter\nnew_factor = factor.filter(some_filter) # fills elements of self with NaN where mask is False\n\n# Multiple returns selecting\nnew_factor = factor[0]\n\n# Others\nnew_factor = factor.shift(1)\nnew_factor = factor.quantile(bins=5) # factor value quantile groupby datetime\nnew_factor = factor.fill_na(0)\nnew_factor = factor.fill_na(ffill=True) # propagate last valid observation forward to next valid\n\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Standardization\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erank\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emask\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003efilter\u003c/span\u003e) \n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edemean\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emask\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003efilter\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003egroupby\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'dict or column_name'\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ezscore\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emask\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003efilter\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eto_weight\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emask\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003efilter\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edemean\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# return a weight that sum(abs(weight)) = 1\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Quick computation\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor1\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eabs\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esum\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# To filter (Comparison operator):\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enew_filter\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e (\u003cspan class=\"pl-s1\"\u003efactor1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor2\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e|\u003c/span\u003e (\u003cspan class=\"pl-s1\"\u003efactor1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003enew_filter\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003en_features\u003c/span\u003e] \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eone_hot\u003c/span\u003e() \u003cspan class=\"pl-c\"\u003e# one-hot encoding\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enew_filter\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eany\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003enew_filter\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eall\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# Rank filter\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enew_filter\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etop\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003en\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003enew_filter\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ebottom\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003en\u003c/span\u003e)\n\u003cspan class=\"pl-c\"\u003e# Specific assets\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enew_filter\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eStaticAssets\u003c/span\u003e({\u003cspan class=\"pl-s\"\u003e'AAPL'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'MSFT'\u003c/span\u003e})\n\n\u003cspan class=\"pl-c\"\u003e# Local filter\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efilter\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esome_filter\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# fills elements of self with NaN where mask is False\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Multiple returns selecting\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e[\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e]\n\n\u003cspan class=\"pl-c\"\u003e# Others\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eshift\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003equantile\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ebins\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# factor value quantile groupby datetime\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efill_na\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003enew_factor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactor\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efill_na\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003effill\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# propagate last valid observation forward to next valid\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDataloader\u003c/h2\u003e\u003ca id=\"user-content-dataloader-1\" class=\"anchor\" aria-label=\"Permalink: Dataloader\" href=\"#dataloader-1\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCsvDirLoader\u003c/h3\u003e\u003ca id=\"user-content-csvdirloader\" class=\"anchor\" aria-label=\"Permalink: CsvDirLoader\" href=\"#csvdirloader\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eloader = spectre.data.CsvDirLoader(prices_path: str, prices_by_year=False, earliest_date: pd.Timestamp = None, dividends_path=None, splits_path=None, file_pattern='*.csv', calender_asset: str = None, align_by_time=False, ohlcv=('open', 'high', 'low', 'close', 'volume'), adjustments=None, split_ratio_is_inverse=False, split_ratio_is_fraction=False, prices_index='date', dividends_index='exDate', splits_index='exDate', **read_csv)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRead CSV files in the directory, each file represents an asset.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eReading csv is very slow, so you also need to use \u003ca href=\"#arrowloader\"\u003eArrowLoader\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eprices_path:\u003c/strong\u003e Prices csv folder. When encountering duplicate datetime in \u003ccode\u003eprices_index\u003c/code\u003e,\nLoader will keep the last, drop others.\u003cbr\u003e\n\u003cstrong\u003eprices_index:\u003c/strong\u003e \u003ccode\u003eindex_col\u003c/code\u003efor csv in \u003ccode\u003eprices_path\u003c/code\u003e\u003cbr\u003e\n\u003cstrong\u003eprices_by_year:\u003c/strong\u003e If prices file name like 'spy_2017.csv', set this to True\u003cbr\u003e\n\u003cstrong\u003eohlcv:\u003c/strong\u003e Required, OHLCV column names. When you don't need to use \u003ccode\u003eadjustments\u003c/code\u003e and\n\u003ccode\u003efactors.OHLCV\u003c/code\u003e, you can set this to None.\u003cbr\u003e\n\u003cstrong\u003eadjustments:\u003c/strong\u003e Optional, list, \u003ccode\u003edividend amount\u003c/code\u003e and \u003ccode\u003esplits ratio\u003c/code\u003e column names.\u003cbr\u003e\n\u003cstrong\u003edividends_path:\u003c/strong\u003e Dividends csv folder, structured as one csv per asset.\nFor duplicate data, loader will first drop the exact same rows, and then for the same\n\u003ccode\u003edividends_index\u003c/code\u003e but different 'dividend amount(\u003ccode\u003eadjustments[0]\u003c/code\u003e)' rows, loader will sum them up.\nIf \u003ccode\u003edividends_path\u003c/code\u003e not set, the \u003ccode\u003eadjustments[0]\u003c/code\u003e column is considered to be included\nin the prices csv.\u003cbr\u003e\n\u003cstrong\u003edividends_index:\u003c/strong\u003e \u003ccode\u003eindex_col\u003c/code\u003efor csv in \u003ccode\u003edividends_path\u003c/code\u003e.\u003cbr\u003e\n\u003cstrong\u003esplits_path:\u003c/strong\u003e Splits csv folder, structured as one csv per asset.\nWhen encountering duplicate datetime in \u003ccode\u003esplits_index\u003c/code\u003e, Loader will use the last\nnon-NaN 'split ratio', drop others.\nIf \u003ccode\u003esplits_path\u003c/code\u003e not set, the \u003ccode\u003eadjustments[1]\u003c/code\u003e column is considered to be included\nin the prices csv.\u003cbr\u003e\n\u003cstrong\u003esplits_index:\u003c/strong\u003e \u003ccode\u003eindex_col\u003c/code\u003efor csv in \u003ccode\u003esplits_path\u003c/code\u003e.\u003cbr\u003e\n\u003cstrong\u003esplit_ratio_is_inverse:\u003c/strong\u003e If split ratio calculated by to/from, set to True.\nFor example, 2-for-1 split, to/form = 2, 1-for-15 Reverse Split, to/form = 0.6666...\u003cbr\u003e\n\u003cstrong\u003esplit_ratio_is_fraction:\u003c/strong\u003e If split ratio in csv is fraction string, like \u003ccode\u003e1/3\u003c/code\u003e, set to True.\u003cbr\u003e\n\u003cstrong\u003efile_pattern:\u003c/strong\u003e csv file name pattern, default is '*.csv'.\u003cbr\u003e\n\u003cstrong\u003eearliest_date:\u003c/strong\u003e Data before this date will not be read, save memory.\u003cbr\u003e\n\u003cstrong\u003ecalender_asset:\u003c/strong\u003e Asset name as trading calendar, like 'SPY', for clean up non-trading\ntime data.\u003cbr\u003e\n\u003cstrong\u003ealign_by_time:\u003c/strong\u003e If True and \u003ccode\u003ecalender_asset\u003c/code\u003e is not None, the index of datetime will be the same\nfor all assets, if some assets have no data at that time, NaNs will be filled. The benefit is\nthat the columns of data matrix in \u003ccode\u003eCustomFactor.compute\u003c/code\u003e will also be aligned.\u003cbr\u003e\n\u003cstrong\u003e**read_csv:\u003c/strong\u003e Parameters for all csv when calling \u003ccode\u003epd.read_csv\u003c/code\u003e.\n\u003ccode\u003eparse_dates\u003c/code\u003e or \u003ccode\u003edate_parser\u003c/code\u003e is required.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eExample for load \u003ca href=\"https://github.com/Heerozh/iex_fetcher\"\u003eIEX\u003c/a\u003e CSV files:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"usecols = {'date', 'uOpen', 'uHigh', 'uLow', 'uClose', 'uVolume', 'exDate', 'amount', 'ratio'}\ncsv_loader = spectre.data.CsvDirLoader(\n './iex/daily/', calender_asset='SPY', \n dividends_path='./iex/dividends/', \n splits_path='./iex/splits/',\n ohlcv=('uOpen', 'uHigh', 'uLow', 'uClose', 'uVolume'), adjustments=('amount', 'ratio'),\n prices_index='date', dividends_index='exDate', splits_index='exDate', \n parse_dates=True, usecols=lambda x: x in usecols,\n dtype={'uOpen': np.float32, 'uHigh': np.float32, 'uLow': np.float32, 'uClose': np.float32, \n 'uVolume': np.float64, 'amount': np.float64, 'ratio': np.float64})\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003eusecols\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e {\u003cspan class=\"pl-s\"\u003e'date'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uOpen'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uHigh'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uLow'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uClose'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uVolume'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'exDate'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'amount'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ratio'\u003c/span\u003e}\n\u003cspan class=\"pl-s1\"\u003ecsv_loader\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eCsvDirLoader\u003c/span\u003e(\n \u003cspan class=\"pl-s\"\u003e'./iex/daily/'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecalender_asset\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'SPY'\u003c/span\u003e, \n \u003cspan class=\"pl-s1\"\u003edividends_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'./iex/dividends/'\u003c/span\u003e, \n \u003cspan class=\"pl-s1\"\u003esplits_path\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'./iex/splits/'\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eohlcv\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'uOpen'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uHigh'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uLow'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uClose'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uVolume'\u003c/span\u003e), \u003cspan class=\"pl-s1\"\u003eadjustments\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'amount'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ratio'\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003eprices_index\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'date'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edividends_index\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'exDate'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esplits_index\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'exDate'\u003c/span\u003e, \n \u003cspan class=\"pl-s1\"\u003eparse_dates\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eusecols\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-k\"\u003elambda\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003ex\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eusecols\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003edtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e{\u003cspan class=\"pl-s\"\u003e'uOpen'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat32\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uHigh'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat32\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uLow'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat32\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'uClose'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat32\u003c/span\u003e, \n \u003cspan class=\"pl-s\"\u003e'uVolume'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat64\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'amount'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat64\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ratio'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat64\u003c/span\u003e})\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eArrowLoader\u003c/h3\u003e\u003ca id=\"user-content-arrowloader\" class=\"anchor\" aria-label=\"Permalink: ArrowLoader\" href=\"#arrowloader\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIngest data from other DataLoader into a feather file, speed up reading speed a lot.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e3GB data takes about 7 seconds on initial load.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eIngest\u003c/strong\u003e\n\u003ccode\u003espectre.data.ArrowLoader.ingest(source=CsvDirLoader(...), save_to='./filename.feather')\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRead\u003c/strong\u003e\n\u003ccode\u003eloader = spectre.data.ArrowLoader('./filename.feather')\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuandlLoader\u003c/h3\u003e\u003ca id=\"user-content-quandlloader\" class=\"anchor\" aria-label=\"Permalink: QuandlLoader\" href=\"#quandlloader\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eno longer updated, only contain prices before 2018\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDownload 'WIKI_PRICES.zip' (You need an account):\n\u003ccode\u003ehttps://www.quandl.com/api/v3/datatables/WIKI/PRICES.csv?qopts.export=true\u0026amp;api_key=[yourapi_key]\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from spectre.data import ArrowLoader, QuandlLoader\nArrowLoader.ingest(source=QuandlLoader('WIKI_PRICES.zip'),\n save_to='wiki_prices.feather')\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eArrowLoader\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eQuandlLoader\u003c/span\u003e\n\u003cspan class=\"pl-v\"\u003eArrowLoader\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eingest\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esource\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eQuandlLoader\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'WIKI_PRICES.zip'\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003esave_to\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'wiki_prices.feather'\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to write your own DataLoader\u003c/h3\u003e\u003ca id=\"user-content-how-to-write-your-own-dataloader\" class=\"anchor\" aria-label=\"Permalink: How to write your own DataLoader\" href=\"#how-to-write-your-own-dataloader\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eInherit from \u003ccode\u003eDataLoader\u003c/code\u003e, overriding the \u003ccode\u003e_load\u003c/code\u003e method, read data into a large \u003ccode\u003eDataFrame\u003c/code\u003e,\nindex is \u003ccode\u003eMultiIndex ['date', 'asset']\u003c/code\u003e, where date is \u003ccode\u003eDatetime\u003c/code\u003e type, \u003ccode\u003easset\u003c/code\u003e is \u003ccode\u003estring\u003c/code\u003e type,\nand then call \u003ccode\u003eself._format(df, split_ratio_is_inverse)\u003c/code\u003e to format the data.\nAlso call \u003ccode\u003etest_load\u003c/code\u003e in your test case to do basic format testing.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor example, suppose you have a csv file that contains data for all assets:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"class YourLoader(spectre.data.DataLoader):\n @property\n def last_modified(self) -\u0026gt; float:\n return os.path.getmtime(self._path)\n\n def __init__(self, file: str, calender_asset='SPY') -\u0026gt; None:\n super().__init__(file,\n ohlcv=('open', 'high', 'low', 'close', 'volume'),\n adjustments=('ex-dividend', 'split_ratio'))\n self._calender = calender_asset\n\n def _load(self) -\u0026gt; pd.DataFrame:\n df = pd.read_csv(self._path, parse_dates=['date'],\n usecols=['asset', 'date', 'open', 'high', 'low', 'close',\n 'volume', 'ex-dividend', 'split_ratio', ],\n dtype={\n 'open': np.float32, 'high': np.float32, 'low': np.float32,\n 'close': np.float32, 'volume': np.float64,\n 'ex-dividend': np.float64, 'split_ratio': np.float64\n })\n\n df.set_index(['date', 'asset'], inplace=True)\n df = self._format(df, split_ratio_is_inverse=True)\n if self._calender:\n df = self._align_to(df, self._calender)\n\n return df\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eYourLoader\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eDataLoader\u003c/span\u003e):\n \u003cspan class=\"pl-en\"\u003e@\u003cspan class=\"pl-s1\"\u003eproperty\u003c/span\u003e\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003elast_modified\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003efloat\u003c/span\u003e:\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eos\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epath\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003egetmtime\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e_path\u003c/span\u003e)\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e__init__\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003estr\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecalender_asset\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'SPY'\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e:\n \u003cspan class=\"pl-en\"\u003esuper\u003c/span\u003e().\u003cspan class=\"pl-c1\"\u003e__init__\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efile\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eohlcv\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'open'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'high'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'low'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'close'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'volume'\u003c/span\u003e),\n \u003cspan class=\"pl-s1\"\u003eadjustments\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'ex-dividend'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'split_ratio'\u003c/span\u003e))\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e_calender\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecalender_asset\u003c/span\u003e\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003e_load\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eDataFrame\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread_csv\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e_path\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eparse_dates\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'date'\u003c/span\u003e],\n \u003cspan class=\"pl-s1\"\u003eusecols\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'asset'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'date'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'open'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'high'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'low'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'close'\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'volume'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'ex-dividend'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'split_ratio'\u003c/span\u003e, ],\n \u003cspan class=\"pl-s1\"\u003edtype\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e{\n \u003cspan class=\"pl-s\"\u003e'open'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat32\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'high'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat32\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'low'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat32\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'close'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat32\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'volume'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat64\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003e'ex-dividend'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat64\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'split_ratio'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003enp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efloat64\u003c/span\u003e\n })\n\n \u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eset_index\u003c/span\u003e([\u003cspan class=\"pl-s\"\u003e'date'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'asset'\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003einplace\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e_format\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esplit_ratio_is_inverse\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e)\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e_calender\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e_align_to\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e_calender\u003c/span\u003e)\n\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine\u003c/h2\u003e\u003ca id=\"user-content-factorengine\" class=\"anchor\" aria-label=\"Permalink: FactorEngine\" href=\"#factorengine\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA fast factor calculation pipeline.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.\u003cstrong\u003einit\u003c/strong\u003e\u003c/h3\u003e\u003ca id=\"user-content-factorengineinit\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.init\" href=\"#factorengineinit\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eengine = FactorEngine(loader: DataLoader)\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.add\u003c/h3\u003e\u003ca id=\"user-content-factorengineadd\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.add\" href=\"#factorengineadd\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eengine.add(factor, column_name)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAdd a factor to engine.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.set_filter\u003c/h3\u003e\u003ca id=\"user-content-factorengineset_filter\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.set_filter\" href=\"#factorengineset_filter\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eengine.set_filter(factor: FilterFactor or None)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSet the Global Filter, engine deletes rows which Global Filter returns as False at the last step,\naffect all factors.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.align_by_time\u003c/h3\u003e\u003ca id=\"user-content-factorenginealign_by_time\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.align_by_time\" href=\"#factorenginealign_by_time\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eengine.align_by_time = bool\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSame as \u003ccode\u003eCsvDirLoader(align_by_time=True)\u003c/code\u003e, but it's dynamic. Notes: Very slow on large amounts of\ndata, and if the data source is already aligned, this method cannot make it return to unaligned.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.clear\u003c/h3\u003e\u003ca id=\"user-content-factorengineclear\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.clear\" href=\"#factorengineclear\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eengine.clear()\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRemove global filter, and all factors.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.to_cuda\u003c/h3\u003e\u003ca id=\"user-content-factorengineto_cuda\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.to_cuda\" href=\"#factorengineto_cuda\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eengine.to_cuda(enable_stream=False)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSwitch to GPU mode.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSet enable_stream to True allows pipeline branches to calculation simultaneously.\nHowever, this will lead to more VRAM usage and may also affect performance.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.to_cpu\u003c/h3\u003e\u003ca id=\"user-content-factorengineto_cpu\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.to_cpu\" href=\"#factorengineto_cpu\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eengine.to_cpu()\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSwitch to CPU mode.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.run\u003c/h3\u003e\u003ca id=\"user-content-factorenginerun\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.run\" href=\"#factorenginerun\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003edf = engine.run(start_time, end_time, delay_factor=True)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRun the engine to calculate the factor data, return a DataFrame. The column is each added factor.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e*Auto Delay\u003c/h4\u003e\u003ca id=\"user-content-auto-delay\" class=\"anchor\" aria-label=\"Permalink: *Auto Delay\" href=\"#auto-delay\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBy default, \u003ccode\u003edelay_factor\u003c/code\u003e is True, it means enable auto-delay. If 'high, low, close, volume' data\nis used by a terminal factor (including its upstream), that factor will be delayed by \u003ccode\u003eshift(1)\u003c/code\u003e\nin the last step, because in theory you can't trade on this factor before it generated. Others\nwill not be delayed, in order to provide the latest data as much as possible.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSet to \u003ccode\u003eFalse\u003c/code\u003e to force engine not delay any factors.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.plot_chart\u003c/h3\u003e\u003ca id=\"user-content-factorengineplot_chart\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.plot_chart\" href=\"#factorengineplot_chart\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eengine.plot_chart(start_time, end_time, trace_types=None, styles=None, delay_factor=True)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePlotting common stock price chart for researching.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003etrace_types\u003c/code\u003e: \u003ccode\u003edict(factor_name=plotly_trace_type)\u003c/code\u003e, trace type can be 'Bar', or 'Scatter',\ndefault is 'Scatter'.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003estyles\u003c/code\u003e: \u003ccode\u003edict(factor_name=plotly_trace_styles)\u003c/code\u003e, add the trace styles, please refer\nto plotly documentation: \u003ca href=\"https://plot.ly/python/reference/#scatter\" rel=\"nofollow\"\u003eScatter traces\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"rsi = factors.RSI()\nbuy_signal = (rsi.shift(1) \u0026lt; 30) \u0026amp; (rsi \u0026gt; 30)\n\nengine = factors.FactorEngine(loader)\nengine.timezone = 'America/New_York'\nengine.set_filter(factors.StaticAssets({'NVDA', 'MSFT'}))\nengine.add(factors.MA(20), 'MA20')\nengine.add(rsi, 'RSI')\nengine.add(factors.OHLCV.close.filter(buy_signal), 'Buy')\nengine.to_cuda()\n_ = engine.plot_chart('2017', '2018', styles={\n 'MA20': {\n 'line': {'dash': 'dash'}\n },\n 'RSI': {\n 'yaxis': 'y3', # y1: price axis, y2: volume axis, yN: add new y-axis\n 'line': {'width': 1}\n },\n 'Buy': { \n 'mode': 'markers', \n 'marker': { 'symbol': 'triangle-up', 'size': 10, 'color': 'rgba(0, 0, 255, 0.5)' }\n }\n})\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ersi\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eRSI\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003ebuy_signal\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e (\u003cspan class=\"pl-s1\"\u003ersi\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eshift\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e\u0026amp;\u003c/span\u003e (\u003cspan class=\"pl-s1\"\u003ersi\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e30\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eFactorEngine\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eloader\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etimezone\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'America/New_York'\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eset_filter\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eStaticAssets\u003c/span\u003e({\u003cspan class=\"pl-s\"\u003e'NVDA'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'MSFT'\u003c/span\u003e}))\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eadd\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eMA\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e20\u003c/span\u003e), \u003cspan class=\"pl-s\"\u003e'MA20'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eadd\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ersi\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'RSI'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eadd\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efilter\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ebuy_signal\u003c/span\u003e), \u003cspan class=\"pl-s\"\u003e'Buy'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eto_cuda\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003e_\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eengine\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eplot_chart\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'2017'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'2018'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003estyles\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e{\n \u003cspan class=\"pl-s\"\u003e'MA20'\u003c/span\u003e: {\n \u003cspan class=\"pl-s\"\u003e'line'\u003c/span\u003e: {\u003cspan class=\"pl-s\"\u003e'dash'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'dash'\u003c/span\u003e}\n },\n \u003cspan class=\"pl-s\"\u003e'RSI'\u003c/span\u003e: {\n \u003cspan class=\"pl-s\"\u003e'yaxis'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'y3'\u003c/span\u003e, \u003cspan class=\"pl-c\"\u003e# y1: price axis, y2: volume axis, yN: add new y-axis\u003c/span\u003e\n \u003cspan class=\"pl-s\"\u003e'line'\u003c/span\u003e: {\u003cspan class=\"pl-s\"\u003e'width'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e}\n },\n \u003cspan class=\"pl-s\"\u003e'Buy'\u003c/span\u003e: { \n \u003cspan class=\"pl-s\"\u003e'mode'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'markers'\u003c/span\u003e, \n \u003cspan class=\"pl-s\"\u003e'marker'\u003c/span\u003e: { \u003cspan class=\"pl-s\"\u003e'symbol'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'triangle-up'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'size'\u003c/span\u003e: \u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'color'\u003c/span\u003e: \u003cspan class=\"pl-s\"\u003e'rgba(0, 0, 255, 0.5)'\u003c/span\u003e }\n }\n})\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/Heerozh/spectre/raw/media/chart.png\"\u003e\u003cimg src=\"https://github.com/Heerozh/spectre/raw/media/chart.png\" width=\"730\" height=\"1000\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.full_run\u003c/h3\u003e\u003ca id=\"user-content-factorenginefull_run\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.full_run\" href=\"#factorenginefull_run\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003efactor_data, mean_returns = engine.full_run( start_time, end_time, trade_at='close', periods=(1, 4, 9), quantiles=5, filter_zscore=20, demean=True, preview=True)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNot only run the engine, but also run factor analysis.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.get_price_matrix\u003c/h3\u003e\u003ca id=\"user-content-factorengineget_price_matrix\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.get_price_matrix\" href=\"#factorengineget_price_matrix\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003edf_prices = engine.get_price_matrix(start_time, end_time, prices: ColumnDataFactor = OHLCV.close)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eGet the adjusted historical prices matrix which columns is all assets.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf global filter is setted, all unfiltered assets from \u003ccode\u003estart_time\u003c/code\u003e to \u003ccode\u003eend_time\u003c/code\u003e will be included.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFactorEngine.test_lookahead_bias\u003c/h3\u003e\u003ca id=\"user-content-factorenginetest_lookahead_bias\" class=\"anchor\" aria-label=\"Permalink: FactorEngine.test_lookahead_bias\" href=\"#factorenginetest_lookahead_bias\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eengine.test_lookahead_bias(start_time, end_time)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRun the engine to test if there is a lookahead bias.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFill random values to second half of the ohlcv data, and then check if there are differences between\nthe two runs in the first half.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eColumnDataFactor\u003c/h2\u003e\u003ca id=\"user-content-columndatafactor\" class=\"anchor\" aria-label=\"Permalink: ColumnDataFactor\" href=\"#columndatafactor\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can use \u003ccode\u003eColumnDataFactor\u003c/code\u003e to represents data from any column in the \u003ccode\u003eDataLoader\u003c/code\u003e, for example:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003espectre.factors.ColumnDataFactor(inputs=['col_name'])\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003efactors.OHLCV.close\u003c/code\u003e is just a sugar way to write\n\u003ccode\u003espectre.factors.ColumnDataFactor (inputs = [data_loader.ohlcv[3]])\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to write your own factor\u003c/h2\u003e\u003ca id=\"user-content-how-to-write-your-own-factor\" class=\"anchor\" aria-label=\"Permalink: How to write your own factor\" href=\"#how-to-write-your-own-factor\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eInherit from \u003ccode\u003efactors.CustomFactor\u003c/code\u003e, write \u003ccode\u003ecompute\u003c/code\u003e function.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAll \u003ccode\u003einputs\u003c/code\u003e will pass to compute function.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ewin = 1\u003c/h3\u003e\u003ca id=\"user-content-win--1\" class=\"anchor\" aria-label=\"Permalink: win = 1\" href=\"#win--1\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWhen \u003ccode\u003ewin = 1\u003c/code\u003e, the \u003ccode\u003einputs\u003c/code\u003e data is tensor type, the first dimension of data is the asset, the\nsecond dimension is each bar price data. Note that if the data is \u003ccode\u003ealign_by_time=False\u003c/code\u003e, the number\nof bars for each asset is different and not aligned (for example, the time for each price in bar_t3\ncolumn may be inconsistent).\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\" +-----------------------------------+\n | bar_t1 bar_t3 |\n | | | |\n | v v |\n | asset 1--\u0026gt; [[1.1, 1.2, 1.3, ...], |\n | asset 2--\u0026gt; [ 5, 6, 7, ...]] |\n +-----------------------------------+\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e +-----------------------------------+\n | bar_t1 bar_t3 |\n | | | |\n | v v |\n | asset 1--\u0026gt; [[1.1, 1.2, 1.3, ...], |\n | asset 2--\u0026gt; [ 5, 6, 7, ...]] |\n +-----------------------------------+\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExample of LogReturns:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from spectre import factors \nimport torch\nclass LogReturns(factors.CustomFactor):\n inputs = [factors.Returns(2, inputs=[factors.OHLCV.close])]\n win = 1\n\n def compute(self, change: torch.Tensor) -\u0026gt; torch.Tensor:\n return (change + 1).log()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e \n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eLogReturns\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eCustomFactor\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eReturns\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e])]\n \u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ecompute\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003echange\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eTensor\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eTensor\u003c/span\u003e:\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e (\u003cspan class=\"pl-s1\"\u003echange\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003elog\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ewin \u0026gt; 1\u003c/h3\u003e\u003ca id=\"user-content-win--1-1\" class=\"anchor\" aria-label=\"Permalink: win \u0026gt; 1\" href=\"#win--1-1\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf rolling window is required(\u003ccode\u003ewin \u0026gt; 1\u003c/code\u003e), all \u003ccode\u003einputs\u003c/code\u003e data will be wrapped into\n\u003ccode\u003espectre.parallel.Rolling\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis is just an unfolded \u003ccode\u003etensor\u003c/code\u003e data, but because the data is very large after unfolded, for\nbetter performance and saving VRAM, the rolling class automatically splits the data into multiple\nsmall chunks. You need to use the \u003ccode\u003eagg\u003c/code\u003e method to operating \u003ccode\u003etensor\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from spectre import factors, parallel\nclass OvernightReturn(factors.CustomFactor):\n inputs = [factors.OHLCV.open, factors.OHLCV.close]\n win = 2\n\n def compute(self, opens: parallel.Rolling, closes: parallel.Rolling) -\u0026gt; torch.Tensor:\n ret = opens.last() / closes.first() - 1\n return ret\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eparallel\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eOvernightReturn\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eCustomFactor\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e [\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eopen\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eOHLCV\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eclose\u003c/span\u003e]\n \u003cspan class=\"pl-s1\"\u003ewin\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ecompute\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eopens\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eparallel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eRolling\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecloses\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eparallel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eRolling\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eTensor\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eret\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eopens\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003elast\u003c/span\u003e() \u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecloses\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efirst\u003c/span\u003e() \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eret\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ccode\u003ecloses.first()\u003c/code\u003e above is just a helper method for \u003ccode\u003ecloses.agg(lambda x: x[:, :, 0])\u003c/code\u003e,\nwhere \u003ccode\u003ex[:, :, 0]\u003c/code\u003e return the first element of rolling window. The first dimension of \u003ccode\u003ex\u003c/code\u003e is the\nasset, the second dimension is each bar, and the third dimension is the bar price and historical\nprice with \u003ccode\u003ewin\u003c/code\u003e length, and \u003ccode\u003eRolling.agg\u003c/code\u003e runs on all the chunks and combines them.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\" +------------------win=3-------------------+\n | history_t-2 curr_bar_value |\n | | | |\n | v v |\n | asset 1--\u0026gt;[[[nan, nan, 1.1], \u0026lt;--bar_t1 |\n | [nan, 1.1, 1.2], \u0026lt;--bar_t2 |\n | [1.1, 1.2, 1.3]], \u0026lt;--bar_t3 |\n | |\n | asset 2--\u0026gt; [[nan, nan, 5], \u0026lt;--bar_t1 |\n | [nan, 5, 6], \u0026lt;--bar_t2 |\n | [ 5, 6, 7]]] \u0026lt;--bar_t3 |\n +------------------------------------------+\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e +------------------win=3-------------------+\n | history_t-2 curr_bar_value |\n | | | |\n | v v |\n | asset 1--\u0026gt;[[[nan, nan, 1.1], \u0026lt;--bar_t1 |\n | [nan, 1.1, 1.2], \u0026lt;--bar_t2 |\n | [1.1, 1.2, 1.3]], \u0026lt;--bar_t3 |\n | |\n | asset 2--\u0026gt; [[nan, nan, 5], \u0026lt;--bar_t1 |\n | [nan, 5, 6], \u0026lt;--bar_t2 |\n | [ 5, 6, 7]]] \u0026lt;--bar_t3 |\n +------------------------------------------+\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eRolling.agg\u003c/code\u003e can carry multiple \u003ccode\u003eRolling\u003c/code\u003e objects, such as\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"weighted_mean = lambda _close, _volume: (_close * _volume).sum(dim=2) / _volume.sum(dim=2)\nclose.agg(weighted_mean, volume)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003eweighted_mean\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003elambda\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003e_close\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003e_volume\u003c/span\u003e: (\u003cspan class=\"pl-s1\"\u003e_close\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e*\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003e_volume\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003esum\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edim\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003e_volume\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esum\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edim\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eclose\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eagg\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eweighted_mean\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003evolume\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsing Pandas Series\u003c/h3\u003e\u003ca id=\"user-content-using-pandas-series\" class=\"anchor\" aria-label=\"Permalink: Using Pandas Series\" href=\"#using-pandas-series\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCustomFactor's inputs data is a matrix without DataFrame's Index information.\nIf you need index, or not familiar with PyTorch, here is a another way:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from spectre import factors\nclass YourFactor(factors.CustomFactor):\n\n def compute(self, data: torch.Tensor) -\u0026gt; torch.Tensor:\n # convert to pd.Series data\n pd_series = self._revert_to_series(data)\n # ...\n # convert back to grouped tensor\n return self._regroup(pd_series)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espectre\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eYourFactor\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efactors\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eCustomFactor\u003c/span\u003e):\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ecompute\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eTensor\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eTensor\u003c/span\u003e:\n \u003cspan class=\"pl-c\"\u003e# convert to pd.Series data\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003epd_series\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e_revert_to_series\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e)\n \u003cspan class=\"pl-c\"\u003e# ...\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e# convert back to grouped tensor\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003e_regroup\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003epd_series\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis method is completely non-parallel and inefficient, but easy to write.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBack-testing\u003c/h2\u003e\u003ca id=\"user-content-back-testing-1\" class=\"anchor\" aria-label=\"Permalink: Back-testing\" href=\"#back-testing-1\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#back-testing\"\u003eQuick Start\u003c/a\u003e contains easy-to-understand examples, please read first.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ccode\u003espectre.trading.CustomAlgorithm\u003c/code\u003e currently does not supports live trading,\nwill implement it in the future.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.initialize\u003c/h3\u003e\u003ca id=\"user-content-customalgorithminitialize\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.initialize\" href=\"#customalgorithminitialize\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003ealg.initialize(self)\u003c/code\u003e \u003cstrong\u003eCallback\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCalled when back-testing starts, at least you need use \u003ccode\u003eget_factor_engine\u003c/code\u003e to add factors\nand call \u003ccode\u003eschedule_rebalance\u003c/code\u003e here.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.terminate\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmterminate\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.terminate\" href=\"#customalgorithmterminate\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003ealg.terminate(self, records: pd.DataFrame)\u003c/code\u003e \u003cstrong\u003eCallback\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCalled when back-testing ends.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003erebalance callback\u003c/h3\u003e\u003ca id=\"user-content-rebalance-callback\" class=\"anchor\" aria-label=\"Permalink: rebalance callback\" href=\"#rebalance-callback\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003erebalance(self, data: pd.DataFrame, history: pd.DataFrame)\u003c/code\u003e \u003cstrong\u003eCallback\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe function name does not have to be 'rebalance', it can be specified in \u003ccode\u003eschedule_rebalance\u003c/code\u003e.\n\u003ccode\u003edata\u003c/code\u003e is the factors data of last bar returned by \u003ccode\u003eFactorEngine\u003c/code\u003e;\n\u003ccode\u003ehistory\u003c/code\u003e same as \u003ccode\u003edata\u003c/code\u003e, but contains previous data, please refer to \u003ccode\u003eset_history_window\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePut calculations into the \u003ccode\u003eFactorEngine\u003c/code\u003e as much as possible can improve backtest performance.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.get_factor_engine\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmget_factor_engine\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.get_factor_engine\" href=\"#customalgorithmget_factor_engine\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.get_factor_engine(name: str = None)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003einitialize, rebalance, terminate\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eGet the factor engine of this trading algorithm. But note that you can add factors or filter only\nduring \u003ccode\u003einitialize\u003c/code\u003e, otherwise it will cause unexpected effects.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe algorithm has a default engine, \u003ccode\u003ename\u003c/code\u003e can be None.\nBut if you created multiple engines using \u003ccode\u003ecreate_factor_engine\u003c/code\u003e, you need to specify which one.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.create_factor_engine\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmcreate_factor_engine\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.create_factor_engine\" href=\"#customalgorithmcreate_factor_engine\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.create_factor_engine(name: str, loader: DataLoader = None)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003einitialize\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCreate another engine, generally used when you need multiple data sources.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.set_history_window\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmset_history_window\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.set_history_window\" href=\"#customalgorithmset_history_window\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.set_history_window(offset: pd.DateOffset=None)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003einitialize\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSet the length of historical data passed to each \u003ccode\u003erebalance\u003c/code\u003e call. \u003cstrong\u003eSLOW\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDefault: If None, pass all available historical data, so there will be no historical data on the\nfirst day, one historical row on the next day, and so on.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.schedule_rebalance\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmschedule_rebalance\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.schedule_rebalance\" href=\"#customalgorithmschedule_rebalance\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.schedule_rebalance(event: Event)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003einitialize\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSchedule \u003ccode\u003erebalance\u003c/code\u003e to be called when an event occurs.\nEvents are: \u003ccode\u003eMarketOpen\u003c/code\u003e, \u003ccode\u003eMarketClose\u003c/code\u003e, \u003ccode\u003eEveryBarData\u003c/code\u003e,\nFor example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"alg.schedule_rebalance(trading.event.MarketClose(self.any_function))\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003ealg\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eschedule_rebalance\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrading\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eevent\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eMarketClose\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eany_function\u003c/span\u003e))\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ccode\u003eMarket*\u003c/code\u003e events has \u003ccode\u003eoffset_ns\u003c/code\u003e parameter \u003ccode\u003eMarketClose(self.any_function, offset_ns=-1000)\u003c/code\u003e,\na negative value of \u003ccode\u003eoffset_ns\u003c/code\u003e means 'before', in backtest mode, the magnitude of the value has no\neffect.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.schedule\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmschedule\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.schedule\" href=\"#customalgorithmschedule\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.schedule(event: Event)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003einitialize\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSchedule an event, callback is \u003ccode\u003ecallback(source: \"Any class who fired this event\")\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.empty_cache_after_run\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmempty_cache_after_run\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.empty_cache_after_run\" href=\"#customalgorithmempty_cache_after_run\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.empty_cache_after_run = True\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003einitialize\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eEmpty engine's cache after factor calculation.\nIf you need more VRMA in rebalance context, or wanna play 3D game when backtesting, set it to\nTrue will help.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.stop_event_manager\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmstop_event_manager\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.stop_event_manager\" href=\"#customalgorithmstop_event_manager\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003ealg.stop_event_manager()\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003eall\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eStop backtesting or live trading.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.fire_event\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmfire_event\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.fire_event\" href=\"#customalgorithmfire_event\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003ealg.fire_event(event_type: Type[Event])\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003eall\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTrigger a type of event (any subclasses that inherit from \u003ccode\u003eEvent\u003c/code\u003e）,\nfor example: \u003ccode\u003ealg.fire_event(MarketClose)\u003c/code\u003e, (do not do this, do not fire built-in events)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.results\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmresults\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.results\" href=\"#customalgorithmresults\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.results\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003eterminate\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eGet back-test results, same as the return value of \u003ca href=\"#spectretradingrun_backtest\"\u003etrading.run_backtest\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.plot\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmplot\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.plot\" href=\"#customalgorithmplot\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.plot(annual_risk_free=0.04, benchmark: Union[pd.Series, str] = None)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003eterminate\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePlot a simple portfolio cumulative return chart.\u003cbr\u003e\n\u003ccode\u003ebenchmark\u003c/code\u003e: \u003ccode\u003epd.Series\u003c/code\u003e of benchmark daily return, or an asset name.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.current\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmcurrent\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.current\" href=\"#customalgorithmcurrent\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.current\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003erebalance\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCurrent datetime, Read-Only.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.get_price_matrix\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmget_price_matrix\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.get_price_matrix\" href=\"#customalgorithmget_price_matrix\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.get_price_matrix(length: pd.DateOffset, name: str = None, prices=OHLCV.close)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003erebalance\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHelp method for calling \u003ccode\u003eengine.get_price_matrix\u003c/code\u003e, \u003ccode\u003ename\u003c/code\u003e specifies which engine.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eReturns the historical asset prices, adjusted and filtered by the current time.\n\u003cstrong\u003eSlow\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustomAlgorithm.record\u003c/h3\u003e\u003ca id=\"user-content-customalgorithmrecord\" class=\"anchor\" aria-label=\"Permalink: CustomAlgorithm.record\" href=\"#customalgorithmrecord\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.record(**kwargs)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003erebalance\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRecord the data and pass all when calling \u003ccode\u003eterminate\u003c/code\u003e, use \u003ccode\u003ecolumn = value\u003c/code\u003e format.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.set_commission\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterset_commission\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.set_commission\" href=\"#simulationblotterset_commission\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.set_commission(percentage=0, per_share=0.005, minimum=1)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003einitialize\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003epercentage: percentage part, calculated by \u003ccode\u003epercentage * price * shares\u003c/code\u003e\u003cbr\u003e\nper_share: calculated by \u003ccode\u003eper_share * shares\u003c/code\u003e\u003cbr\u003e\nminimum: minimum commission if above sum does not exceed\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ecommission = max(percentage_part + per_share_part, minimum)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.set_slippage\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterset_slippage\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.set_slippage\" href=\"#simulationblotterset_slippage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.set_slippage(percentage=0, per_share=0.01)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003einitialize, rebalance\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMarket impact add to the price.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.set_short_fee\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterset_short_fee\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.set_short_fee\" href=\"#simulationblotterset_short_fee\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.set_short_fee(percentage=0)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003einitialize\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSet the transaction fees which only charged for sell orders.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.daily_curb\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterdaily_curb\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.daily_curb\" href=\"#simulationblotterdaily_curb\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.daily_curb = float\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003einitialize, rebalance\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eLimit on trading a specific asset if today to previous day return \u0026gt;= ±value. \u003cstrong\u003eSLOW\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.order_target\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterorder_target\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.order_target\" href=\"#simulationblotterorder_target\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.order_target(asset: str, target: number)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003erebalance\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePlace an order on an asset to target number of shares in position, negative number means short.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf asset cannot be traded or limited by \u003ccode\u003edaily_curb\u003c/code\u003e, it will return False.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.batch_order_target\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterbatch_order_target\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.batch_order_target\" href=\"#simulationblotterbatch_order_target\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.batch_order_target(asset: Iterable[str], target: Iterable[float])\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003erebalance\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSame as \u003ccode\u003eSimulationBlotter.order_target\u003c/code\u003e, but for multiple assets.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eReturn value is a list of skipped assets, which indicate that they cannot be traded or limited by\n\u003ccode\u003edaily_curb\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.order_target_percent\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterorder_target_percent\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.order_target_percent\" href=\"#simulationblotterorder_target_percent\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.order_target_percent(asset: str, pct: float)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003erebalance\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003ePlace an order on an asset to target percentage of portfolio net value, negative number means short.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf asset cannot be traded or limited by \u003ccode\u003edaily_curb\u003c/code\u003e, it will return False.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.batch_order_target_percent\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterbatch_order_target_percent\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.batch_order_target_percent\" href=\"#simulationblotterbatch_order_target_percent\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.batch_order_target_percent(asset: Iterable[str], pct: Iterable[float])\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003erebalance\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSame as \u003ccode\u003eSimulationBlotter.order_target_percent\u003c/code\u003e, but for multiple assets and better performance.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eReturn value is a list of skipped assets, which indicate that they cannot be traded or limited by\n\u003ccode\u003edaily_curb\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.order\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterorder\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.order\" href=\"#simulationblotterorder\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.order(asset: str, amount: int)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003erebalance\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOrder a certain amount of an asset, negative number means short.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf asset cannot be traded or limited by \u003ccode\u003edaily_curb\u003c/code\u003e, it will return False.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.get_price\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterget_price\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.get_price\" href=\"#simulationblotterget_price\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003efloat = self.blotter.get_price(asset: Union[str, Iterable])\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003erebalance\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eGet current price of assert.\n\u003cem\u003eNotice: Batch calls are slow, You can add prices as factor to get the price,\nlike: \u003ccode\u003eengine.add(OHLCV.close, 'prices')\u003c/code\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.portfolio.set_stop_model\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterportfolioset_stop_model\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.portfolio.set_stop_model\" href=\"#simulationblotterportfolioset_stop_model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.portfolio.set_stop_model(model: StopModel)\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003einitialize\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSet stop tracking model for positions, models are:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003etrading.StopModel(ratio, callback)\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003etrading.TrailingStopModel(ratio, callback)\u003c/code\u003e\u003cbr\u003e\n\u003ccode\u003etrading.PnLDecayTrailingStopModel\u003c/code\u003e and \u003ccode\u003etrading.TimeDecayTrailingStopModel\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eStop loss example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"class Backtester(trading.CustomAlgorithm):\n def initialize(self):\n ...\n self.blotter.portfolio.set_stop_model(trading.TrailingStopModel(-0.1, self.stop))\n\n def stop(self, asset, amount):\n self.blotter.order(asset, amount)\n self.record(...)\n\n def rebalance(self, data, history):\n self.blotter.portfolio.check_stop_trigger()\n ...\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBacktester\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrading\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eCustomAlgorithm\u003c/span\u003e):\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003einitialize\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n ...\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eblotter\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eportfolio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eset_stop_model\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrading\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eTrailingStopModel\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0.1\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003estop\u003c/span\u003e))\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003estop\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003easset\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eamount\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eblotter\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eorder\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003easset\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eamount\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erecord\u003c/span\u003e(...)\n\n \u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erebalance\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ehistory\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eblotter\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eportfolio\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003echeck_stop_trigger\u003c/span\u003e()\n ...\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePnLDecayTrailingStopModel\u003c/h4\u003e\u003ca id=\"user-content-pnldecaytrailingstopmodel\" class=\"anchor\" aria-label=\"Permalink: PnLDecayTrailingStopModel\" href=\"#pnldecaytrailingstopmodel\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003etrading.PnLDecayTrailingStopModel(ratio, pnl_target, callback, decay_rate=0.05, max_decay=0)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis is a model that can stop gain and stop loss at the same time.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eExponential decay to the stop ratio: \u003ccode\u003eratio * decay_rate ^ (PnL% / PnL_target%)\u003c/code\u003e,\nSo \u003ccode\u003ePnLDecayTrailingStopModel(-0.1, 0.1, callback)\u003c/code\u003e means initial stop loss is -10%, and the\n\u003ccode\u003eratio\u003c/code\u003e will decrease when profit% approaches the target +10%. If recorded high profit% exceeds 10%,\nany drawdown will trigger a stop loss.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTimeDecayTrailingStopModel\u003c/h4\u003e\u003ca id=\"user-content-timedecaytrailingstopmodel\" class=\"anchor\" aria-label=\"Permalink: TimeDecayTrailingStopModel\" href=\"#timedecaytrailingstopmodel\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003etrading.TimeDecayTrailingStopModel(ratio, period_target: pd.Timedelta, callback, decay_rate=0.05, max_decay=0)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSame as \u003ccode\u003ePnLDecayTrailingStopModel\u003c/code\u003e, but target is time period.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.get_returns\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterget_returns\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.get_returns\" href=\"#simulationblotterget_returns\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.get_returns()\u003c/code\u003e\n\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003erebalance, terminate\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eGet the portfolio returns, use \u003ccode\u003e(self.blotter.get_returns() + 1).prod()\u003c/code\u003e to get current cumulative\nreturn.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimulationBlotter.portfolio Read Only Properties\u003c/h3\u003e\u003ca id=\"user-content-simulationblotterportfolio-read-only-properties\" class=\"anchor\" aria-label=\"Permalink: SimulationBlotter.portfolio Read Only Properties\" href=\"#simulationblotterportfolio-read-only-properties\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003econtext:\u003c/strong\u003e \u003cem\u003erebalance, terminate\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.portfolio.positions\u003c/code\u003e Current positions, \u003ccode\u003eDict[asset, Position]\u003c/code\u003e type.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"class Position:\n shares = None\n average_price = None\n last_price = None\n unrealized = None\n realized = None\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003ePosition\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003eshares\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eaverage_price\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003elast_price\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eunrealized\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003erealized\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eNone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.portfolio.value\u003c/code\u003e Current portfolio value\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.portfolio.cash\u003c/code\u003e Current portfolio cash\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eself.blotter.portfolio.leverage\u003c/code\u003e Current portfolio leverage\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003espectre.trading.run_backtest\u003c/h3\u003e\u003ca id=\"user-content-spectretradingrun_backtest\" class=\"anchor\" aria-label=\"Permalink: spectre.trading.run_backtest\" href=\"#spectretradingrun_backtest\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eresults = trading.run_backtest(loader: DataLoader, alg_type: Type[CustomAlgorithm], start, end)\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eRun backtest, return value is namedtuple:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eresults.returns:\u003c/strong\u003e daily return\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eresults.positions:\u003c/strong\u003e daily positions\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eresults.transactions:\u003c/strong\u003e full transactions with all orders\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCopyright \u0026amp; Thanks\u003c/h1\u003e\u003ca id=\"user-content-copyright--thanks\" class=\"anchor\" aria-label=\"Permalink: Copyright \u0026amp; Thanks\" href=\"#copyright--thanks\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCopyright (C) 2019-2020, by Zhang Jianhao (\u003ca href=\"mailto:heeroz@gmail.com\"\u003eheeroz@gmail.com\u003c/a\u003e), All rights reserved.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThanks to \u003ca href=\"https://www.jetbrains.com/?from=spectre\" rel=\"nofollow\"\u003eJetBrains\u003c/a\u003e's support.\u003c/p\u003e\n\u003chr\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eA spectre is haunting Market — the spectre of capitalism.\u003c/em\u003e\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"||spectre","anchor":"spectre","htmlText":"||spectre"},{"level":1,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":1,"text":"Benchmarks","anchor":"benchmarks","htmlText":"Benchmarks"},{"level":1,"text":"Quick Start","anchor":"quick-start","htmlText":"Quick Start"},{"level":2,"text":"DataLoader","anchor":"dataloader","htmlText":"DataLoader"},{"level":2,"text":"Factor and FactorEngine","anchor":"factor-and-factorengine","htmlText":"Factor and FactorEngine"},{"level":2,"text":"Factor Analysis","anchor":"factor-analysis","htmlText":"Factor Analysis"},{"level":3,"text":"Diagram","anchor":"diagram","htmlText":"Diagram"},{"level":3,"text":"Compatible with alphalens","anchor":"compatible-with-alphalens","htmlText":"Compatible with alphalens"},{"level":2,"text":"Back-testing","anchor":"back-testing","htmlText":"Back-testing"},{"level":1,"text":"API","anchor":"api","htmlText":"API"},{"level":2,"text":"Note","anchor":"note","htmlText":"Note"},{"level":3,"text":"Differences to zipline:","anchor":"differences-to-zipline","htmlText":"Differences to zipline:"},{"level":3,"text":"Differences to common chart:","anchor":"differences-to-common-chart","htmlText":"Differences to common chart:"},{"level":2,"text":"Factors","anchor":"factors","htmlText":"Factors"},{"level":2,"text":"Built-in Technical Indicator Factors list","anchor":"built-in-technical-indicator-factors-list","htmlText":"Built-in Technical Indicator Factors list"},{"level":2,"text":"Factors Common Methods","anchor":"factors-common-methods","htmlText":"Factors Common Methods"},{"level":2,"text":"Dataloader","anchor":"dataloader-1","htmlText":"Dataloader"},{"level":3,"text":"CsvDirLoader","anchor":"csvdirloader","htmlText":"CsvDirLoader"},{"level":3,"text":"ArrowLoader","anchor":"arrowloader","htmlText":"ArrowLoader"},{"level":3,"text":"QuandlLoader","anchor":"quandlloader","htmlText":"QuandlLoader"},{"level":3,"text":"How to write your own DataLoader","anchor":"how-to-write-your-own-dataloader","htmlText":"How to write your own DataLoader"},{"level":2,"text":"FactorEngine","anchor":"factorengine","htmlText":"FactorEngine"},{"level":3,"text":"FactorEngine.init","anchor":"factorengineinit","htmlText":"FactorEngine.init"},{"level":3,"text":"FactorEngine.add","anchor":"factorengineadd","htmlText":"FactorEngine.add"},{"level":3,"text":"FactorEngine.set_filter","anchor":"factorengineset_filter","htmlText":"FactorEngine.set_filter"},{"level":3,"text":"FactorEngine.align_by_time","anchor":"factorenginealign_by_time","htmlText":"FactorEngine.align_by_time"},{"level":3,"text":"FactorEngine.clear","anchor":"factorengineclear","htmlText":"FactorEngine.clear"},{"level":3,"text":"FactorEngine.to_cuda","anchor":"factorengineto_cuda","htmlText":"FactorEngine.to_cuda"},{"level":3,"text":"FactorEngine.to_cpu","anchor":"factorengineto_cpu","htmlText":"FactorEngine.to_cpu"},{"level":3,"text":"FactorEngine.run","anchor":"factorenginerun","htmlText":"FactorEngine.run"},{"level":4,"text":"*Auto Delay","anchor":"auto-delay","htmlText":"*Auto Delay"},{"level":3,"text":"FactorEngine.plot_chart","anchor":"factorengineplot_chart","htmlText":"FactorEngine.plot_chart"},{"level":3,"text":"FactorEngine.full_run","anchor":"factorenginefull_run","htmlText":"FactorEngine.full_run"},{"level":3,"text":"FactorEngine.get_price_matrix","anchor":"factorengineget_price_matrix","htmlText":"FactorEngine.get_price_matrix"},{"level":3,"text":"FactorEngine.test_lookahead_bias","anchor":"factorenginetest_lookahead_bias","htmlText":"FactorEngine.test_lookahead_bias"},{"level":2,"text":"ColumnDataFactor","anchor":"columndatafactor","htmlText":"ColumnDataFactor"},{"level":2,"text":"How to write your own factor","anchor":"how-to-write-your-own-factor","htmlText":"How to write your own factor"},{"level":3,"text":"win = 1","anchor":"win--1","htmlText":"win = 1"},{"level":3,"text":"win \u003e 1","anchor":"win--1-1","htmlText":"win \u0026gt; 1"},{"level":3,"text":"Using Pandas Series","anchor":"using-pandas-series","htmlText":"Using Pandas Series"},{"level":2,"text":"Back-testing","anchor":"back-testing-1","htmlText":"Back-testing"},{"level":3,"text":"CustomAlgorithm.initialize","anchor":"customalgorithminitialize","htmlText":"CustomAlgorithm.initialize"},{"level":3,"text":"CustomAlgorithm.terminate","anchor":"customalgorithmterminate","htmlText":"CustomAlgorithm.terminate"},{"level":3,"text":"rebalance callback","anchor":"rebalance-callback","htmlText":"rebalance 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fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m11.28 3.22 4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L13.94 8l-3.72-3.72a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215Zm-6.56 0a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042L2.06 8l3.72 3.72a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L.47 8.53a.75.75 0 0 1 0-1.06Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x">Code</span><span data-component="trailingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-triangle-down" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m4.427 7.427 3.396 3.396a.25.25 0 0 0 .354 0l3.396-3.396A.25.25 0 0 0 11.396 7H4.604a.25.25 0 0 0-.177.427Z"></path></svg></span></span></button><div 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data-component="leadingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x"><span class="fgColor-default">420 Commits</span></span></span></a><div class="d-sm-none"></div><div class="d-flex d-lg-none"><span role="tooltip" aria-label="420 Commits" id="history-icon-button-tooltip" class="Tooltip__TooltipBase-sc-17tf59c-0 hWlpPn tooltipped-n"><a 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js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><p dir="auto"><a href="https://coveralls.io/github/Heerozh/spectre?branch=master" rel="nofollow"><img src="https://camo.githubusercontent.com/e8780bfcf770a304370aef53317d651295a584fe9cb3a6db13eb3c004037a134/68747470733a2f2f636f766572616c6c732e696f2f7265706f732f6769746875622f486565726f7a682f737065637472652f62616467652e7376673f6272616e63683d6d6173746572" alt="Coverage Status" data-canonical-src="https://coveralls.io/repos/github/Heerozh/spectre/badge.svg?branch=master" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">||spectre</h1><a id="user-content-spectre" class="anchor" aria-label="Permalink: ||spectre" href="#spectre"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">spectre is a <strong>GPU-accelerated Parallel</strong> quantitative trading library, focused on <strong>performance</strong>.</p> <ul dir="auto"> <li>Fast GPU Factor Engine, see below <a href="#benchmarks">Benchmarks</a></li> <li>Pure python code, based on PyTorch, so it can integrate DL model very smoothly.</li> <li>Compatible with <code>alphalens</code> and <code>pyfolio</code></li> </ul> <p dir="auto">Python 3.7+, PyTorch 1.3+, Pandas 1.0+ recommended</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Installation</h1><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install --no-deps git+git://github.com/Heerozh/spectre.git"><pre>pip install --no-deps git+git://github.com/Heerozh/spectre.git</pre></div> <p dir="auto">Dependencies:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch conda install pyarrow pandas tqdm plotly requests"><pre>conda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch conda install pyarrow pandas tqdm plotly requests</pre></div> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Benchmarks</h1><a id="user-content-benchmarks" class="anchor" aria-label="Permalink: Benchmarks" href="#benchmarks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">My Machine：</p> <ul dir="auto"> <li>i9-7900X @ 3.30GHz, 20 Cores</li> <li>DDR4 3800MHz</li> <li>3090: GIGABYTE GeForce RTX 3090 GAMING OC 24G</li> <li>2080Ti: RTX 2080Ti Founders</li> </ul> <p dir="auto">Running on Quandl 5 years, 3196 Assets, total 3,637,344 bars.</p> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th>spectre (CUDA/3090)</th> <th>spectre (CUDA/2080Ti)</th> <th>spectre (CPU)</th> <th>zipline.pipeline</th> </tr> </thead> <tbody> <tr> <td>SMA(100)</td> <td>87.9 ms ± 3.35 ms (<strong>33.9x</strong>)</td> <td>144 ms ± 974 µs (<strong>20.7x</strong>)</td> <td>2.68 s ± 36.1 ms (1.11x)</td> <td>2.98 s ± 14.4 ms (1x)</td> </tr> <tr> <td>EMA(50) win=229</td> <td>166 ms ± 3.25 ms (<strong>50.5x</strong>)</td> <td>270 ms ± 1.89 ms (<strong>31.0x</strong>)</td> <td>4.37 s ± 46.4 ms (1.74x)</td> <td>8.38 s ± 56.8 ms (1x)</td> </tr> <tr> <td>(MACD+RSI+STOCHF).rank.zscore</td> <td>184 ms ± 7.83 ms (<strong>77.7x</strong>)</td> <td>282 ms ± 1.33 ms (<strong>50.7x</strong>)</td> <td>6.01 s ± 28.1 (2.38x)</td> <td>14.3 s ± 277 ms (1x)</td> </tr> </tbody> </table></markdown-accessiblity-table> <ul dir="auto"> <li>The CUDA memory used in the spectre benchmark is 1.8G, returned by cuda.max_memory_allocated().</li> <li>Benchmarks excluded the initial run (no copy data to VRAM, about saving 300ms).</li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Quick Start</h1><a id="user-content-quick-start" class="anchor" aria-label="Permalink: Quick Start" href="#quick-start"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">DataLoader</h2><a id="user-content-dataloader" class="anchor" aria-label="Permalink: DataLoader" href="#dataloader"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">First of all is data, you can use <a href="#csvdirloader">CsvDirLoader</a> read your csv files.</p> <p dir="auto">spectre also has built-in Yahoo downloader, <code>symbols=None</code> will download all SP500 components.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from spectre.data import YahooDownloader YahooDownloader.ingest(start_date="2001", save_to="./prices/yahoo", symbols=None, skip_exists=True)"><pre><span class="pl-k">from</span> <span class="pl-s1">spectre</span>.<span class="pl-s1">data</span> <span class="pl-k">import</span> <span class="pl-v">YahooDownloader</span> <span class="pl-v">YahooDownloader</span>.<span class="pl-c1">ingest</span>(<span class="pl-s1">start_date</span><span class="pl-c1">=</span><span class="pl-s">"2001"</span>, <span class="pl-s1">save_to</span><span class="pl-c1">=</span><span class="pl-s">"./prices/yahoo"</span>, <span class="pl-s1">symbols</span><span class="pl-c1">=</span><span class="pl-c1">None</span>, <span class="pl-s1">skip_exists</span><span class="pl-c1">=</span><span class="pl-c1">True</span>)</pre></div> <p dir="auto">You can use <code>spectre.data.ArrowLoader('./prices/yahoo/yahoo.feather')</code> load those data now.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Factor and FactorEngine</h2><a id="user-content-factor-and-factorengine" class="anchor" aria-label="Permalink: Factor and FactorEngine" href="#factor-and-factorengine"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from spectre import factors from spectre.data import ArrowLoader loader = ArrowLoader('./prices/yahoo/yahoo.feather') engine = factors.FactorEngine(loader) engine.to_cuda() engine.add(factors.SMA(5), 'ma5') engine.add(factors.OHLCV.close, 'close') df = engine.run('2019-01-11', '2019-01-15') df"><pre><span class="pl-k">from</span> <span class="pl-s1">spectre</span> <span class="pl-k">import</span> <span class="pl-s1">factors</span> <span class="pl-k">from</span> <span class="pl-s1">spectre</span>.<span class="pl-s1">data</span> <span class="pl-k">import</span> <span class="pl-v">ArrowLoader</span> <span class="pl-s1">loader</span> <span class="pl-c1">=</span> <span class="pl-en">ArrowLoader</span>(<span class="pl-s">'./prices/yahoo/yahoo.feather'</span>) <span class="pl-s1">engine</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">FactorEngine</span>(<span class="pl-s1">loader</span>) <span class="pl-s1">engine</span>.<span class="pl-c1">to_cuda</span>() <span class="pl-s1">engine</span>.<span class="pl-c1">add</span>(<span class="pl-s1">factors</span>.<span class="pl-c1">SMA</span>(<span class="pl-c1">5</span>), <span class="pl-s">'ma5'</span>) <span class="pl-s1">engine</span>.<span class="pl-c1">add</span>(<span class="pl-s1">factors</span>.<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>, <span class="pl-s">'close'</span>) <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">engine</span>.<span class="pl-c1">run</span>(<span class="pl-s">'2019-01-11'</span>, <span class="pl-s">'2019-01-15'</span>) <span class="pl-s1">df</span></pre></div> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th></th> <th>ma5</th> <th>close</th> </tr> </thead> <tbody> <tr> <td><strong>date</strong></td> <td><strong>asset</strong></td> <td></td> <td></td> </tr> <tr> <td>2019-01-14 00:00:00+00:00</td> <td>A</td> <td>68.842003</td> <td>70.379997</td> </tr> <tr> <td></td> <td>AAPL</td> <td>151.615997</td> <td>152.289993</td> </tr> <tr> <td></td> <td>ABC</td> <td>75.835999</td> <td>76.559998</td> </tr> <tr> <td></td> <td>ABT</td> <td>69.056000</td> <td>69.330002</td> </tr> <tr> <td></td> <td>ADBE</td> <td>234.537994</td> <td>237.550003</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> <tr> <td>2019-01-15 00:00:00+00:00</td> <td>XYL</td> <td>68.322006</td> <td>69.160004</td> </tr> <tr> <td></td> <td>YUM</td> <td>91.010002</td> <td>90.000000</td> </tr> <tr> <td></td> <td>ZBH</td> <td>102.932007</td> <td>102.690002</td> </tr> <tr> <td></td> <td>ZION</td> <td>43.760002</td> <td>44.320000</td> </tr> <tr> <td></td> <td>ZTS</td> <td>85.846001</td> <td>84.500000</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Factor Analysis</h2><a id="user-content-factor-analysis" class="anchor" aria-label="Permalink: Factor Analysis" href="#factor-analysis"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from spectre import factors import math risk_free_rate = 0.04 / 252 excess_logret = factors.LogReturns() - math.log(1 + risk_free_rate) universe = factors.AverageDollarVolume(win=120).top(100) # Barra MOMENTUM ema126 = factors.EMA(half_life=126, inputs=[excess_logret]) rstr = ema126.shift(11).sum(252) MOMENTUM = rstr # Barra Volatility ema42 = factors.EMA(half_life=42, inputs=[excess_logret]) dastd = factors.STDDEV(252, inputs=[ema42]) VOLATILITY = dastd # run engine from spectre.data import ArrowLoader loader = ArrowLoader('./prices/yahoo/yahoo.feather') engine = factors.FactorEngine(loader) engine.set_filter( universe ) engine.add( MOMENTUM, 'MOMENTUM' ) engine.add( VOLATILITY, 'VOLATILITY' ) engine.to_cuda() %time factor_data, mean_return = engine.full_run("2013-01-02", "2018-01-19", periods=(1,5,10,))"><pre><span class="pl-k">from</span> <span class="pl-s1">spectre</span> <span class="pl-k">import</span> <span class="pl-s1">factors</span> <span class="pl-k">import</span> <span class="pl-s1">math</span> <span class="pl-s1">risk_free_rate</span> <span class="pl-c1">=</span> <span class="pl-c1">0.04</span> <span class="pl-c1">/</span> <span class="pl-c1">252</span> <span class="pl-s1">excess_logret</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">LogReturns</span>() <span class="pl-c1">-</span> <span class="pl-s1">math</span>.<span class="pl-c1">log</span>(<span class="pl-c1">1</span> <span class="pl-c1">+</span> <span class="pl-s1">risk_free_rate</span>) <span class="pl-s1">universe</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">AverageDollarVolume</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">120</span>).<span class="pl-c1">top</span>(<span class="pl-c1">100</span>) <span class="pl-c"># Barra MOMENTUM</span> <span class="pl-s1">ema126</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">EMA</span>(<span class="pl-s1">half_life</span><span class="pl-c1">=</span><span class="pl-c1">126</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-s1">excess_logret</span>]) <span class="pl-s1">rstr</span> <span class="pl-c1">=</span> <span class="pl-s1">ema126</span>.<span class="pl-c1">shift</span>(<span class="pl-c1">11</span>).<span class="pl-c1">sum</span>(<span class="pl-c1">252</span>) <span class="pl-c1">MOMENTUM</span> <span class="pl-c1">=</span> <span class="pl-s1">rstr</span> <span class="pl-c"># Barra Volatility</span> <span class="pl-s1">ema42</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">EMA</span>(<span class="pl-s1">half_life</span><span class="pl-c1">=</span><span class="pl-c1">42</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-s1">excess_logret</span>]) <span class="pl-s1">dastd</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">STDDEV</span>(<span class="pl-c1">252</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-s1">ema42</span>]) <span class="pl-c1">VOLATILITY</span> <span class="pl-c1">=</span> <span class="pl-s1">dastd</span> <span class="pl-c"># run engine</span> <span class="pl-k">from</span> <span class="pl-s1">spectre</span>.<span class="pl-s1">data</span> <span class="pl-k">import</span> <span class="pl-v">ArrowLoader</span> <span class="pl-s1">loader</span> <span class="pl-c1">=</span> <span class="pl-en">ArrowLoader</span>(<span class="pl-s">'./prices/yahoo/yahoo.feather'</span>) <span class="pl-s1">engine</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">FactorEngine</span>(<span class="pl-s1">loader</span>) <span class="pl-s1">engine</span>.<span class="pl-c1">set_filter</span>( <span class="pl-s1">universe</span> ) <span class="pl-s1">engine</span>.<span class="pl-c1">add</span>( <span class="pl-c1">MOMENTUM</span>, <span class="pl-s">'MOMENTUM'</span> ) <span class="pl-s1">engine</span>.<span class="pl-c1">add</span>( <span class="pl-c1">VOLATILITY</span>, <span class="pl-s">'VOLATILITY'</span> ) <span class="pl-s1">engine</span>.<span class="pl-c1">to_cuda</span>() <span class="pl-c1">%</span><span class="pl-s1">time</span> <span class="pl-s1">factor_data</span>, <span class="pl-s1">mean_return</span> <span class="pl-c1">=</span> <span class="pl-s1">engine</span>.<span class="pl-c1">full_run</span>(<span class="pl-s">"2013-01-02"</span>, <span class="pl-s">"2018-01-19"</span>, <span class="pl-s1">periods</span><span class="pl-c1">=</span>(<span class="pl-c1">1</span>,<span class="pl-c1">5</span>,<span class="pl-c1">10</span>,))</pre></div> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://github.com/Heerozh/spectre/raw/media/full_run.png"><img src="https://github.com/Heerozh/spectre/raw/media/full_run.png" width="800" height="600" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Diagram</h3><a id="user-content-diagram" class="anchor" aria-label="Permalink: Diagram" href="#diagram"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can also view your factor structure graphically:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="factors.BBANDS(win=5).normalized().rank().zscore().show_graph()"><pre><span class="pl-s1">factors</span>.<span class="pl-c1">BBANDS</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">5</span>).<span class="pl-c1">normalized</span>().<span class="pl-c1">rank</span>().<span class="pl-c1">zscore</span>().<span class="pl-c1">show_graph</span>()</pre></div> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://github.com/Heerozh/spectre/raw/media/factor_diagram.png"><img src="https://github.com/Heerozh/spectre/raw/media/factor_diagram.png" width="800" height="360" style="max-width: 100%;"></a></p> <p dir="auto">The thickness of the line represents the length of the Rolling Window, kind of like "bandwidth".</p> <p dir="auto">If <code>engine.to_cuda(enable_stream=True)</code>, the calculation of the branches will be performed simultaneously, but the VRAM usage will increase proportionally.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Compatible with alphalens</h3><a id="user-content-compatible-with-alphalens" class="anchor" aria-label="Permalink: Compatible with alphalens" href="#compatible-with-alphalens"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The return value of <code>full_run</code> is compatible with <code>alphalens</code>:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import alphalens as al ... factor_data, _ = engine.full_run("2013-01-02", "2018-01-19") clean_data = factor_data[['{factor_name}', 'Returns']].droplevel(0, axis=1) al.tears.create_full_tear_sheet(clean_data)"><pre><span class="pl-k">import</span> <span class="pl-s1">alphalens</span> <span class="pl-k">as</span> <span class="pl-s1">al</span> ... <span class="pl-s1">factor_data</span>, <span class="pl-s1">_</span> <span class="pl-c1">=</span> <span class="pl-s1">engine</span>.<span class="pl-c1">full_run</span>(<span class="pl-s">"2013-01-02"</span>, <span class="pl-s">"2018-01-19"</span>) <span class="pl-s1">clean_data</span> <span class="pl-c1">=</span> <span class="pl-s1">factor_data</span>[[<span class="pl-s">'{factor_name}'</span>, <span class="pl-s">'Returns'</span>]].<span class="pl-c1">droplevel</span>(<span class="pl-c1">0</span>, <span class="pl-s1">axis</span><span class="pl-c1">=</span><span class="pl-c1">1</span>) <span class="pl-s1">al</span>.<span class="pl-c1">tears</span>.<span class="pl-c1">create_full_tear_sheet</span>(<span class="pl-s1">clean_data</span>)</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Back-testing</h2><a id="user-content-back-testing" class="anchor" aria-label="Permalink: Back-testing" href="#back-testing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Back-testing uses FactorEngine's results as data, market event as triggers.</p> <p dir="auto">You can find other examples in the <code>./examples</code> directory.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from spectre import factors, trading from spectre.data import ArrowLoader import pandas as pd, math class MyAlg(trading.CustomAlgorithm): def initialize(self): # your factors risk_free_rate = 0.04 / 252 excess_logret = factors.LogReturns() - math.log(1 + risk_free_rate) universe = factors.AverageDollarVolume(win=120).top(100) # Barra MOMENTUM Risk Factor ema126 = factors.EMA(half_life=126, inputs=[excess_logret]) rstr = ema126.shift(11).sum(252) MOMENTUM = rstr.zscore(mask=universe) # Barra Volatility Risk Factor ema42 = factors.EMA(half_life=42, inputs=[excess_logret]) dastd = factors.STDDEV(252, inputs=[ema42]) VOLATILITY = dastd.zscore(mask=universe) # setup engine engine = self.get_factor_engine() engine.to_cuda() engine.set_filter( universe ) engine.add( (MOMENTUM + VOLATILITY).to_weight(), 'alpha_weight' ) # schedule rebalance before market close self.schedule_rebalance(trading.event.MarketClose(self.rebalance, offset_ns=-10000)) # simulation parameters self.blotter.capital_base = 1000000 self.blotter.set_commission(percentage=0, per_share=0.005, minimum=1) # self.blotter.set_slippage(percentage=0, per_share=0.4) def rebalance(self, data: 'pd.DataFrame', history: 'pd.DataFrame'): data = data.fillna(0) self.blotter.batch_order_target_percent(data.index, data.alpha_weight) # closing asset position that are no longer in our universe. removes = self.blotter.portfolio.positions.keys() - set(data.index) self.blotter.batch_order_target_percent(removes, [0] * len(removes)) # record data for debugging / plotting self.record(aapl_weight=data.loc['AAPL', 'alpha_weight'], aapl_price=self.blotter.get_price('AAPL')) def terminate(self, records: 'pd.DataFrame'): # plotting results self.plot(benchmark='SPY') # plotting the relationship between AAPL price and weight ax1 = records.aapl_price.plot() ax2 = ax1.twinx() records.aapl_weight.plot(ax=ax2, style='g-') loader = ArrowLoader('./prices/yahoo/yahoo.feather') %time results = trading.run_backtest(loader, MyAlg, '2014-01-01', '2019-01-01')"><pre><span class="pl-k">from</span> <span class="pl-s1">spectre</span> <span class="pl-k">import</span> <span class="pl-s1">factors</span>, <span class="pl-s1">trading</span> <span class="pl-k">from</span> <span class="pl-s1">spectre</span>.<span class="pl-s1">data</span> <span class="pl-k">import</span> <span class="pl-v">ArrowLoader</span> <span class="pl-k">import</span> <span class="pl-s1">pandas</span> <span class="pl-k">as</span> <span class="pl-s1">pd</span>, <span class="pl-s1">math</span> <span class="pl-k">class</span> <span class="pl-v">MyAlg</span>(<span class="pl-s1">trading</span>.<span class="pl-c1">CustomAlgorithm</span>): <span class="pl-k">def</span> <span class="pl-en">initialize</span>(<span class="pl-s1">self</span>): <span class="pl-c"># your factors</span> <span class="pl-s1">risk_free_rate</span> <span class="pl-c1">=</span> <span class="pl-c1">0.04</span> <span class="pl-c1">/</span> <span class="pl-c1">252</span> <span class="pl-s1">excess_logret</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">LogReturns</span>() <span class="pl-c1">-</span> <span class="pl-s1">math</span>.<span class="pl-c1">log</span>(<span class="pl-c1">1</span> <span class="pl-c1">+</span> <span class="pl-s1">risk_free_rate</span>) <span class="pl-s1">universe</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">AverageDollarVolume</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">120</span>).<span class="pl-c1">top</span>(<span class="pl-c1">100</span>) <span class="pl-c"># Barra MOMENTUM Risk Factor</span> <span class="pl-s1">ema126</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">EMA</span>(<span class="pl-s1">half_life</span><span class="pl-c1">=</span><span class="pl-c1">126</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-s1">excess_logret</span>]) <span class="pl-s1">rstr</span> <span class="pl-c1">=</span> <span class="pl-s1">ema126</span>.<span class="pl-c1">shift</span>(<span class="pl-c1">11</span>).<span class="pl-c1">sum</span>(<span class="pl-c1">252</span>) <span class="pl-c1">MOMENTUM</span> <span class="pl-c1">=</span> <span class="pl-s1">rstr</span>.<span class="pl-c1">zscore</span>(<span class="pl-s1">mask</span><span class="pl-c1">=</span><span class="pl-s1">universe</span>) <span class="pl-c"># Barra Volatility Risk Factor</span> <span class="pl-s1">ema42</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">EMA</span>(<span class="pl-s1">half_life</span><span class="pl-c1">=</span><span class="pl-c1">42</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-s1">excess_logret</span>]) <span class="pl-s1">dastd</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">STDDEV</span>(<span class="pl-c1">252</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-s1">ema42</span>]) <span class="pl-c1">VOLATILITY</span> <span class="pl-c1">=</span> <span class="pl-s1">dastd</span>.<span class="pl-c1">zscore</span>(<span class="pl-s1">mask</span><span class="pl-c1">=</span><span class="pl-s1">universe</span>) <span class="pl-c"># setup engine</span> <span class="pl-s1">engine</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">get_factor_engine</span>() <span class="pl-s1">engine</span>.<span class="pl-c1">to_cuda</span>() <span class="pl-s1">engine</span>.<span class="pl-c1">set_filter</span>( <span class="pl-s1">universe</span> ) <span class="pl-s1">engine</span>.<span class="pl-c1">add</span>( (<span class="pl-c1">MOMENTUM</span> <span class="pl-c1">+</span> <span class="pl-c1">VOLATILITY</span>).<span class="pl-c1">to_weight</span>(), <span class="pl-s">'alpha_weight'</span> ) <span class="pl-c"># schedule rebalance before market close</span> <span class="pl-s1">self</span>.<span class="pl-c1">schedule_rebalance</span>(<span class="pl-s1">trading</span>.<span class="pl-c1">event</span>.<span class="pl-c1">MarketClose</span>(<span class="pl-s1">self</span>.<span class="pl-c1">rebalance</span>, <span class="pl-s1">offset_ns</span><span class="pl-c1">=</span><span class="pl-c1">-</span><span class="pl-c1">10000</span>)) <span class="pl-c"># simulation parameters</span> <span class="pl-s1">self</span>.<span class="pl-c1">blotter</span>.<span class="pl-c1">capital_base</span> <span class="pl-c1">=</span> <span class="pl-c1">1000000</span> <span class="pl-s1">self</span>.<span class="pl-c1">blotter</span>.<span class="pl-c1">set_commission</span>(<span class="pl-s1">percentage</span><span class="pl-c1">=</span><span class="pl-c1">0</span>, <span class="pl-s1">per_share</span><span class="pl-c1">=</span><span class="pl-c1">0.005</span>, <span class="pl-s1">minimum</span><span class="pl-c1">=</span><span class="pl-c1">1</span>) <span class="pl-c"># self.blotter.set_slippage(percentage=0, per_share=0.4)</span> <span class="pl-k">def</span> <span class="pl-en">rebalance</span>(<span class="pl-s1">self</span>, <span class="pl-s1">data</span>: <span class="pl-s">'pd.DataFrame'</span>, <span class="pl-s1">history</span>: <span class="pl-s">'pd.DataFrame'</span>): <span class="pl-s1">data</span> <span class="pl-c1">=</span> <span class="pl-s1">data</span>.<span class="pl-c1">fillna</span>(<span class="pl-c1">0</span>) <span class="pl-s1">self</span>.<span class="pl-c1">blotter</span>.<span class="pl-c1">batch_order_target_percent</span>(<span class="pl-s1">data</span>.<span class="pl-c1">index</span>, <span class="pl-s1">data</span>.<span class="pl-c1">alpha_weight</span>) <span class="pl-c"># closing asset position that are no longer in our universe.</span> <span class="pl-s1">removes</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">blotter</span>.<span class="pl-c1">portfolio</span>.<span class="pl-c1">positions</span>.<span class="pl-c1">keys</span>() <span class="pl-c1">-</span> <span class="pl-en">set</span>(<span class="pl-s1">data</span>.<span class="pl-c1">index</span>) <span class="pl-s1">self</span>.<span class="pl-c1">blotter</span>.<span class="pl-c1">batch_order_target_percent</span>(<span class="pl-s1">removes</span>, [<span class="pl-c1">0</span>] <span class="pl-c1">*</span> <span class="pl-en">len</span>(<span class="pl-s1">removes</span>)) <span class="pl-c"># record data for debugging / plotting</span> <span class="pl-s1">self</span>.<span class="pl-c1">record</span>(<span class="pl-s1">aapl_weight</span><span class="pl-c1">=</span><span class="pl-s1">data</span>.<span class="pl-c1">loc</span>[<span class="pl-s">'AAPL'</span>, <span class="pl-s">'alpha_weight'</span>], <span class="pl-s1">aapl_price</span><span class="pl-c1">=</span><span class="pl-s1">self</span>.<span class="pl-c1">blotter</span>.<span class="pl-c1">get_price</span>(<span class="pl-s">'AAPL'</span>)) <span class="pl-k">def</span> <span class="pl-en">terminate</span>(<span class="pl-s1">self</span>, <span class="pl-s1">records</span>: <span class="pl-s">'pd.DataFrame'</span>): <span class="pl-c"># plotting results</span> <span class="pl-s1">self</span>.<span class="pl-c1">plot</span>(<span class="pl-s1">benchmark</span><span class="pl-c1">=</span><span class="pl-s">'SPY'</span>) <span class="pl-c"># plotting the relationship between AAPL price and weight</span> <span class="pl-s1">ax1</span> <span class="pl-c1">=</span> <span class="pl-s1">records</span>.<span class="pl-c1">aapl_price</span>.<span class="pl-c1">plot</span>() <span class="pl-s1">ax2</span> <span class="pl-c1">=</span> <span class="pl-s1">ax1</span>.<span class="pl-c1">twinx</span>() <span class="pl-s1">records</span>.<span class="pl-c1">aapl_weight</span>.<span class="pl-c1">plot</span>(<span class="pl-s1">ax</span><span class="pl-c1">=</span><span class="pl-s1">ax2</span>, <span class="pl-s1">style</span><span class="pl-c1">=</span><span class="pl-s">'g-'</span>) <span class="pl-s1">loader</span> <span class="pl-c1">=</span> <span class="pl-en">ArrowLoader</span>(<span class="pl-s">'./prices/yahoo/yahoo.feather'</span>) <span class="pl-c1">%</span><span class="pl-s1">time</span> <span class="pl-s1">results</span> <span class="pl-c1">=</span> <span class="pl-s1">trading</span>.<span class="pl-c1">run_backtest</span>(<span class="pl-s1">loader</span>, <span class="pl-v">MyAlg</span>, <span class="pl-s">'2014-01-01'</span>, <span class="pl-s">'2019-01-01'</span>)</pre></div> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://github.com/Heerozh/spectre/raw/media/backtest.png"><img src="https://github.com/Heerozh/spectre/raw/media/backtest.png" width="800" height="630" style="max-width: 100%;"></a></p> <p dir="auto">It awful but you get the idea.</p> <p dir="auto">The return value of <code>run_backtest</code> is compatible with <code>pyfolio</code>:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import pyfolio as pf pf.create_full_tear_sheet(results.returns, positions=results.positions.value, transactions=results.transactions, live_start_date='2017-01-03')"><pre><span class="pl-k">import</span> <span class="pl-s1">pyfolio</span> <span class="pl-k">as</span> <span class="pl-s1">pf</span> <span class="pl-s1">pf</span>.<span class="pl-c1">create_full_tear_sheet</span>(<span class="pl-s1">results</span>.<span class="pl-c1">returns</span>, <span class="pl-s1">positions</span><span class="pl-c1">=</span><span class="pl-s1">results</span>.<span class="pl-c1">positions</span>.<span class="pl-c1">value</span>, <span class="pl-s1">transactions</span><span class="pl-c1">=</span><span class="pl-s1">results</span>.<span class="pl-c1">transactions</span>, <span class="pl-s1">live_start_date</span><span class="pl-c1">=</span><span class="pl-s">'2017-01-03'</span>)</pre></div> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">API</h1><a id="user-content-api" class="anchor" aria-label="Permalink: API" href="#api"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Note</h2><a id="user-content-note" class="anchor" aria-label="Permalink: Note" href="#note"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Differences to zipline:</h3><a id="user-content-differences-to-zipline" class="anchor" aria-label="Permalink: Differences to zipline:" href="#differences-to-zipline"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>In order to GPU optimize, the <code>CustomFactor.compute</code> function calculates the results of all bars at once, so you need to be careful to prevent Look-Ahead Bias, because the inputs are not just historical data. Also using <code>engine.test_lookahead_bias</code> do some tests.</li> <li>spectre's normally using float32 data type for GPU performance.</li> <li>spectre FactorEngine arranges data by bars, so <code>Return(win=10)</code> means 10 bars return, may actually be more than 10 days if some assets not open trading in period. You can change this behavior by aligning data: filling missing bars with NaNs in your DataLoader, please refer to the <code>align_by_time</code> parameter of <code>CsvDirLoader</code>.</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Differences to common chart:</h3><a id="user-content-differences-to-common-chart" class="anchor" aria-label="Permalink: Differences to common chart:" href="#differences-to-common-chart"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>If there is adjustments data, the prices is re-adjusted every day, so the factor you got, like MA, will be different from the stock chart software which only adjusted according to last day. If you want adjusted by last day, use like 'AdjustedColumnDataFactor(OHLCV.close)' as input data. This will speeds up a lot because it only needs to be adjusted once, but brings Look-Ahead Bias.</li> <li>Factors that uses the close data will be delayed by 1 bar.</li> <li>spectre's <code>EMA</code> uses the algorithm same as <code>zipline</code> and <code>Dataframe.ewm(span=...)</code>, when <code>span</code> is greater than 100, it will be slightly different from common EMA.</li> <li>spectre's <code>RSI</code> uses the algorithm same as <code>zipline</code>, for consistency in benchmarks.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Factors</h2><a id="user-content-factors" class="anchor" aria-label="Permalink: Factors" href="#factors"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Built-in Technical Indicator Factors list</h2><a id="user-content-built-in-technical-indicator-factors-list" class="anchor" aria-label="Permalink: Built-in Technical Indicator Factors list" href="#built-in-technical-indicator-factors-list"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="Returns(inputs=[OHLCV.close]) LogReturns(inputs=[OHLCV.close]) SimpleMovingAverage = MA = SMA(win=5, inputs=[OHLCV.close]) VWAP(inputs=[OHLCV.close, OHLCV.volume]) ExponentialWeightedMovingAverage = EMA(span=5, inputs=[OHLCV.close]) AverageDollarVolume(win=5, inputs=[OHLCV.close, OHLCV.volume]) AnnualizedVolatility(win=20, inputs=[Returns(win=2), 252]) BollingerBands = BBANDS(win=20, inputs=[OHLCV.close, 2]) MovingAverageConvergenceDivergenceSignal = MACD(12, 26, 9, inputs=[OHLCV.close]) TrueRange = TRANGE(inputs=[OHLCV.high, OHLCV.low, OHLCV.close]) RSI(win=14, inputs=[OHLCV.close]) FastStochasticOscillator = STOCHF(win=14, inputs=[OHLCV.high, OHLCV.low, OHLCV.close]) StandardDeviation = STDDEV(win=5, inputs=[OHLCV.close]) RollingHigh = MAX(win=5, inputs=[OHLCV.close]) RollingLow = MIN(win=5, inputs=[OHLCV.close])"><pre><span class="pl-en">Returns</span>(<span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>]) <span class="pl-en">LogReturns</span>(<span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>]) <span class="pl-v">SimpleMovingAverage</span> <span class="pl-c1">=</span> <span class="pl-c1">MA</span> <span class="pl-c1">=</span> <span class="pl-en">SMA</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">5</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>]) <span class="pl-en">VWAP</span>(<span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>, <span class="pl-c1">OHLCV</span>.<span class="pl-c1">volume</span>]) <span class="pl-v">ExponentialWeightedMovingAverage</span> <span class="pl-c1">=</span> <span class="pl-en">EMA</span>(<span class="pl-s1">span</span><span class="pl-c1">=</span><span class="pl-c1">5</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>]) <span class="pl-en">AverageDollarVolume</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">5</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>, <span class="pl-c1">OHLCV</span>.<span class="pl-c1">volume</span>]) <span class="pl-en">AnnualizedVolatility</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">20</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-en">Returns</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">2</span>), <span class="pl-c1">252</span>]) <span class="pl-v">BollingerBands</span> <span class="pl-c1">=</span> <span class="pl-en">BBANDS</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">20</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>, <span class="pl-c1">2</span>]) <span class="pl-v">MovingAverageConvergenceDivergenceSignal</span> <span class="pl-c1">=</span> <span class="pl-en">MACD</span>(<span class="pl-c1">12</span>, <span class="pl-c1">26</span>, <span class="pl-c1">9</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>]) <span class="pl-v">TrueRange</span> <span class="pl-c1">=</span> <span class="pl-en">TRANGE</span>(<span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">high</span>, <span class="pl-c1">OHLCV</span>.<span class="pl-c1">low</span>, <span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>]) <span class="pl-en">RSI</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">14</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>]) <span class="pl-v">FastStochasticOscillator</span> <span class="pl-c1">=</span> <span class="pl-en">STOCHF</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">14</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">high</span>, <span class="pl-c1">OHLCV</span>.<span class="pl-c1">low</span>, <span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>]) <span class="pl-v">StandardDeviation</span> <span class="pl-c1">=</span> <span class="pl-en">STDDEV</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">5</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>]) <span class="pl-v">RollingHigh</span> <span class="pl-c1">=</span> <span class="pl-en">MAX</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">5</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>]) <span class="pl-v">RollingLow</span> <span class="pl-c1">=</span> <span class="pl-en">MIN</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">5</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>])</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Factors Common Methods</h2><a id="user-content-factors-common-methods" class="anchor" aria-label="Permalink: Factors Common Methods" href="#factors-common-methods"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Standardization new_factor = factor.rank(mask=filter) new_factor = factor.demean(mask=filter, groupby: 'dict or column_name'=None) new_factor = factor.zscore(mask=filter) new_factor = factor.to_weight(mask=filter, demean=True) # return a weight that sum(abs(weight)) = 1 # Quick computation new_factor = factor1 + factor1 new_factor = factor.abs() new_factor = factor.sum() # To filter (Comparison operator): new_filter = (factor1 < factor2) | (factor1 > 0) new_filter[n_features] = factor.one_hot() # one-hot encoding new_filter = factor.any(win=5) new_filter = factor.all(win=5) # Rank filter new_filter = factor.top(n) new_filter = factor.bottom(n) # Specific assets new_filter = StaticAssets({'AAPL', 'MSFT'}) # Local filter new_factor = factor.filter(some_filter) # fills elements of self with NaN where mask is False # Multiple returns selecting new_factor = factor[0] # Others new_factor = factor.shift(1) new_factor = factor.quantile(bins=5) # factor value quantile groupby datetime new_factor = factor.fill_na(0) new_factor = factor.fill_na(ffill=True) # propagate last valid observation forward to next valid "><pre><span class="pl-c"># Standardization</span> <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">rank</span>(<span class="pl-s1">mask</span><span class="pl-c1">=</span><span class="pl-s1">filter</span>) <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">demean</span>(<span class="pl-s1">mask</span><span class="pl-c1">=</span><span class="pl-s1">filter</span>, <span class="pl-s1">groupby</span>: <span class="pl-s">'dict or column_name'</span><span class="pl-c1">=</span><span class="pl-c1">None</span>) <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">zscore</span>(<span class="pl-s1">mask</span><span class="pl-c1">=</span><span class="pl-s1">filter</span>) <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">to_weight</span>(<span class="pl-s1">mask</span><span class="pl-c1">=</span><span class="pl-s1">filter</span>, <span class="pl-s1">demean</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-c"># return a weight that sum(abs(weight)) = 1</span> <span class="pl-c"># Quick computation</span> <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor1</span> <span class="pl-c1">+</span> <span class="pl-s1">factor1</span> <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">abs</span>() <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">sum</span>() <span class="pl-c"># To filter (Comparison operator):</span> <span class="pl-s1">new_filter</span> <span class="pl-c1">=</span> (<span class="pl-s1">factor1</span> <span class="pl-c1"><</span> <span class="pl-s1">factor2</span>) <span class="pl-c1">|</span> (<span class="pl-s1">factor1</span> <span class="pl-c1">></span> <span class="pl-c1">0</span>) <span class="pl-s1">new_filter</span>[<span class="pl-s1">n_features</span>] <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">one_hot</span>() <span class="pl-c"># one-hot encoding</span> <span class="pl-s1">new_filter</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">any</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">5</span>) <span class="pl-s1">new_filter</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">all</span>(<span class="pl-s1">win</span><span class="pl-c1">=</span><span class="pl-c1">5</span>) <span class="pl-c"># Rank filter</span> <span class="pl-s1">new_filter</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">top</span>(<span class="pl-s1">n</span>) <span class="pl-s1">new_filter</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">bottom</span>(<span class="pl-s1">n</span>) <span class="pl-c"># Specific assets</span> <span class="pl-s1">new_filter</span> <span class="pl-c1">=</span> <span class="pl-en">StaticAssets</span>({<span class="pl-s">'AAPL'</span>, <span class="pl-s">'MSFT'</span>}) <span class="pl-c"># Local filter</span> <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">filter</span>(<span class="pl-s1">some_filter</span>) <span class="pl-c"># fills elements of self with NaN where mask is False</span> <span class="pl-c"># Multiple returns selecting</span> <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>[<span class="pl-c1">0</span>] <span class="pl-c"># Others</span> <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">shift</span>(<span class="pl-c1">1</span>) <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">quantile</span>(<span class="pl-s1">bins</span><span class="pl-c1">=</span><span class="pl-c1">5</span>) <span class="pl-c"># factor value quantile groupby datetime</span> <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">fill_na</span>(<span class="pl-c1">0</span>) <span class="pl-s1">new_factor</span> <span class="pl-c1">=</span> <span class="pl-s1">factor</span>.<span class="pl-c1">fill_na</span>(<span class="pl-s1">ffill</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-c"># propagate last valid observation forward to next valid</span></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Dataloader</h2><a id="user-content-dataloader-1" class="anchor" aria-label="Permalink: Dataloader" href="#dataloader-1"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CsvDirLoader</h3><a id="user-content-csvdirloader" class="anchor" aria-label="Permalink: CsvDirLoader" href="#csvdirloader"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>loader = spectre.data.CsvDirLoader(prices_path: str, prices_by_year=False, earliest_date: pd.Timestamp = None, dividends_path=None, splits_path=None, file_pattern='*.csv', calender_asset: str = None, align_by_time=False, ohlcv=('open', 'high', 'low', 'close', 'volume'), adjustments=None, split_ratio_is_inverse=False, split_ratio_is_fraction=False, prices_index='date', dividends_index='exDate', splits_index='exDate', **read_csv)</code></p> <p dir="auto">Read CSV files in the directory, each file represents an asset.</p> <p dir="auto">Reading csv is very slow, so you also need to use <a href="#arrowloader">ArrowLoader</a>.</p> <p dir="auto"><strong>prices_path:</strong> Prices csv folder. When encountering duplicate datetime in <code>prices_index</code>, Loader will keep the last, drop others.<br> <strong>prices_index:</strong> <code>index_col</code>for csv in <code>prices_path</code><br> <strong>prices_by_year:</strong> If prices file name like 'spy_2017.csv', set this to True<br> <strong>ohlcv:</strong> Required, OHLCV column names. When you don't need to use <code>adjustments</code> and <code>factors.OHLCV</code>, you can set this to None.<br> <strong>adjustments:</strong> Optional, list, <code>dividend amount</code> and <code>splits ratio</code> column names.<br> <strong>dividends_path:</strong> Dividends csv folder, structured as one csv per asset. For duplicate data, loader will first drop the exact same rows, and then for the same <code>dividends_index</code> but different 'dividend amount(<code>adjustments[0]</code>)' rows, loader will sum them up. If <code>dividends_path</code> not set, the <code>adjustments[0]</code> column is considered to be included in the prices csv.<br> <strong>dividends_index:</strong> <code>index_col</code>for csv in <code>dividends_path</code>.<br> <strong>splits_path:</strong> Splits csv folder, structured as one csv per asset. When encountering duplicate datetime in <code>splits_index</code>, Loader will use the last non-NaN 'split ratio', drop others. If <code>splits_path</code> not set, the <code>adjustments[1]</code> column is considered to be included in the prices csv.<br> <strong>splits_index:</strong> <code>index_col</code>for csv in <code>splits_path</code>.<br> <strong>split_ratio_is_inverse:</strong> If split ratio calculated by to/from, set to True. For example, 2-for-1 split, to/form = 2, 1-for-15 Reverse Split, to/form = 0.6666...<br> <strong>split_ratio_is_fraction:</strong> If split ratio in csv is fraction string, like <code>1/3</code>, set to True.<br> <strong>file_pattern:</strong> csv file name pattern, default is '*.csv'.<br> <strong>earliest_date:</strong> Data before this date will not be read, save memory.<br> <strong>calender_asset:</strong> Asset name as trading calendar, like 'SPY', for clean up non-trading time data.<br> <strong>align_by_time:</strong> If True and <code>calender_asset</code> is not None, the index of datetime will be the same for all assets, if some assets have no data at that time, NaNs will be filled. The benefit is that the columns of data matrix in <code>CustomFactor.compute</code> will also be aligned.<br> <strong>**read_csv:</strong> Parameters for all csv when calling <code>pd.read_csv</code>. <code>parse_dates</code> or <code>date_parser</code> is required.</p> <p dir="auto">Example for load <a href="https://github.com/Heerozh/iex_fetcher">IEX</a> CSV files:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="usecols = {'date', 'uOpen', 'uHigh', 'uLow', 'uClose', 'uVolume', 'exDate', 'amount', 'ratio'} csv_loader = spectre.data.CsvDirLoader( './iex/daily/', calender_asset='SPY', dividends_path='./iex/dividends/', splits_path='./iex/splits/', ohlcv=('uOpen', 'uHigh', 'uLow', 'uClose', 'uVolume'), adjustments=('amount', 'ratio'), prices_index='date', dividends_index='exDate', splits_index='exDate', parse_dates=True, usecols=lambda x: x in usecols, dtype={'uOpen': np.float32, 'uHigh': np.float32, 'uLow': np.float32, 'uClose': np.float32, 'uVolume': np.float64, 'amount': np.float64, 'ratio': np.float64})"><pre><span class="pl-s1">usecols</span> <span class="pl-c1">=</span> {<span class="pl-s">'date'</span>, <span class="pl-s">'uOpen'</span>, <span class="pl-s">'uHigh'</span>, <span class="pl-s">'uLow'</span>, <span class="pl-s">'uClose'</span>, <span class="pl-s">'uVolume'</span>, <span class="pl-s">'exDate'</span>, <span class="pl-s">'amount'</span>, <span class="pl-s">'ratio'</span>} <span class="pl-s1">csv_loader</span> <span class="pl-c1">=</span> <span class="pl-s1">spectre</span>.<span class="pl-c1">data</span>.<span class="pl-c1">CsvDirLoader</span>( <span class="pl-s">'./iex/daily/'</span>, <span class="pl-s1">calender_asset</span><span class="pl-c1">=</span><span class="pl-s">'SPY'</span>, <span class="pl-s1">dividends_path</span><span class="pl-c1">=</span><span class="pl-s">'./iex/dividends/'</span>, <span class="pl-s1">splits_path</span><span class="pl-c1">=</span><span class="pl-s">'./iex/splits/'</span>, <span class="pl-s1">ohlcv</span><span class="pl-c1">=</span>(<span class="pl-s">'uOpen'</span>, <span class="pl-s">'uHigh'</span>, <span class="pl-s">'uLow'</span>, <span class="pl-s">'uClose'</span>, <span class="pl-s">'uVolume'</span>), <span class="pl-s1">adjustments</span><span class="pl-c1">=</span>(<span class="pl-s">'amount'</span>, <span class="pl-s">'ratio'</span>), <span class="pl-s1">prices_index</span><span class="pl-c1">=</span><span class="pl-s">'date'</span>, <span class="pl-s1">dividends_index</span><span class="pl-c1">=</span><span class="pl-s">'exDate'</span>, <span class="pl-s1">splits_index</span><span class="pl-c1">=</span><span class="pl-s">'exDate'</span>, <span class="pl-s1">parse_dates</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">usecols</span><span class="pl-c1">=</span><span class="pl-k">lambda</span> <span class="pl-s1">x</span>: <span class="pl-s1">x</span> <span class="pl-c1">in</span> <span class="pl-s1">usecols</span>, <span class="pl-s1">dtype</span><span class="pl-c1">=</span>{<span class="pl-s">'uOpen'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float32</span>, <span class="pl-s">'uHigh'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float32</span>, <span class="pl-s">'uLow'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float32</span>, <span class="pl-s">'uClose'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float32</span>, <span class="pl-s">'uVolume'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float64</span>, <span class="pl-s">'amount'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float64</span>, <span class="pl-s">'ratio'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float64</span>})</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ArrowLoader</h3><a id="user-content-arrowloader" class="anchor" aria-label="Permalink: ArrowLoader" href="#arrowloader"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Ingest data from other DataLoader into a feather file, speed up reading speed a lot.</p> <p dir="auto">3GB data takes about 7 seconds on initial load.</p> <p dir="auto"><strong>Ingest</strong> <code>spectre.data.ArrowLoader.ingest(source=CsvDirLoader(...), save_to='./filename.feather')</code></p> <p dir="auto"><strong>Read</strong> <code>loader = spectre.data.ArrowLoader('./filename.feather')</code></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">QuandlLoader</h3><a id="user-content-quandlloader" class="anchor" aria-label="Permalink: QuandlLoader" href="#quandlloader"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>no longer updated, only contain prices before 2018</strong></p> <p dir="auto">Download 'WIKI_PRICES.zip' (You need an account): <code>https://www.quandl.com/api/v3/datatables/WIKI/PRICES.csv?qopts.export=true&api_key=[yourapi_key]</code></p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from spectre.data import ArrowLoader, QuandlLoader ArrowLoader.ingest(source=QuandlLoader('WIKI_PRICES.zip'), save_to='wiki_prices.feather')"><pre><span class="pl-k">from</span> <span class="pl-s1">spectre</span>.<span class="pl-s1">data</span> <span class="pl-k">import</span> <span class="pl-v">ArrowLoader</span>, <span class="pl-v">QuandlLoader</span> <span class="pl-v">ArrowLoader</span>.<span class="pl-c1">ingest</span>(<span class="pl-s1">source</span><span class="pl-c1">=</span><span class="pl-en">QuandlLoader</span>(<span class="pl-s">'WIKI_PRICES.zip'</span>), <span class="pl-s1">save_to</span><span class="pl-c1">=</span><span class="pl-s">'wiki_prices.feather'</span>)</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How to write your own DataLoader</h3><a id="user-content-how-to-write-your-own-dataloader" class="anchor" aria-label="Permalink: How to write your own DataLoader" href="#how-to-write-your-own-dataloader"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Inherit from <code>DataLoader</code>, overriding the <code>_load</code> method, read data into a large <code>DataFrame</code>, index is <code>MultiIndex ['date', 'asset']</code>, where date is <code>Datetime</code> type, <code>asset</code> is <code>string</code> type, and then call <code>self._format(df, split_ratio_is_inverse)</code> to format the data. Also call <code>test_load</code> in your test case to do basic format testing.</p> <p dir="auto">For example, suppose you have a csv file that contains data for all assets:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="class YourLoader(spectre.data.DataLoader): @property def last_modified(self) -> float: return os.path.getmtime(self._path) def __init__(self, file: str, calender_asset='SPY') -> None: super().__init__(file, ohlcv=('open', 'high', 'low', 'close', 'volume'), adjustments=('ex-dividend', 'split_ratio')) self._calender = calender_asset def _load(self) -> pd.DataFrame: df = pd.read_csv(self._path, parse_dates=['date'], usecols=['asset', 'date', 'open', 'high', 'low', 'close', 'volume', 'ex-dividend', 'split_ratio', ], dtype={ 'open': np.float32, 'high': np.float32, 'low': np.float32, 'close': np.float32, 'volume': np.float64, 'ex-dividend': np.float64, 'split_ratio': np.float64 }) df.set_index(['date', 'asset'], inplace=True) df = self._format(df, split_ratio_is_inverse=True) if self._calender: df = self._align_to(df, self._calender) return df"><pre><span class="pl-k">class</span> <span class="pl-v">YourLoader</span>(<span class="pl-s1">spectre</span>.<span class="pl-c1">data</span>.<span class="pl-c1">DataLoader</span>): <span class="pl-en">@<span class="pl-s1">property</span></span> <span class="pl-k">def</span> <span class="pl-en">last_modified</span>(<span class="pl-s1">self</span>) <span class="pl-c1">-></span> <span class="pl-smi">float</span>: <span class="pl-k">return</span> <span class="pl-s1">os</span>.<span class="pl-c1">path</span>.<span class="pl-c1">getmtime</span>(<span class="pl-s1">self</span>.<span class="pl-c1">_path</span>) <span class="pl-k">def</span> <span class="pl-en">__init__</span>(<span class="pl-s1">self</span>, <span class="pl-s1">file</span>: <span class="pl-smi">str</span>, <span class="pl-s1">calender_asset</span><span class="pl-c1">=</span><span class="pl-s">'SPY'</span>) <span class="pl-c1">-></span> <span class="pl-c1">None</span>: <span class="pl-en">super</span>().<span class="pl-c1">__init__</span>(<span class="pl-s1">file</span>, <span class="pl-s1">ohlcv</span><span class="pl-c1">=</span>(<span class="pl-s">'open'</span>, <span class="pl-s">'high'</span>, <span class="pl-s">'low'</span>, <span class="pl-s">'close'</span>, <span class="pl-s">'volume'</span>), <span class="pl-s1">adjustments</span><span class="pl-c1">=</span>(<span class="pl-s">'ex-dividend'</span>, <span class="pl-s">'split_ratio'</span>)) <span class="pl-s1">self</span>.<span class="pl-c1">_calender</span> <span class="pl-c1">=</span> <span class="pl-s1">calender_asset</span> <span class="pl-k">def</span> <span class="pl-en">_load</span>(<span class="pl-s1">self</span>) <span class="pl-c1">-></span> <span class="pl-s1">pd</span>.<span class="pl-c1">DataFrame</span>: <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-c1">read_csv</span>(<span class="pl-s1">self</span>.<span class="pl-c1">_path</span>, <span class="pl-s1">parse_dates</span><span class="pl-c1">=</span>[<span class="pl-s">'date'</span>], <span class="pl-s1">usecols</span><span class="pl-c1">=</span>[<span class="pl-s">'asset'</span>, <span class="pl-s">'date'</span>, <span class="pl-s">'open'</span>, <span class="pl-s">'high'</span>, <span class="pl-s">'low'</span>, <span class="pl-s">'close'</span>, <span class="pl-s">'volume'</span>, <span class="pl-s">'ex-dividend'</span>, <span class="pl-s">'split_ratio'</span>, ], <span class="pl-s1">dtype</span><span class="pl-c1">=</span>{ <span class="pl-s">'open'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float32</span>, <span class="pl-s">'high'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float32</span>, <span class="pl-s">'low'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float32</span>, <span class="pl-s">'close'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float32</span>, <span class="pl-s">'volume'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float64</span>, <span class="pl-s">'ex-dividend'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float64</span>, <span class="pl-s">'split_ratio'</span>: <span class="pl-s1">np</span>.<span class="pl-c1">float64</span> }) <span class="pl-s1">df</span>.<span class="pl-c1">set_index</span>([<span class="pl-s">'date'</span>, <span class="pl-s">'asset'</span>], <span class="pl-s1">inplace</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_format</span>(<span class="pl-s1">df</span>, <span class="pl-s1">split_ratio_is_inverse</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-k">if</span> <span class="pl-s1">self</span>.<span class="pl-c1">_calender</span>: <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_align_to</span>(<span class="pl-s1">df</span>, <span class="pl-s1">self</span>.<span class="pl-c1">_calender</span>) <span class="pl-k">return</span> <span class="pl-s1">df</span></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">FactorEngine</h2><a id="user-content-factorengine" class="anchor" aria-label="Permalink: FactorEngine" href="#factorengine"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A fast factor calculation pipeline.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.<strong>init</strong></h3><a id="user-content-factorengineinit" class="anchor" aria-label="Permalink: FactorEngine.init" href="#factorengineinit"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>engine = FactorEngine(loader: DataLoader)</code></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.add</h3><a id="user-content-factorengineadd" class="anchor" aria-label="Permalink: FactorEngine.add" href="#factorengineadd"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>engine.add(factor, column_name)</code></p> <p dir="auto">Add a factor to engine.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.set_filter</h3><a id="user-content-factorengineset_filter" class="anchor" aria-label="Permalink: FactorEngine.set_filter" href="#factorengineset_filter"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>engine.set_filter(factor: FilterFactor or None)</code></p> <p dir="auto">Set the Global Filter, engine deletes rows which Global Filter returns as False at the last step, affect all factors.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.align_by_time</h3><a id="user-content-factorenginealign_by_time" class="anchor" aria-label="Permalink: FactorEngine.align_by_time" href="#factorenginealign_by_time"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>engine.align_by_time = bool</code></p> <p dir="auto">Same as <code>CsvDirLoader(align_by_time=True)</code>, but it's dynamic. Notes: Very slow on large amounts of data, and if the data source is already aligned, this method cannot make it return to unaligned.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.clear</h3><a id="user-content-factorengineclear" class="anchor" aria-label="Permalink: FactorEngine.clear" href="#factorengineclear"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>engine.clear()</code></p> <p dir="auto">Remove global filter, and all factors.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.to_cuda</h3><a id="user-content-factorengineto_cuda" class="anchor" aria-label="Permalink: FactorEngine.to_cuda" href="#factorengineto_cuda"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>engine.to_cuda(enable_stream=False)</code></p> <p dir="auto">Switch to GPU mode.</p> <p dir="auto">Set enable_stream to True allows pipeline branches to calculation simultaneously. However, this will lead to more VRAM usage and may also affect performance.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.to_cpu</h3><a id="user-content-factorengineto_cpu" class="anchor" aria-label="Permalink: FactorEngine.to_cpu" href="#factorengineto_cpu"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>engine.to_cpu()</code></p> <p dir="auto">Switch to CPU mode.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.run</h3><a id="user-content-factorenginerun" class="anchor" aria-label="Permalink: FactorEngine.run" href="#factorenginerun"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>df = engine.run(start_time, end_time, delay_factor=True)</code></p> <p dir="auto">Run the engine to calculate the factor data, return a DataFrame. The column is each added factor.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">*Auto Delay</h4><a id="user-content-auto-delay" class="anchor" aria-label="Permalink: *Auto Delay" href="#auto-delay"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">By default, <code>delay_factor</code> is True, it means enable auto-delay. If 'high, low, close, volume' data is used by a terminal factor (including its upstream), that factor will be delayed by <code>shift(1)</code> in the last step, because in theory you can't trade on this factor before it generated. Others will not be delayed, in order to provide the latest data as much as possible.</p> <p dir="auto">Set to <code>False</code> to force engine not delay any factors.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.plot_chart</h3><a id="user-content-factorengineplot_chart" class="anchor" aria-label="Permalink: FactorEngine.plot_chart" href="#factorengineplot_chart"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>engine.plot_chart(start_time, end_time, trace_types=None, styles=None, delay_factor=True)</code></p> <p dir="auto">Plotting common stock price chart for researching.</p> <p dir="auto"><code>trace_types</code>: <code>dict(factor_name=plotly_trace_type)</code>, trace type can be 'Bar', or 'Scatter', default is 'Scatter'.</p> <p dir="auto"><code>styles</code>: <code>dict(factor_name=plotly_trace_styles)</code>, add the trace styles, please refer to plotly documentation: <a href="https://plot.ly/python/reference/#scatter" rel="nofollow">Scatter traces</a></p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="rsi = factors.RSI() buy_signal = (rsi.shift(1) < 30) & (rsi > 30) engine = factors.FactorEngine(loader) engine.timezone = 'America/New_York' engine.set_filter(factors.StaticAssets({'NVDA', 'MSFT'})) engine.add(factors.MA(20), 'MA20') engine.add(rsi, 'RSI') engine.add(factors.OHLCV.close.filter(buy_signal), 'Buy') engine.to_cuda() _ = engine.plot_chart('2017', '2018', styles={ 'MA20': { 'line': {'dash': 'dash'} }, 'RSI': { 'yaxis': 'y3', # y1: price axis, y2: volume axis, yN: add new y-axis 'line': {'width': 1} }, 'Buy': { 'mode': 'markers', 'marker': { 'symbol': 'triangle-up', 'size': 10, 'color': 'rgba(0, 0, 255, 0.5)' } } })"><pre><span class="pl-s1">rsi</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">RSI</span>() <span class="pl-s1">buy_signal</span> <span class="pl-c1">=</span> (<span class="pl-s1">rsi</span>.<span class="pl-c1">shift</span>(<span class="pl-c1">1</span>) <span class="pl-c1"><</span> <span class="pl-c1">30</span>) <span class="pl-c1">&</span> (<span class="pl-s1">rsi</span> <span class="pl-c1">></span> <span class="pl-c1">30</span>) <span class="pl-s1">engine</span> <span class="pl-c1">=</span> <span class="pl-s1">factors</span>.<span class="pl-c1">FactorEngine</span>(<span class="pl-s1">loader</span>) <span class="pl-s1">engine</span>.<span class="pl-c1">timezone</span> <span class="pl-c1">=</span> <span class="pl-s">'America/New_York'</span> <span class="pl-s1">engine</span>.<span class="pl-c1">set_filter</span>(<span class="pl-s1">factors</span>.<span class="pl-c1">StaticAssets</span>({<span class="pl-s">'NVDA'</span>, <span class="pl-s">'MSFT'</span>})) <span class="pl-s1">engine</span>.<span class="pl-c1">add</span>(<span class="pl-s1">factors</span>.<span class="pl-c1">MA</span>(<span class="pl-c1">20</span>), <span class="pl-s">'MA20'</span>) <span class="pl-s1">engine</span>.<span class="pl-c1">add</span>(<span class="pl-s1">rsi</span>, <span class="pl-s">'RSI'</span>) <span class="pl-s1">engine</span>.<span class="pl-c1">add</span>(<span class="pl-s1">factors</span>.<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>.<span class="pl-c1">filter</span>(<span class="pl-s1">buy_signal</span>), <span class="pl-s">'Buy'</span>) <span class="pl-s1">engine</span>.<span class="pl-c1">to_cuda</span>() <span class="pl-s1">_</span> <span class="pl-c1">=</span> <span class="pl-s1">engine</span>.<span class="pl-c1">plot_chart</span>(<span class="pl-s">'2017'</span>, <span class="pl-s">'2018'</span>, <span class="pl-s1">styles</span><span class="pl-c1">=</span>{ <span class="pl-s">'MA20'</span>: { <span class="pl-s">'line'</span>: {<span class="pl-s">'dash'</span>: <span class="pl-s">'dash'</span>} }, <span class="pl-s">'RSI'</span>: { <span class="pl-s">'yaxis'</span>: <span class="pl-s">'y3'</span>, <span class="pl-c"># y1: price axis, y2: volume axis, yN: add new y-axis</span> <span class="pl-s">'line'</span>: {<span class="pl-s">'width'</span>: <span class="pl-c1">1</span>} }, <span class="pl-s">'Buy'</span>: { <span class="pl-s">'mode'</span>: <span class="pl-s">'markers'</span>, <span class="pl-s">'marker'</span>: { <span class="pl-s">'symbol'</span>: <span class="pl-s">'triangle-up'</span>, <span class="pl-s">'size'</span>: <span class="pl-c1">10</span>, <span class="pl-s">'color'</span>: <span class="pl-s">'rgba(0, 0, 255, 0.5)'</span> } } })</pre></div> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://github.com/Heerozh/spectre/raw/media/chart.png"><img src="https://github.com/Heerozh/spectre/raw/media/chart.png" width="730" height="1000" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.full_run</h3><a id="user-content-factorenginefull_run" class="anchor" aria-label="Permalink: FactorEngine.full_run" href="#factorenginefull_run"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>factor_data, mean_returns = engine.full_run( start_time, end_time, trade_at='close', periods=(1, 4, 9), quantiles=5, filter_zscore=20, demean=True, preview=True)</code></p> <p dir="auto">Not only run the engine, but also run factor analysis.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.get_price_matrix</h3><a id="user-content-factorengineget_price_matrix" class="anchor" aria-label="Permalink: FactorEngine.get_price_matrix" href="#factorengineget_price_matrix"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>df_prices = engine.get_price_matrix(start_time, end_time, prices: ColumnDataFactor = OHLCV.close)</code></p> <p dir="auto">Get the adjusted historical prices matrix which columns is all assets.</p> <p dir="auto">If global filter is setted, all unfiltered assets from <code>start_time</code> to <code>end_time</code> will be included.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">FactorEngine.test_lookahead_bias</h3><a id="user-content-factorenginetest_lookahead_bias" class="anchor" aria-label="Permalink: FactorEngine.test_lookahead_bias" href="#factorenginetest_lookahead_bias"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>engine.test_lookahead_bias(start_time, end_time)</code></p> <p dir="auto">Run the engine to test if there is a lookahead bias.</p> <p dir="auto">Fill random values to second half of the ohlcv data, and then check if there are differences between the two runs in the first half.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">ColumnDataFactor</h2><a id="user-content-columndatafactor" class="anchor" aria-label="Permalink: ColumnDataFactor" href="#columndatafactor"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can use <code>ColumnDataFactor</code> to represents data from any column in the <code>DataLoader</code>, for example:</p> <p dir="auto"><code>spectre.factors.ColumnDataFactor(inputs=['col_name'])</code></p> <p dir="auto"><code>factors.OHLCV.close</code> is just a sugar way to write <code>spectre.factors.ColumnDataFactor (inputs = [data_loader.ohlcv[3]])</code>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How to write your own factor</h2><a id="user-content-how-to-write-your-own-factor" class="anchor" aria-label="Permalink: How to write your own factor" href="#how-to-write-your-own-factor"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Inherit from <code>factors.CustomFactor</code>, write <code>compute</code> function.</p> <p dir="auto">All <code>inputs</code> will pass to compute function.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">win = 1</h3><a id="user-content-win--1" class="anchor" aria-label="Permalink: win = 1" href="#win--1"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">When <code>win = 1</code>, the <code>inputs</code> data is tensor type, the first dimension of data is the asset, the second dimension is each bar price data. Note that if the data is <code>align_by_time=False</code>, the number of bars for each asset is different and not aligned (for example, the time for each price in bar_t3 column may be inconsistent).</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content=" +-----------------------------------+ | bar_t1 bar_t3 | | | | | | v v | | asset 1--> [[1.1, 1.2, 1.3, ...], | | asset 2--> [ 5, 6, 7, ...]] | +-----------------------------------+"><pre class="notranslate"><code> +-----------------------------------+ | bar_t1 bar_t3 | | | | | | v v | | asset 1--> [[1.1, 1.2, 1.3, ...], | | asset 2--> [ 5, 6, 7, ...]] | +-----------------------------------+ </code></pre></div> <p dir="auto">Example of LogReturns:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from spectre import factors import torch class LogReturns(factors.CustomFactor): inputs = [factors.Returns(2, inputs=[factors.OHLCV.close])] win = 1 def compute(self, change: torch.Tensor) -> torch.Tensor: return (change + 1).log()"><pre><span class="pl-k">from</span> <span class="pl-s1">spectre</span> <span class="pl-k">import</span> <span class="pl-s1">factors</span> <span class="pl-k">import</span> <span class="pl-s1">torch</span> <span class="pl-k">class</span> <span class="pl-v">LogReturns</span>(<span class="pl-s1">factors</span>.<span class="pl-c1">CustomFactor</span>): <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> [<span class="pl-s1">factors</span>.<span class="pl-c1">Returns</span>(<span class="pl-c1">2</span>, <span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-s1">factors</span>.<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>])] <span class="pl-s1">win</span> <span class="pl-c1">=</span> <span class="pl-c1">1</span> <span class="pl-k">def</span> <span class="pl-en">compute</span>(<span class="pl-s1">self</span>, <span class="pl-s1">change</span>: <span class="pl-s1">torch</span>.<span class="pl-c1">Tensor</span>) <span class="pl-c1">-></span> <span class="pl-s1">torch</span>.<span class="pl-c1">Tensor</span>: <span class="pl-k">return</span> (<span class="pl-s1">change</span> <span class="pl-c1">+</span> <span class="pl-c1">1</span>).<span class="pl-c1">log</span>()</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">win > 1</h3><a id="user-content-win--1-1" class="anchor" aria-label="Permalink: win > 1" href="#win--1-1"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If rolling window is required(<code>win > 1</code>), all <code>inputs</code> data will be wrapped into <code>spectre.parallel.Rolling</code>.</p> <p dir="auto">This is just an unfolded <code>tensor</code> data, but because the data is very large after unfolded, for better performance and saving VRAM, the rolling class automatically splits the data into multiple small chunks. You need to use the <code>agg</code> method to operating <code>tensor</code>.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from spectre import factors, parallel class OvernightReturn(factors.CustomFactor): inputs = [factors.OHLCV.open, factors.OHLCV.close] win = 2 def compute(self, opens: parallel.Rolling, closes: parallel.Rolling) -> torch.Tensor: ret = opens.last() / closes.first() - 1 return ret"><pre><span class="pl-k">from</span> <span class="pl-s1">spectre</span> <span class="pl-k">import</span> <span class="pl-s1">factors</span>, <span class="pl-s1">parallel</span> <span class="pl-k">class</span> <span class="pl-v">OvernightReturn</span>(<span class="pl-s1">factors</span>.<span class="pl-c1">CustomFactor</span>): <span class="pl-s1">inputs</span> <span class="pl-c1">=</span> [<span class="pl-s1">factors</span>.<span class="pl-c1">OHLCV</span>.<span class="pl-c1">open</span>, <span class="pl-s1">factors</span>.<span class="pl-c1">OHLCV</span>.<span class="pl-c1">close</span>] <span class="pl-s1">win</span> <span class="pl-c1">=</span> <span class="pl-c1">2</span> <span class="pl-k">def</span> <span class="pl-en">compute</span>(<span class="pl-s1">self</span>, <span class="pl-s1">opens</span>: <span class="pl-s1">parallel</span>.<span class="pl-c1">Rolling</span>, <span class="pl-s1">closes</span>: <span class="pl-s1">parallel</span>.<span class="pl-c1">Rolling</span>) <span class="pl-c1">-></span> <span class="pl-s1">torch</span>.<span class="pl-c1">Tensor</span>: <span class="pl-s1">ret</span> <span class="pl-c1">=</span> <span class="pl-s1">opens</span>.<span class="pl-c1">last</span>() <span class="pl-c1">/</span> <span class="pl-s1">closes</span>.<span class="pl-c1">first</span>() <span class="pl-c1">-</span> <span class="pl-c1">1</span> <span class="pl-k">return</span> <span class="pl-s1">ret</span></pre></div> <p dir="auto">The <code>closes.first()</code> above is just a helper method for <code>closes.agg(lambda x: x[:, :, 0])</code>, where <code>x[:, :, 0]</code> return the first element of rolling window. The first dimension of <code>x</code> is the asset, the second dimension is each bar, and the third dimension is the bar price and historical price with <code>win</code> length, and <code>Rolling.agg</code> runs on all the chunks and combines them.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content=" +------------------win=3-------------------+ | history_t-2 curr_bar_value | | | | | | v v | | asset 1-->[[[nan, nan, 1.1], <--bar_t1 | | [nan, 1.1, 1.2], <--bar_t2 | | [1.1, 1.2, 1.3]], <--bar_t3 | | | | asset 2--> [[nan, nan, 5], <--bar_t1 | | [nan, 5, 6], <--bar_t2 | | [ 5, 6, 7]]] <--bar_t3 | +------------------------------------------+"><pre class="notranslate"><code> +------------------win=3-------------------+ | history_t-2 curr_bar_value | | | | | | v v | | asset 1-->[[[nan, nan, 1.1], <--bar_t1 | | [nan, 1.1, 1.2], <--bar_t2 | | [1.1, 1.2, 1.3]], <--bar_t3 | | | | asset 2--> [[nan, nan, 5], <--bar_t1 | | [nan, 5, 6], <--bar_t2 | | [ 5, 6, 7]]] <--bar_t3 | +------------------------------------------+ </code></pre></div> <p dir="auto"><code>Rolling.agg</code> can carry multiple <code>Rolling</code> objects, such as</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="weighted_mean = lambda _close, _volume: (_close * _volume).sum(dim=2) / _volume.sum(dim=2) close.agg(weighted_mean, volume)"><pre><span class="pl-s1">weighted_mean</span> <span class="pl-c1">=</span> <span class="pl-k">lambda</span> <span class="pl-s1">_close</span>, <span class="pl-s1">_volume</span>: (<span class="pl-s1">_close</span> <span class="pl-c1">*</span> <span class="pl-s1">_volume</span>).<span class="pl-c1">sum</span>(<span class="pl-s1">dim</span><span class="pl-c1">=</span><span class="pl-c1">2</span>) <span class="pl-c1">/</span> <span class="pl-s1">_volume</span>.<span class="pl-c1">sum</span>(<span class="pl-s1">dim</span><span class="pl-c1">=</span><span class="pl-c1">2</span>) <span class="pl-s1">close</span>.<span class="pl-c1">agg</span>(<span class="pl-s1">weighted_mean</span>, <span class="pl-s1">volume</span>)</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Using Pandas Series</h3><a id="user-content-using-pandas-series" class="anchor" aria-label="Permalink: Using Pandas Series" href="#using-pandas-series"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">CustomFactor's inputs data is a matrix without DataFrame's Index information. If you need index, or not familiar with PyTorch, here is a another way:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from spectre import factors class YourFactor(factors.CustomFactor): def compute(self, data: torch.Tensor) -> torch.Tensor: # convert to pd.Series data pd_series = self._revert_to_series(data) # ... # convert back to grouped tensor return self._regroup(pd_series)"><pre><span class="pl-k">from</span> <span class="pl-s1">spectre</span> <span class="pl-k">import</span> <span class="pl-s1">factors</span> <span class="pl-k">class</span> <span class="pl-v">YourFactor</span>(<span class="pl-s1">factors</span>.<span class="pl-c1">CustomFactor</span>): <span class="pl-k">def</span> <span class="pl-en">compute</span>(<span class="pl-s1">self</span>, <span class="pl-s1">data</span>: <span class="pl-s1">torch</span>.<span class="pl-c1">Tensor</span>) <span class="pl-c1">-></span> <span class="pl-s1">torch</span>.<span class="pl-c1">Tensor</span>: <span class="pl-c"># convert to pd.Series data</span> <span class="pl-s1">pd_series</span> <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">_revert_to_series</span>(<span class="pl-s1">data</span>) <span class="pl-c"># ...</span> <span class="pl-c"># convert back to grouped tensor</span> <span class="pl-k">return</span> <span class="pl-s1">self</span>.<span class="pl-c1">_regroup</span>(<span class="pl-s1">pd_series</span>)</pre></div> <p dir="auto">This method is completely non-parallel and inefficient, but easy to write.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Back-testing</h2><a id="user-content-back-testing-1" class="anchor" aria-label="Permalink: Back-testing" href="#back-testing-1"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="#back-testing">Quick Start</a> contains easy-to-understand examples, please read first.</p> <p dir="auto">The <code>spectre.trading.CustomAlgorithm</code> currently does not supports live trading, will implement it in the future.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.initialize</h3><a id="user-content-customalgorithminitialize" class="anchor" aria-label="Permalink: CustomAlgorithm.initialize" href="#customalgorithminitialize"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>alg.initialize(self)</code> <strong>Callback</strong></p> <p dir="auto">Called when back-testing starts, at least you need use <code>get_factor_engine</code> to add factors and call <code>schedule_rebalance</code> here.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.terminate</h3><a id="user-content-customalgorithmterminate" class="anchor" aria-label="Permalink: CustomAlgorithm.terminate" href="#customalgorithmterminate"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>alg.terminate(self, records: pd.DataFrame)</code> <strong>Callback</strong></p> <p dir="auto">Called when back-testing ends.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">rebalance callback</h3><a id="user-content-rebalance-callback" class="anchor" aria-label="Permalink: rebalance callback" href="#rebalance-callback"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>rebalance(self, data: pd.DataFrame, history: pd.DataFrame)</code> <strong>Callback</strong></p> <p dir="auto">The function name does not have to be 'rebalance', it can be specified in <code>schedule_rebalance</code>. <code>data</code> is the factors data of last bar returned by <code>FactorEngine</code>; <code>history</code> same as <code>data</code>, but contains previous data, please refer to <code>set_history_window</code>.</p> <p dir="auto">Put calculations into the <code>FactorEngine</code> as much as possible can improve backtest performance.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.get_factor_engine</h3><a id="user-content-customalgorithmget_factor_engine" class="anchor" aria-label="Permalink: CustomAlgorithm.get_factor_engine" href="#customalgorithmget_factor_engine"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.get_factor_engine(name: str = None)</code> <strong>context:</strong> <em>initialize, rebalance, terminate</em></p> <p dir="auto">Get the factor engine of this trading algorithm. But note that you can add factors or filter only during <code>initialize</code>, otherwise it will cause unexpected effects.</p> <p dir="auto">The algorithm has a default engine, <code>name</code> can be None. But if you created multiple engines using <code>create_factor_engine</code>, you need to specify which one.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.create_factor_engine</h3><a id="user-content-customalgorithmcreate_factor_engine" class="anchor" aria-label="Permalink: CustomAlgorithm.create_factor_engine" href="#customalgorithmcreate_factor_engine"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.create_factor_engine(name: str, loader: DataLoader = None)</code> <strong>context:</strong> <em>initialize</em></p> <p dir="auto">Create another engine, generally used when you need multiple data sources.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.set_history_window</h3><a id="user-content-customalgorithmset_history_window" class="anchor" aria-label="Permalink: CustomAlgorithm.set_history_window" href="#customalgorithmset_history_window"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.set_history_window(offset: pd.DateOffset=None)</code> <strong>context:</strong> <em>initialize</em></p> <p dir="auto">Set the length of historical data passed to each <code>rebalance</code> call. <strong>SLOW</strong></p> <p dir="auto">Default: If None, pass all available historical data, so there will be no historical data on the first day, one historical row on the next day, and so on.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.schedule_rebalance</h3><a id="user-content-customalgorithmschedule_rebalance" class="anchor" aria-label="Permalink: CustomAlgorithm.schedule_rebalance" href="#customalgorithmschedule_rebalance"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.schedule_rebalance(event: Event)</code> <strong>context:</strong> <em>initialize</em></p> <p dir="auto">Schedule <code>rebalance</code> to be called when an event occurs. Events are: <code>MarketOpen</code>, <code>MarketClose</code>, <code>EveryBarData</code>, For example:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="alg.schedule_rebalance(trading.event.MarketClose(self.any_function))"><pre><span class="pl-s1">alg</span>.<span class="pl-c1">schedule_rebalance</span>(<span class="pl-s1">trading</span>.<span class="pl-c1">event</span>.<span class="pl-c1">MarketClose</span>(<span class="pl-s1">self</span>.<span class="pl-c1">any_function</span>))</pre></div> <p dir="auto">The <code>Market*</code> events has <code>offset_ns</code> parameter <code>MarketClose(self.any_function, offset_ns=-1000)</code>, a negative value of <code>offset_ns</code> means 'before', in backtest mode, the magnitude of the value has no effect.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.schedule</h3><a id="user-content-customalgorithmschedule" class="anchor" aria-label="Permalink: CustomAlgorithm.schedule" href="#customalgorithmschedule"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.schedule(event: Event)</code> <strong>context:</strong> <em>initialize</em></p> <p dir="auto">Schedule an event, callback is <code>callback(source: "Any class who fired this event")</code></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.empty_cache_after_run</h3><a id="user-content-customalgorithmempty_cache_after_run" class="anchor" aria-label="Permalink: CustomAlgorithm.empty_cache_after_run" href="#customalgorithmempty_cache_after_run"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.empty_cache_after_run = True</code> <strong>context:</strong> <em>initialize</em></p> <p dir="auto">Empty engine's cache after factor calculation. If you need more VRMA in rebalance context, or wanna play 3D game when backtesting, set it to True will help.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.stop_event_manager</h3><a id="user-content-customalgorithmstop_event_manager" class="anchor" aria-label="Permalink: CustomAlgorithm.stop_event_manager" href="#customalgorithmstop_event_manager"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>alg.stop_event_manager()</code> <strong>context:</strong> <em>all</em></p> <p dir="auto">Stop backtesting or live trading.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.fire_event</h3><a id="user-content-customalgorithmfire_event" class="anchor" aria-label="Permalink: CustomAlgorithm.fire_event" href="#customalgorithmfire_event"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>alg.fire_event(event_type: Type[Event])</code> <strong>context:</strong> <em>all</em></p> <p dir="auto">Trigger a type of event (any subclasses that inherit from <code>Event</code>）, for example: <code>alg.fire_event(MarketClose)</code>, (do not do this, do not fire built-in events)</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.results</h3><a id="user-content-customalgorithmresults" class="anchor" aria-label="Permalink: CustomAlgorithm.results" href="#customalgorithmresults"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.results</code> <strong>context:</strong> <em>terminate</em></p> <p dir="auto">Get back-test results, same as the return value of <a href="#spectretradingrun_backtest">trading.run_backtest</a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.plot</h3><a id="user-content-customalgorithmplot" class="anchor" aria-label="Permalink: CustomAlgorithm.plot" href="#customalgorithmplot"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.plot(annual_risk_free=0.04, benchmark: Union[pd.Series, str] = None)</code> <strong>context:</strong> <em>terminate</em></p> <p dir="auto">Plot a simple portfolio cumulative return chart.<br> <code>benchmark</code>: <code>pd.Series</code> of benchmark daily return, or an asset name.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.current</h3><a id="user-content-customalgorithmcurrent" class="anchor" aria-label="Permalink: CustomAlgorithm.current" href="#customalgorithmcurrent"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.current</code> <strong>context:</strong> <em>rebalance</em></p> <p dir="auto">Current datetime, Read-Only.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.get_price_matrix</h3><a id="user-content-customalgorithmget_price_matrix" class="anchor" aria-label="Permalink: CustomAlgorithm.get_price_matrix" href="#customalgorithmget_price_matrix"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.get_price_matrix(length: pd.DateOffset, name: str = None, prices=OHLCV.close)</code> <strong>context:</strong> <em>rebalance</em></p> <p dir="auto">Help method for calling <code>engine.get_price_matrix</code>, <code>name</code> specifies which engine.</p> <p dir="auto">Returns the historical asset prices, adjusted and filtered by the current time. <strong>Slow</strong></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">CustomAlgorithm.record</h3><a id="user-content-customalgorithmrecord" class="anchor" aria-label="Permalink: CustomAlgorithm.record" href="#customalgorithmrecord"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.record(**kwargs)</code> <strong>context:</strong> <em>rebalance</em></p> <p dir="auto">Record the data and pass all when calling <code>terminate</code>, use <code>column = value</code> format.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.set_commission</h3><a id="user-content-simulationblotterset_commission" class="anchor" aria-label="Permalink: SimulationBlotter.set_commission" href="#simulationblotterset_commission"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.blotter.set_commission(percentage=0, per_share=0.005, minimum=1)</code> <strong>context:</strong> <em>initialize</em></p> <p dir="auto">percentage: percentage part, calculated by <code>percentage * price * shares</code><br> per_share: calculated by <code>per_share * shares</code><br> minimum: minimum commission if above sum does not exceed</p> <p dir="auto">commission = max(percentage_part + per_share_part, minimum)</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.set_slippage</h3><a id="user-content-simulationblotterset_slippage" class="anchor" aria-label="Permalink: SimulationBlotter.set_slippage" href="#simulationblotterset_slippage"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.blotter.set_slippage(percentage=0, per_share=0.01)</code> <strong>context:</strong> <em>initialize, rebalance</em></p> <p dir="auto">Market impact add to the price.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.set_short_fee</h3><a id="user-content-simulationblotterset_short_fee" class="anchor" aria-label="Permalink: SimulationBlotter.set_short_fee" href="#simulationblotterset_short_fee"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.blotter.set_short_fee(percentage=0)</code> <strong>context:</strong> <em>initialize</em></p> <p dir="auto">Set the transaction fees which only charged for sell orders.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.daily_curb</h3><a id="user-content-simulationblotterdaily_curb" class="anchor" aria-label="Permalink: SimulationBlotter.daily_curb" href="#simulationblotterdaily_curb"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.blotter.daily_curb = float</code> <strong>context:</strong> <em>initialize, rebalance</em></p> <p dir="auto">Limit on trading a specific asset if today to previous day return >= ±value. <strong>SLOW</strong></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.order_target</h3><a id="user-content-simulationblotterorder_target" class="anchor" aria-label="Permalink: SimulationBlotter.order_target" href="#simulationblotterorder_target"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.blotter.order_target(asset: str, target: number)</code> <strong>context:</strong> <em>rebalance</em></p> <p dir="auto">Place an order on an asset to target number of shares in position, negative number means short.</p> <p dir="auto">If asset cannot be traded or limited by <code>daily_curb</code>, it will return False.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.batch_order_target</h3><a id="user-content-simulationblotterbatch_order_target" class="anchor" aria-label="Permalink: SimulationBlotter.batch_order_target" href="#simulationblotterbatch_order_target"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.blotter.batch_order_target(asset: Iterable[str], target: Iterable[float])</code> <strong>context:</strong> <em>rebalance</em></p> <p dir="auto">Same as <code>SimulationBlotter.order_target</code>, but for multiple assets.</p> <p dir="auto">Return value is a list of skipped assets, which indicate that they cannot be traded or limited by <code>daily_curb</code>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.order_target_percent</h3><a id="user-content-simulationblotterorder_target_percent" class="anchor" aria-label="Permalink: SimulationBlotter.order_target_percent" href="#simulationblotterorder_target_percent"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.blotter.order_target_percent(asset: str, pct: float)</code> <strong>context:</strong> <em>rebalance</em></p> <p dir="auto">Place an order on an asset to target percentage of portfolio net value, negative number means short.</p> <p dir="auto">If asset cannot be traded or limited by <code>daily_curb</code>, it will return False.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.batch_order_target_percent</h3><a id="user-content-simulationblotterbatch_order_target_percent" class="anchor" aria-label="Permalink: SimulationBlotter.batch_order_target_percent" href="#simulationblotterbatch_order_target_percent"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.blotter.batch_order_target_percent(asset: Iterable[str], pct: Iterable[float])</code> <strong>context:</strong> <em>rebalance</em></p> <p dir="auto">Same as <code>SimulationBlotter.order_target_percent</code>, but for multiple assets and better performance.</p> <p dir="auto">Return value is a list of skipped assets, which indicate that they cannot be traded or limited by <code>daily_curb</code>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.order</h3><a id="user-content-simulationblotterorder" class="anchor" aria-label="Permalink: SimulationBlotter.order" href="#simulationblotterorder"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.blotter.order(asset: str, amount: int)</code> <strong>context:</strong> <em>rebalance</em></p> <p dir="auto">Order a certain amount of an asset, negative number means short.</p> <p dir="auto">If asset cannot be traded or limited by <code>daily_curb</code>, it will return False.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.get_price</h3><a id="user-content-simulationblotterget_price" class="anchor" aria-label="Permalink: SimulationBlotter.get_price" href="#simulationblotterget_price"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>float = self.blotter.get_price(asset: Union[str, Iterable])</code> <strong>context:</strong> <em>rebalance</em></p> <p dir="auto">Get current price of assert. <em>Notice: Batch calls are slow, You can add prices as factor to get the price, like: <code>engine.add(OHLCV.close, 'prices')</code></em></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.portfolio.set_stop_model</h3><a id="user-content-simulationblotterportfolioset_stop_model" class="anchor" aria-label="Permalink: SimulationBlotter.portfolio.set_stop_model" href="#simulationblotterportfolioset_stop_model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.blotter.portfolio.set_stop_model(model: StopModel)</code> <strong>context:</strong> <em>initialize</em></p> <p dir="auto">Set stop tracking model for positions, models are:</p> <p dir="auto"><code>trading.StopModel(ratio, callback)</code><br> <code>trading.TrailingStopModel(ratio, callback)</code><br> <code>trading.PnLDecayTrailingStopModel</code> and <code>trading.TimeDecayTrailingStopModel</code></p> <p dir="auto">Stop loss example:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="class Backtester(trading.CustomAlgorithm): def initialize(self): ... self.blotter.portfolio.set_stop_model(trading.TrailingStopModel(-0.1, self.stop)) def stop(self, asset, amount): self.blotter.order(asset, amount) self.record(...) def rebalance(self, data, history): self.blotter.portfolio.check_stop_trigger() ..."><pre><span class="pl-k">class</span> <span class="pl-v">Backtester</span>(<span class="pl-s1">trading</span>.<span class="pl-c1">CustomAlgorithm</span>): <span class="pl-k">def</span> <span class="pl-en">initialize</span>(<span class="pl-s1">self</span>): ... <span class="pl-s1">self</span>.<span class="pl-c1">blotter</span>.<span class="pl-c1">portfolio</span>.<span class="pl-c1">set_stop_model</span>(<span class="pl-s1">trading</span>.<span class="pl-c1">TrailingStopModel</span>(<span class="pl-c1">-</span><span class="pl-c1">0.1</span>, <span class="pl-s1">self</span>.<span class="pl-c1">stop</span>)) <span class="pl-k">def</span> <span class="pl-en">stop</span>(<span class="pl-s1">self</span>, <span class="pl-s1">asset</span>, <span class="pl-s1">amount</span>): <span class="pl-s1">self</span>.<span class="pl-c1">blotter</span>.<span class="pl-c1">order</span>(<span class="pl-s1">asset</span>, <span class="pl-s1">amount</span>) <span class="pl-s1">self</span>.<span class="pl-c1">record</span>(...) <span class="pl-k">def</span> <span class="pl-en">rebalance</span>(<span class="pl-s1">self</span>, <span class="pl-s1">data</span>, <span class="pl-s1">history</span>): <span class="pl-s1">self</span>.<span class="pl-c1">blotter</span>.<span class="pl-c1">portfolio</span>.<span class="pl-c1">check_stop_trigger</span>() ...</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">PnLDecayTrailingStopModel</h4><a id="user-content-pnldecaytrailingstopmodel" class="anchor" aria-label="Permalink: PnLDecayTrailingStopModel" href="#pnldecaytrailingstopmodel"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>trading.PnLDecayTrailingStopModel(ratio, pnl_target, callback, decay_rate=0.05, max_decay=0)</code></p> <p dir="auto">This is a model that can stop gain and stop loss at the same time.</p> <p dir="auto">Exponential decay to the stop ratio: <code>ratio * decay_rate ^ (PnL% / PnL_target%)</code>, So <code>PnLDecayTrailingStopModel(-0.1, 0.1, callback)</code> means initial stop loss is -10%, and the <code>ratio</code> will decrease when profit% approaches the target +10%. If recorded high profit% exceeds 10%, any drawdown will trigger a stop loss.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">TimeDecayTrailingStopModel</h4><a id="user-content-timedecaytrailingstopmodel" class="anchor" aria-label="Permalink: TimeDecayTrailingStopModel" href="#timedecaytrailingstopmodel"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>trading.TimeDecayTrailingStopModel(ratio, period_target: pd.Timedelta, callback, decay_rate=0.05, max_decay=0)</code></p> <p dir="auto">Same as <code>PnLDecayTrailingStopModel</code>, but target is time period.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.get_returns</h3><a id="user-content-simulationblotterget_returns" class="anchor" aria-label="Permalink: SimulationBlotter.get_returns" href="#simulationblotterget_returns"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>self.blotter.get_returns()</code> <strong>context:</strong> <em>rebalance, terminate</em></p> <p dir="auto">Get the portfolio returns, use <code>(self.blotter.get_returns() + 1).prod()</code> to get current cumulative return.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SimulationBlotter.portfolio Read Only Properties</h3><a id="user-content-simulationblotterportfolio-read-only-properties" class="anchor" aria-label="Permalink: SimulationBlotter.portfolio Read Only Properties" href="#simulationblotterportfolio-read-only-properties"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>context:</strong> <em>rebalance, terminate</em></p> <p dir="auto"><code>self.blotter.portfolio.positions</code> Current positions, <code>Dict[asset, Position]</code> type.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="class Position: shares = None average_price = None last_price = None unrealized = None realized = None"><pre><span class="pl-k">class</span> <span class="pl-v">Position</span>: <span class="pl-s1">shares</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span> <span class="pl-s1">average_price</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span> <span class="pl-s1">last_price</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span> <span class="pl-s1">unrealized</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span> <span class="pl-s1">realized</span> <span class="pl-c1">=</span> <span class="pl-c1">None</span></pre></div> <p dir="auto"><code>self.blotter.portfolio.value</code> Current portfolio value</p> <p dir="auto"><code>self.blotter.portfolio.cash</code> Current portfolio cash</p> <p dir="auto"><code>self.blotter.portfolio.leverage</code> Current portfolio leverage</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">spectre.trading.run_backtest</h3><a id="user-content-spectretradingrun_backtest" class="anchor" aria-label="Permalink: spectre.trading.run_backtest" href="#spectretradingrun_backtest"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><code>results = trading.run_backtest(loader: DataLoader, alg_type: Type[CustomAlgorithm], start, end)</code></p> <p dir="auto">Run backtest, return value is namedtuple:</p> <p dir="auto"><strong>results.returns:</strong> daily return</p> <p dir="auto"><strong>results.positions:</strong> daily positions</p> <p dir="auto"><strong>results.transactions:</strong> full transactions with all orders</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Copyright & Thanks</h1><a id="user-content-copyright--thanks" class="anchor" aria-label="Permalink: Copyright & Thanks" href="#copyright--thanks"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Copyright (C) 2019-2020, by Zhang Jianhao (<a href="mailto:heeroz@gmail.com">heeroz@gmail.com</a>), All rights reserved.</p> <p dir="auto">Thanks to <a href="https://www.jetbrains.com/?from=spectre" rel="nofollow">JetBrains</a>'s support.</p> <hr> <blockquote> <p dir="auto"><em>A spectre is haunting Market — the spectre of capitalism.</em></p> </blockquote> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="O6xHdfNudKSa4GLgBi+IBLZ8HmxA5/J11E/o54VZKX7bwGGZwXgLU7IPK9qUHSeXHDuGgYV2dyU/g+RTnuZGOA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> GPU-accelerated Factors analysis library and Backtester </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/spectre" title="Topic: spectre" data-view-component="true" class="topic-tag topic-tag-link"> spectre </a> <a href="/topics/backtester" title="Topic: backtester" data-view-component="true" class="topic-tag topic-tag-link"> backtester </a> <a href="/topics/algorithmic-trading" title="Topic: algorithmic-trading" data-view-component="true" class="topic-tag topic-tag-link"> algorithmic-trading </a> <a href="/topics/factor-analysis" title="Topic: factor-analysis" data-view-component="true" class="topic-tag topic-tag-link"> factor-analysis </a> <a href="/topics/backtesting" title="Topic: backtesting" data-view-component="true" class="topic-tag topic-tag-link"> backtesting </a> <a href="/topics/quantitative-analysis" title="Topic: quantitative-analysis" data-view-component="true" class="topic-tag topic-tag-link"> 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class="mt-2"> <a href="/Heerozh/spectre/forks" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>113</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FHeerozh%2Fspectre&report=Heerozh+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" 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<div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">spectre v0.4-beta</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2020-01-13T19:31:45Z" class="no-wrap">Jan 13, 2020</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/Heerozh/spectre/releases" data-view-component="true" class="Link">+ 2 releases</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/users/Heerozh/packages?repo_name=spectre" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span></a></h2> <div 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