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<!doctype html><html lang="en"><head><title data-rh="true">Real-Time AI Stock Advisor with Ollama (Llama 3) & Streamlit | by Tapan Babbar | InsiderFinance Wire</title><meta data-rh="true" charset="utf-8"/><meta data-rh="true" name="viewport" content="width=device-width,minimum-scale=1,initial-scale=1,maximum-scale=1"/><meta data-rh="true" name="theme-color" content="#000000"/><meta data-rh="true" name="twitter:app:name:iphone" content="Medium"/><meta data-rh="true" name="twitter:app:id:iphone" content="828256236"/><meta data-rh="true" property="al:ios:app_name" content="Medium"/><meta data-rh="true" property="al:ios:app_store_id" content="828256236"/><meta data-rh="true" property="al:android:package" content="com.medium.reader"/><meta data-rh="true" property="fb:app_id" content="542599432471018"/><meta data-rh="true" property="og:site_name" content="Medium"/><meta data-rh="true" property="og:type" content="article"/><meta data-rh="true" property="article:published_time" content="2024-10-12T12:30:34.380Z"/><meta data-rh="true" name="title" content="Real-Time AI Stock Advisor with Ollama (Llama 3) & Streamlit | by Tapan Babbar | InsiderFinance Wire"/><meta data-rh="true" property="og:title" content="Real-Time AI Stock Advisor with Ollama (Llama 3) & Streamlit"/><meta data-rh="true" property="al:android:url" content="medium://p/c8ce727c236f"/><meta data-rh="true" property="al:ios:url" content="medium://p/c8ce727c236f"/><meta data-rh="true" property="al:android:app_name" content="Medium"/><meta data-rh="true" name="description" content="While working on stock data visualizations for a fintech project, I wondered: what if the data could explain itself in plain English? 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class="ab cb"><div class="ci bh fz ga gb gc"><div><h1 id="d39a" class="pw-post-title gs gt gu bf gv gw gx gy gz ha hb hc hd he hf hg hh hi hj hk hl hm hn ho hp hq bk" data-testid="storyTitle">Real-Time AI Stock Advisor with Ollama (Llama 3) & Streamlit</h1></div><div><h2 id="5fb2" class="pw-subtitle-paragraph hr gt gu bf b hs ht hu hv hw hx hy hz ia ib ic id ie if ig cq du">This project uses LLM-powered insights to fetch stock data every minute, analyze trends, and provide real-time, easy-to-understand explanations.</h2><div><div class="speechify-ignore ab cp"><div class="speechify-ignore bh l"><div class="ih ii ij ik il ab"><div><div class="ab im"><div><div class="bm" aria-hidden="false"><a href="https://medium.com/@tapanbabbar?source=post_page---byline--c8ce727c236f--------------------------------" rel="noopener follow"><div class="l in io by ip iq"><div class="l fj"><img alt="Tapan Babbar" class="l fd by dd de cx" 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nl"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*OlgoaLUgCClqhFTOcVj-aQ.gif 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*OlgoaLUgCClqhFTOcVj-aQ.gif 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*OlgoaLUgCClqhFTOcVj-aQ.gif 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*OlgoaLUgCClqhFTOcVj-aQ.gif 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*OlgoaLUgCClqhFTOcVj-aQ.gif 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*OlgoaLUgCClqhFTOcVj-aQ.gif 1100w, https://miro.medium.com/v2/resize:fit:952/format:webp/1*OlgoaLUgCClqhFTOcVj-aQ.gif 952w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 476px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*OlgoaLUgCClqhFTOcVj-aQ.gif 640w, https://miro.medium.com/v2/resize:fit:720/1*OlgoaLUgCClqhFTOcVj-aQ.gif 720w, https://miro.medium.com/v2/resize:fit:750/1*OlgoaLUgCClqhFTOcVj-aQ.gif 750w, https://miro.medium.com/v2/resize:fit:786/1*OlgoaLUgCClqhFTOcVj-aQ.gif 786w, https://miro.medium.com/v2/resize:fit:828/1*OlgoaLUgCClqhFTOcVj-aQ.gif 828w, https://miro.medium.com/v2/resize:fit:1100/1*OlgoaLUgCClqhFTOcVj-aQ.gif 1100w, https://miro.medium.com/v2/resize:fit:952/1*OlgoaLUgCClqhFTOcVj-aQ.gif 952w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 476px"/><img alt="" class="bh mq ns c" width="476" height="223" loading="eager" role="presentation"/></picture></div></figure><p id="4a8c" class="pw-post-body-paragraph nt nu gu nv b hs nw nx ny hv nz oa ob oc od oe of og oh oi oj ok ol om on oo gn bk">While working on stock data visualizations for a fintech project, I wondered: what if the data could explain itself in plain English? That’s how this side quest began. Using LLM-powered (Llama 3) insights, this script fetches stock data every minute, analyzes trends, and delivers straightforward explanations. No more squinting at charts — it’s like having a personal stock expert breaking down what’s happening in real-time.</p><h2 id="cfd7" class="op oq gu bf or os ot dy ou ov ow ea ox oc oy oz pa og pb pc pd ok pe pf pg ph bk">Step 1: Setting Up the Environment</h2><p id="6879" class="pw-post-body-paragraph nt nu gu nv b hs pi nx ny hv pj oa ob oc pk oe of og pl oi oj ok pm om on oo gn bk">Before we start writing code, you need to install a few key libraries that will make this project possible. We’ll be using <code class="cx pn po pp pq b">Streamlit</code> for the UI, <code class="cx pn po pp pq b">yfinance</code> to fetch stock data, and <code class="cx pn po pp pq b">Ollama</code> to generate natural language insights.</p><pre class="nm nn no np nq pr pq ps bp pt bb bk"><span id="fda3" class="pu oq gu pq b bg pv pw l px py">import streamlit as st<br/>import yfinance as yf<br/>import pandas as pd<br/>import schedule<br/>import time<br/>import ollama<br/>from datetime import datetime, timedelta</span></pre><h2 id="f5de" class="op oq gu bf or os ot dy ou ov ow ea ox oc oy oz pa og pb pc pd ok pe pf pg ph bk">Step 2: Fetch Stock Data for Analysis</h2><p id="3af1" class="pw-post-body-paragraph nt nu gu nv b hs pi nx ny hv pj oa ob oc pk oe of og pl oi oj ok pm om on oo gn bk">Next, we fetch historical stock data for both <strong class="nv gv">Apple (AAPL)</strong> and the <strong class="nv gv">Dow Jones Index (DJI)</strong>. We’ll use <code class="cx pn po pp pq b">yfinance</code> to get stock data for the previous day in 1-minute intervals.</p><pre class="nm nn no np nq pr pq ps bp pt bb bk"><span id="fa62" class="pu oq gu pq b bg pv pw l px py"># Fetching historical data for Apple (AAPL) and Dow Jones (DJI)<br/>stock = yf.Ticker("AAPL")<br/>dow_jones = yf.Ticker("^DJI")<br/>data = stock.history(period="1d", interval="1m")<br/>dow_data = dow_jones.history(period="1d", interval="1m")</span></pre><figure class="nm nn no np nq nr nj nk paragraph-image"><div role="button" tabindex="0" class="qa qb fj qc bh qd"><div class="nj nk pz"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/0*SkONptsXd2ekziFa.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/0*SkONptsXd2ekziFa.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/0*SkONptsXd2ekziFa.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/0*SkONptsXd2ekziFa.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/0*SkONptsXd2ekziFa.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/0*SkONptsXd2ekziFa.png 1100w, https://miro.medium.com/v2/resize:fit:1400/format:webp/0*SkONptsXd2ekziFa.png 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/0*SkONptsXd2ekziFa.png 640w, https://miro.medium.com/v2/resize:fit:720/0*SkONptsXd2ekziFa.png 720w, https://miro.medium.com/v2/resize:fit:750/0*SkONptsXd2ekziFa.png 750w, https://miro.medium.com/v2/resize:fit:786/0*SkONptsXd2ekziFa.png 786w, https://miro.medium.com/v2/resize:fit:828/0*SkONptsXd2ekziFa.png 828w, https://miro.medium.com/v2/resize:fit:1100/0*SkONptsXd2ekziFa.png 1100w, https://miro.medium.com/v2/resize:fit:1400/0*SkONptsXd2ekziFa.png 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px"/><img alt="" class="bh mq ns c" width="700" height="317" loading="lazy" role="presentation"/></picture></div></div></figure><p id="a879" class="pw-post-body-paragraph nt nu gu nv b hs nw nx ny hv nz oa ob oc od oe of og oh oi oj ok ol om on oo gn bk">At this stage, we have the stock data, and we’ll be using it for further analysis. For demonstration purposes, I use data from the last trading day. <strong class="nv gv">You can easily tweak this to pull real-time stock data directly from the API to process live market updates.</strong></p><h2 id="f909" class="op oq gu bf or os ot dy ou ov ow ea ox oc oy oz pa og pb pc pd ok pe pf pg ph bk">Step 3: Process Real-Time Stock Updates</h2><p id="2591" class="pw-post-body-paragraph nt nu gu nv b hs pi nx ny hv pj oa ob oc pk oe of og pl oi oj ok pm om on oo gn bk">Now, let’s simulate real-time updates by processing one stock data point per minute. We’ll calculate metrics like rolling averages and momentum to understand market trends.</p><pre class="nm nn no np nq pr pq ps bp pt bb bk"><span id="e8a6" class="pu oq gu pq b bg pv pw l px py"># Global variables to store rolling data<br/>rolling_window = pd.DataFrame()<br/>dow_rolling_window = pd.DataFrame()<br/>daily_high = float('-inf')<br/>daily_low = float('inf')<br/>buying_momentum = 0<br/>selling_momentum = 0<br/><br/># Function to process a new stock update every minute<br/>def process_stock_update():<br/> global rolling_window, data, dow_rolling_window, dow_data<br/> global daily_high, daily_low, buying_momentum, selling_momentum<br/><br/> if not data.empty and not dow_data.empty:<br/> # Simulate receiving a new data point for AAPL and Dow Jones<br/> update = data.iloc[0].to_frame().T<br/> dow_update = dow_data.iloc[0].to_frame().T<br/> data = data.iloc[1:] # Remove the processed row<br/> dow_data = dow_data.iloc[1:]<br/><br/> # Append the new data points to the rolling windows<br/> rolling_window = pd.concat([rolling_window, update], ignore_index=False)<br/> dow_rolling_window = pd.concat([dow_rolling_window, dow_update], ignore_index=False)<br/><br/> # Update daily high and low<br/> daily_high = max(daily_high, update['Close'].values[0])<br/> daily_low = min(daily_low, update['Close'].values[0])<br/><br/> # Calculate momentum<br/> if len(rolling_window) >= 2:<br/> price_change = update['Close'].values[0] - rolling_window['Close'].iloc[-2]<br/> if price_change > 0:<br/> buying_momentum += price_change<br/> else:<br/> selling_momentum += abs(price_change)</span></pre><p id="0cc1" class="pw-post-body-paragraph nt nu gu nv b hs nw nx ny hv nz oa ob oc od oe of og oh oi oj ok ol om on oo gn bk">Each new data point gets added to a rolling window for a 5-minute analysis. Daily high/low values and price momentum are updated based on changes between consecutive data points.</p><h2 id="955d" class="op oq gu bf or os ot dy ou ov ow ea ox oc oy oz pa og pb pc pd ok pe pf pg ph bk">Step 4: Analyzing the Stock Trends</h2><p id="ca07" class="pw-post-body-paragraph nt nu gu nv b hs pi nx ny hv pj oa ob oc pk oe of og pl oi oj ok pm om on oo gn bk">To make sense of the real-time data, we calculate moving averages, price changes, volume changes, and technical indicators like <strong class="nv gv">Exponential Moving Average (EMA)</strong>, <strong class="nv gv">Bollinger Bands, </strong>and <strong class="nv gv">RSI (Relative Strength Index)</strong>. Here’s how these indicators work:</p><ul class=""><li id="3306" class="nt nu gu nv b hs nw nx ny hv nz oa ob oc od oe of og oh oi oj ok ol om on oo qe qf qg bk"><a class="af qh" href="https://www.investopedia.com/terms/e/ema.asp" rel="noopener ugc nofollow" target="_blank"><strong class="nv gv">Exponential Moving Average (EMA):</strong></a><strong class="nv gv"> </strong>Puts more weight on recent prices to identify short-term trends.</li><li id="4e81" class="nt nu gu nv b hs qi nx ny hv qj oa ob oc qk oe of og ql oi oj ok qm om on oo qe qf qg bk"><a class="af qh" href="https://www.investopedia.com/terms/r/rsi.asp" rel="noopener ugc nofollow" target="_blank"><strong class="nv gv">Relative Strength Index (RSI):</strong></a><strong class="nv gv"> </strong>Measures price movement speed and oscillates between 0 and 100 to identify overbought/oversold conditions.</li><li id="a7fd" class="nt nu gu nv b hs qi nx ny hv qj oa ob oc qk oe of og ql oi oj ok qm om on oo qe qf qg bk"><a class="af qh" href="https://www.investopedia.com/terms/b/bollingerbands.asp" rel="noopener ugc nofollow" target="_blank"><strong class="nv gv">Bollinger Bands:</strong></a> Help assess market volatility with upper and lower bands around the moving average.</li></ul><pre class="nm nn no np nq pr pq ps bp pt bb bk"><span id="e02d" class="pu oq gu pq b bg pv pw l px py">def calculate_insights(window, dow_window):<br/> if len(window) >= 5:<br/> # 5-minute rolling average<br/> rolling_avg = window['Close'].rolling(window=5).mean().iloc[-1]<br/> <br/> # Exponential Moving Average (EMA)<br/> ema = window['Close'].ewm(span=5, adjust=False).mean().iloc[-1]<br/> <br/> # Bollinger Bands (using a 5-period window)<br/> std = window['Close'].rolling(window=5).std().iloc[-1]<br/> bollinger_upper = rolling_avg + (2 * std)<br/> bollinger_lower = rolling_avg - (2 * std)<br/><br/> # RSI calculation<br/> delta = window['Close'].diff()<br/> gain = delta.where(delta > 0, 0)<br/> loss = -delta.where(delta < 0, 0)<br/> avg_gain = gain.rolling(window=14, min_periods=1).mean().iloc[-1]<br/> avg_loss = loss.rolling(window=14, min_periods=1).mean().iloc[-1]<br/> rs = avg_gain / avg_loss if avg_loss != 0 else float('nan')<br/> rsi = 100 - (100 / (1 + rs))<br/><br/> # Calculate Relative Strength Index (RSI) if there are enough periods (14 is typical)<br/> delta = window['Close'].diff()<br/> gain = delta.where(delta > 0, 0)<br/> loss = -delta.where(delta < 0, 0)<br/> avg_gain = gain.rolling(window=14, min_periods=1).mean().iloc[-1]<br/> avg_loss = loss.rolling(window=14, min_periods=1).mean().iloc[-1]<br/> rs = avg_gain / avg_loss if avg_loss != 0 else float('nan')<br/> rsi = 100 - (100 / (1 + rs))<br/><br/> # Calculate Dow Jones index rolling average<br/> dow_rolling_avg = dow_window['Close'].rolling(window=5).mean().iloc[-1]<br/> <br/> market_open_duration = get_market_open_duration(window)<br/> if int(market_open_duration) % 5 == 0: # Trigger LLM every 5 minutes<br/> get_natural_language_insights(<br/> rolling_avg, ema, rsi, bollinger_upper, bollinger_lower,<br/> price_change, volume_change, dow_rolling_avg, market_open_duration, <br/> dow_price_change, dow_volume_change, daily_high, daily_low, <br/> buying_momentum, selling_momentum<br/> )</span></pre><figure class="nm nn no np nq nr nj nk paragraph-image"><div role="button" tabindex="0" class="qa qb fj qc bh qd"><div class="nj nk qn"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/0*gA3OREfypUFbcCjp.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/0*gA3OREfypUFbcCjp.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/0*gA3OREfypUFbcCjp.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/0*gA3OREfypUFbcCjp.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/0*gA3OREfypUFbcCjp.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/0*gA3OREfypUFbcCjp.png 1100w, https://miro.medium.com/v2/resize:fit:1400/format:webp/0*gA3OREfypUFbcCjp.png 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/0*gA3OREfypUFbcCjp.png 640w, https://miro.medium.com/v2/resize:fit:720/0*gA3OREfypUFbcCjp.png 720w, https://miro.medium.com/v2/resize:fit:750/0*gA3OREfypUFbcCjp.png 750w, https://miro.medium.com/v2/resize:fit:786/0*gA3OREfypUFbcCjp.png 786w, https://miro.medium.com/v2/resize:fit:828/0*gA3OREfypUFbcCjp.png 828w, https://miro.medium.com/v2/resize:fit:1100/0*gA3OREfypUFbcCjp.png 1100w, https://miro.medium.com/v2/resize:fit:1400/0*gA3OREfypUFbcCjp.png 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px"/><img alt="" class="bh mq ns c" width="700" height="299" loading="lazy" role="presentation"/></picture></div></div></figure><h2 id="8102" class="op oq gu bf or os ot dy ou ov ow ea ox oc oy oz pa og pb pc pd ok pe pf pg ph bk">Step 5: Getting Natural Language Insights Using Ollama</h2><p id="c428" class="pw-post-body-paragraph nt nu gu nv b hs pi nx ny hv pj oa ob oc pk oe of og pl oi oj ok pm om on oo gn bk">To make this project more interactive, we can integrate <strong class="nv gv">Llama 3</strong> model to generate natural language insights every 5 minutes.</p><pre class="nm nn no np nq pr pq ps bp pt bb bk"><span id="2498" class="pu oq gu pq b bg pv pw l px py">def get_natural_language_insights(<br/> rolling_avg, ema, rsi, bollinger_upper, bollinger_lower,<br/> price_change, volume_change, dow_rolling_avg, market_open_duration, dow_price_change, dow_volume_change, <br/> daily_high, daily_low, buying_momentum, selling_momentum, timestamp<br/>):<br/> prompt = f"""<br/> You are a professional stock broker. Apple's stock has a 5-minute rolling average of {rolling_avg:.2f}.<br/> The Exponential Moving Average (EMA) is {ema:.2f}, and the Relative Strength Index (RSI) is {rsi:.2f}.<br/> The Bollinger Bands are set with an upper band of {bollinger_upper:.2f} and a lower band of {bollinger_lower:.2f}.<br/> The price has changed by {price_change:.2f}, and the volume has shifted by {volume_change}.<br/> The DOW price has changed by {dow_price_change:.2f}, and the volume has shifted by {dow_volume_change}.<br/> Meanwhile, the Dow Jones index has a 5-minute rolling average of {dow_rolling_avg:.2f}.<br/> The market has been open for {market_open_duration:.2f} minutes.<br/> Today's high was {daily_high:.2f} and low was {daily_low:.2f}.<br/> The buying momentum is {buying_momentum:.2f} and selling momentum is {selling_momentum:.2f}.<br/> Based on this data, provide insights into the current stock trend and the general market sentiment.<br/> The insights should not be longer than 100 words and should not have an introduction.<br/> """<br/> response = ollama.chat(<br/> model="llama3",<br/> messages=[{"role": "user", "content": prompt}]<br/> )<br/> response_text = response['message']['content'].strip()<br/> print("Natural Language Insight:", response_text)</span></pre><p id="1967" class="pw-post-body-paragraph nt nu gu nv b hs nw nx ny hv nz oa ob oc od oe of og oh oi oj ok ol om on oo gn bk">This function summarizes the stock performance in plain English using technical indicators and trends from the previous 5 minutes.</p><h2 id="c0b0" class="op oq gu bf or os ot dy ou ov ow ea ox oc oy oz pa og pb pc pd ok pe pf pg ph bk">Step 6: Setting up the Streamlit UI</h2><p id="52a8" class="pw-post-body-paragraph nt nu gu nv b hs pi nx ny hv pj oa ob oc pk oe of og pl oi oj ok pm om on oo gn bk">Now let’s set up a basic UI with <strong class="nv gv">Streamlit</strong> to display stock updates and insights in real time.</p><pre class="nm nn no np nq pr pq ps bp pt bb bk"><span id="f353" class="pu oq gu pq b bg pv pw l px py">message = st.chat_message("assistant")<br/>message.write(timestamp)<br/>message.write(response_text)</span></pre><pre class="qo pr pq ps bp pt bb bk"><span id="656a" class="pu oq gu pq b bg pv pw l px py">streamlit run app.py</span></pre><figure class="nm nn no np nq nr nj nk paragraph-image"><div role="button" tabindex="0" class="qa qb fj qc bh qd"><div class="nj nk pz"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/0*R8YN0Od20TVNYO6e.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/0*R8YN0Od20TVNYO6e.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/0*R8YN0Od20TVNYO6e.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/0*R8YN0Od20TVNYO6e.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/0*R8YN0Od20TVNYO6e.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/0*R8YN0Od20TVNYO6e.png 1100w, https://miro.medium.com/v2/resize:fit:1400/format:webp/0*R8YN0Od20TVNYO6e.png 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/0*R8YN0Od20TVNYO6e.png 640w, https://miro.medium.com/v2/resize:fit:720/0*R8YN0Od20TVNYO6e.png 720w, https://miro.medium.com/v2/resize:fit:750/0*R8YN0Od20TVNYO6e.png 750w, https://miro.medium.com/v2/resize:fit:786/0*R8YN0Od20TVNYO6e.png 786w, https://miro.medium.com/v2/resize:fit:828/0*R8YN0Od20TVNYO6e.png 828w, https://miro.medium.com/v2/resize:fit:1100/0*R8YN0Od20TVNYO6e.png 1100w, https://miro.medium.com/v2/resize:fit:1400/0*R8YN0Od20TVNYO6e.png 1400w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px"/><img alt="" class="bh mq ns c" width="700" height="760" loading="lazy" role="presentation"/></picture></div></div></figure><p id="b245" class="pw-post-body-paragraph nt nu gu nv b hs nw nx ny hv nz oa ob oc od oe of og oh oi oj ok ol om on oo gn bk">And that’s how you can build your very own real-time stock advisor!</p><h2 id="5978" class="op oq gu bf or os ot dy ou ov ow ea ox oc oy oz pa og pb pc pd ok pe pf pg ph bk">Improvements and Future Enhancements</h2><p id="d421" class="pw-post-body-paragraph nt nu gu nv b hs pi nx ny hv pj oa ob oc pk oe of og pl oi oj ok pm om on oo gn bk">This project is a prototype focused on trends from the last trading day. You can enhance it by incorporating historical data, news integration, or advanced models like time series neural networks. Here are some ideas for improvement:</p><ol class=""><li id="200c" class="nt nu gu nv b hs nw nx ny hv nz oa ob oc od oe of og oh oi oj ok ol om on oo qp qf qg bk"><strong class="nv gv">Historical Data Integration</strong>: Currently, the app processes only the previous trading day’s data. You can enhance it by integrating a rolling window of historical data over weeks or months.</li><li id="b811" class="nt nu gu nv b hs qi nx ny hv qj oa ob oc qk oe of og ql oi oj ok qm om on oo qp qf qg bk"><strong class="nv gv">News Integration</strong>: Stock trends are often influenced by news events. Adding a real-time news feed could provide better context.</li><li id="a20d" class="nt nu gu nv b hs qi nx ny hv qj oa ob oc qk oe of og ql oi oj ok qm om on oo qp qf qg bk"><strong class="nv gv">Advanced Time Series Forecasting</strong>: You could further improve insights by incorporating time-series neural networks for more accurate trend predictions.</li><li id="713d" class="nt nu gu nv b hs qi nx ny hv qj oa ob oc qk oe of og ql oi oj ok qm om on oo qp qf qg bk"><strong class="nv gv">Custom Alerts</strong>: Implementing customizable alerts based on significant stock movements or indicators like RSI could make this app more practical for day traders.</li></ol></div></div></div><div class="ab cb qq qr qs qt" role="separator"><span class="qu by bm qv qw qx"></span><span class="qu by bm qv qw qx"></span><span class="qu by bm qv qw"></span></div><div class="gn go gp gq gr"><div class="ab cb"><div class="ci bh fz ga gb gc"><p id="f797" class="pw-post-body-paragraph nt nu gu nv b hs nw nx ny hv nz oa ob oc od oe of og oh oi oj ok ol om on oo gn bk">The full source code and the Jupyter Notebook are available in the <a class="af qh" href="https://github.com/tapanBabbar9/llama3/tree/main/stock-trader" rel="noopener ugc nofollow" target="_blank">GitHub repository</a>. Feel free to reach out if you have ideas for optimizing prompts or other improvements.</p><figure class="nm nn no np nq nr nj nk paragraph-image"><div class="nj nk qy"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 1100w, https://miro.medium.com/v2/resize:fit:960/format:webp/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 960w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 480px" type="image/webp"/><source data-testid="og" srcSet="https://miro.medium.com/v2/resize:fit:640/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 640w, https://miro.medium.com/v2/resize:fit:720/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 720w, https://miro.medium.com/v2/resize:fit:750/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 750w, https://miro.medium.com/v2/resize:fit:786/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 786w, https://miro.medium.com/v2/resize:fit:828/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 828w, https://miro.medium.com/v2/resize:fit:1100/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 1100w, https://miro.medium.com/v2/resize:fit:960/1*mD5ZyPIE4wi_zZenb0Rbbw.gif 960w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 480px"/><img alt="" class="bh mq ns c" width="480" height="270" loading="eager" role="presentation"/></picture></div></figure></div></div></div><div class="ab cb qq qr qs qt" role="separator"><span class="qu by bm qv qw qx"></span><span class="qu by bm qv qw qx"></span><span class="qu by bm qv qw"></span></div><div class="gn go gp gq gr"><div class="ab cb"><div class="ci bh fz ga gb gc"><h2 id="fe71" class="op oq gu bf or os ot dy ou ov ow ea ox oc oy oz pa og pb pc pd ok pe pf pg ph bk">A Message from InsiderFinance</h2><figure class="nm nn no np nq nr nj nk paragraph-image"><div class="nj nk qz"><picture><source srcSet="https://miro.medium.com/v2/resize:fit:640/format:webp/0*10x5_2smmKq8oIlf.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/0*10x5_2smmKq8oIlf.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/0*10x5_2smmKq8oIlf.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/0*10x5_2smmKq8oIlf.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/0*10x5_2smmKq8oIlf.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/0*10x5_2smmKq8oIlf.png 1100w, https://miro.medium.com/v2/resize:fit:602/format:webp/0*10x5_2smmKq8oIlf.png 602w" sizes="(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) 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That’s how this side quest began. Using LLM-powered (Llama 3) insights, this script fetches stock data every minute, analyzes trends, and delivers straightforward explanations. No more squinting at charts — it’s like having a personal stock expert breaking down what’s happening in real-time.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_4":{"__typename":"Paragraph","id":"37fcbf16f7ba_4","name":"cfd7","type":"H4","href":null,"layout":null,"metadata":null,"text":"Step 1: Setting Up the Environment","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_5":{"__typename":"Paragraph","id":"37fcbf16f7ba_5","name":"6879","type":"P","href":null,"layout":null,"metadata":null,"text":"Before we start writing code, you need to install a few key libraries that will make this project possible. We’ll be using Streamlit for the UI, yfinance to fetch stock data, and Ollama to generate natural language insights.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"CODE","start":123,"end":132,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"CODE","start":145,"end":153,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"CODE","start":179,"end":185,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_6":{"__typename":"Paragraph","id":"37fcbf16f7ba_6","name":"fda3","type":"PRE","href":null,"layout":null,"metadata":null,"text":"import streamlit as st\nimport yfinance as yf\nimport pandas as pd\nimport schedule\nimport time\nimport ollama\nfrom datetime import datetime, timedelta","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"EXPLICIT","lang":"python"},"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_7":{"__typename":"Paragraph","id":"37fcbf16f7ba_7","name":"f5de","type":"H4","href":null,"layout":null,"metadata":null,"text":"Step 2: Fetch Stock Data for Analysis","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_8":{"__typename":"Paragraph","id":"37fcbf16f7ba_8","name":"3af1","type":"P","href":null,"layout":null,"metadata":null,"text":"Next, we fetch historical stock data for both Apple (AAPL) and the Dow Jones Index (DJI). We’ll use yfinance to get stock data for the previous day in 1-minute intervals.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"CODE","start":100,"end":108,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"STRONG","start":46,"end":58,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"STRONG","start":67,"end":88,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_9":{"__typename":"Paragraph","id":"37fcbf16f7ba_9","name":"fa62","type":"PRE","href":null,"layout":null,"metadata":null,"text":"# Fetching historical data for Apple (AAPL) and Dow Jones (DJI)\nstock = yf.Ticker(\"AAPL\")\ndow_jones = yf.Ticker(\"^DJI\")\ndata = stock.history(period=\"1d\", interval=\"1m\")\ndow_data = dow_jones.history(period=\"1d\", interval=\"1m\")","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"EXPLICIT","lang":"python"},"iframe":null,"mixtapeMetadata":null},"ImageMetadata:0*SkONptsXd2ekziFa.png":{"__typename":"ImageMetadata","id":"0*SkONptsXd2ekziFa.png","originalHeight":634,"originalWidth":1400,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:37fcbf16f7ba_10":{"__typename":"Paragraph","id":"37fcbf16f7ba_10","name":"e2ff","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:0*SkONptsXd2ekziFa.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_11":{"__typename":"Paragraph","id":"37fcbf16f7ba_11","name":"a879","type":"P","href":null,"layout":null,"metadata":null,"text":"At this stage, we have the stock data, and we’ll be using it for further analysis. For demonstration purposes, I use data from the last trading day. You can easily tweak this to pull real-time stock data directly from the API to process live market updates.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":149,"end":257,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_12":{"__typename":"Paragraph","id":"37fcbf16f7ba_12","name":"f909","type":"H4","href":null,"layout":null,"metadata":null,"text":"Step 3: Process Real-Time Stock Updates","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_13":{"__typename":"Paragraph","id":"37fcbf16f7ba_13","name":"2591","type":"P","href":null,"layout":null,"metadata":null,"text":"Now, let’s simulate real-time updates by processing one stock data point per minute. We’ll calculate metrics like rolling averages and momentum to understand market trends.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_14":{"__typename":"Paragraph","id":"37fcbf16f7ba_14","name":"e8a6","type":"PRE","href":null,"layout":null,"metadata":null,"text":"# Global variables to store rolling data\nrolling_window = pd.DataFrame()\ndow_rolling_window = pd.DataFrame()\ndaily_high = float('-inf')\ndaily_low = float('inf')\nbuying_momentum = 0\nselling_momentum = 0\n\n# Function to process a new stock update every minute\ndef process_stock_update():\n global rolling_window, data, dow_rolling_window, dow_data\n global daily_high, daily_low, buying_momentum, selling_momentum\n\n if not data.empty and not dow_data.empty:\n # Simulate receiving a new data point for AAPL and Dow Jones\n update = data.iloc[0].to_frame().T\n dow_update = dow_data.iloc[0].to_frame().T\n data = data.iloc[1:] # Remove the processed row\n dow_data = dow_data.iloc[1:]\n\n # Append the new data points to the rolling windows\n rolling_window = pd.concat([rolling_window, update], ignore_index=False)\n dow_rolling_window = pd.concat([dow_rolling_window, dow_update], ignore_index=False)\n\n # Update daily high and low\n daily_high = max(daily_high, update['Close'].values[0])\n daily_low = min(daily_low, update['Close'].values[0])\n\n # Calculate momentum\n if len(rolling_window) \u003E= 2:\n price_change = update['Close'].values[0] - rolling_window['Close'].iloc[-2]\n if price_change \u003E 0:\n buying_momentum += price_change\n else:\n selling_momentum += abs(price_change)","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"AUTO","lang":"python"},"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_15":{"__typename":"Paragraph","id":"37fcbf16f7ba_15","name":"0cc1","type":"P","href":null,"layout":null,"metadata":null,"text":"Each new data point gets added to a rolling window for a 5-minute analysis. Daily high\u002Flow values and price momentum are updated based on changes between consecutive data points.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_16":{"__typename":"Paragraph","id":"37fcbf16f7ba_16","name":"955d","type":"H4","href":null,"layout":null,"metadata":null,"text":"Step 4: Analyzing the Stock Trends","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_17":{"__typename":"Paragraph","id":"37fcbf16f7ba_17","name":"ca07","type":"P","href":null,"layout":null,"metadata":null,"text":"To make sense of the real-time data, we calculate moving averages, price changes, volume changes, and technical indicators like Exponential Moving Average (EMA), Bollinger Bands, and RSI (Relative Strength Index). Here’s how these indicators work:","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":128,"end":160,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"STRONG","start":162,"end":179,"href":null,"anchorType":null,"userId":null,"linkMetadata":null},{"__typename":"Markup","type":"STRONG","start":183,"end":212,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_18":{"__typename":"Paragraph","id":"37fcbf16f7ba_18","name":"3306","type":"ULI","href":null,"layout":null,"metadata":null,"text":"Exponential Moving Average (EMA): Puts more weight on recent prices to identify short-term trends.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"A","start":0,"end":33,"href":"https:\u002F\u002Fwww.investopedia.com\u002Fterms\u002Fe\u002Fema.asp","anchorType":"LINK","userId":null,"linkMetadata":null},{"__typename":"Markup","type":"STRONG","start":0,"end":34,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_19":{"__typename":"Paragraph","id":"37fcbf16f7ba_19","name":"4e81","type":"ULI","href":null,"layout":null,"metadata":null,"text":"Relative Strength Index (RSI): Measures price movement speed and oscillates between 0 and 100 to identify overbought\u002Foversold conditions.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"A","start":0,"end":30,"href":"https:\u002F\u002Fwww.investopedia.com\u002Fterms\u002Fr\u002Frsi.asp","anchorType":"LINK","userId":null,"linkMetadata":null},{"__typename":"Markup","type":"STRONG","start":0,"end":31,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_20":{"__typename":"Paragraph","id":"37fcbf16f7ba_20","name":"a7fd","type":"ULI","href":null,"layout":null,"metadata":null,"text":"Bollinger Bands: Help assess market volatility with upper and lower bands around the moving average.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"A","start":0,"end":16,"href":"https:\u002F\u002Fwww.investopedia.com\u002Fterms\u002Fb\u002Fbollingerbands.asp","anchorType":"LINK","userId":null,"linkMetadata":null},{"__typename":"Markup","type":"STRONG","start":0,"end":16,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_21":{"__typename":"Paragraph","id":"37fcbf16f7ba_21","name":"e02d","type":"PRE","href":null,"layout":null,"metadata":null,"text":"def calculate_insights(window, dow_window):\n if len(window) \u003E= 5:\n # 5-minute rolling average\n rolling_avg = window['Close'].rolling(window=5).mean().iloc[-1]\n \n # Exponential Moving Average (EMA)\n ema = window['Close'].ewm(span=5, adjust=False).mean().iloc[-1]\n \n # Bollinger Bands (using a 5-period window)\n std = window['Close'].rolling(window=5).std().iloc[-1]\n bollinger_upper = rolling_avg + (2 * std)\n bollinger_lower = rolling_avg - (2 * std)\n\n # RSI calculation\n delta = window['Close'].diff()\n gain = delta.where(delta \u003E 0, 0)\n loss = -delta.where(delta \u003C 0, 0)\n avg_gain = gain.rolling(window=14, min_periods=1).mean().iloc[-1]\n avg_loss = loss.rolling(window=14, min_periods=1).mean().iloc[-1]\n rs = avg_gain \u002F avg_loss if avg_loss != 0 else float('nan')\n rsi = 100 - (100 \u002F (1 + rs))\n\n # Calculate Relative Strength Index (RSI) if there are enough periods (14 is typical)\n delta = window['Close'].diff()\n gain = delta.where(delta \u003E 0, 0)\n loss = -delta.where(delta \u003C 0, 0)\n avg_gain = gain.rolling(window=14, min_periods=1).mean().iloc[-1]\n avg_loss = loss.rolling(window=14, min_periods=1).mean().iloc[-1]\n rs = avg_gain \u002F avg_loss if avg_loss != 0 else float('nan')\n rsi = 100 - (100 \u002F (1 + rs))\n\n # Calculate Dow Jones index rolling average\n dow_rolling_avg = dow_window['Close'].rolling(window=5).mean().iloc[-1]\n \n market_open_duration = get_market_open_duration(window)\n if int(market_open_duration) % 5 == 0: # Trigger LLM every 5 minutes\n get_natural_language_insights(\n rolling_avg, ema, rsi, bollinger_upper, bollinger_lower,\n price_change, volume_change, dow_rolling_avg, market_open_duration, \n dow_price_change, dow_volume_change, daily_high, daily_low, \n buying_momentum, selling_momentum\n )","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"EXPLICIT","lang":"python"},"iframe":null,"mixtapeMetadata":null},"ImageMetadata:0*gA3OREfypUFbcCjp.png":{"__typename":"ImageMetadata","id":"0*gA3OREfypUFbcCjp.png","originalHeight":450,"originalWidth":1056,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:37fcbf16f7ba_22":{"__typename":"Paragraph","id":"37fcbf16f7ba_22","name":"a2e5","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:0*gA3OREfypUFbcCjp.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_23":{"__typename":"Paragraph","id":"37fcbf16f7ba_23","name":"8102","type":"H4","href":null,"layout":null,"metadata":null,"text":"Step 5: Getting Natural Language Insights Using Ollama","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_24":{"__typename":"Paragraph","id":"37fcbf16f7ba_24","name":"c428","type":"P","href":null,"layout":null,"metadata":null,"text":"To make this project more interactive, we can integrate Llama 3 model to generate natural language insights every 5 minutes.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":56,"end":63,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_25":{"__typename":"Paragraph","id":"37fcbf16f7ba_25","name":"2498","type":"PRE","href":null,"layout":null,"metadata":null,"text":"def get_natural_language_insights(\n rolling_avg, ema, rsi, bollinger_upper, bollinger_lower,\n price_change, volume_change, dow_rolling_avg, market_open_duration, dow_price_change, dow_volume_change, \n daily_high, daily_low, buying_momentum, selling_momentum, timestamp\n):\n prompt = f\"\"\"\n You are a professional stock broker. Apple's stock has a 5-minute rolling average of {rolling_avg:.2f}.\n The Exponential Moving Average (EMA) is {ema:.2f}, and the Relative Strength Index (RSI) is {rsi:.2f}.\n The Bollinger Bands are set with an upper band of {bollinger_upper:.2f} and a lower band of {bollinger_lower:.2f}.\n The price has changed by {price_change:.2f}, and the volume has shifted by {volume_change}.\n The DOW price has changed by {dow_price_change:.2f}, and the volume has shifted by {dow_volume_change}.\n Meanwhile, the Dow Jones index has a 5-minute rolling average of {dow_rolling_avg:.2f}.\n The market has been open for {market_open_duration:.2f} minutes.\n Today's high was {daily_high:.2f} and low was {daily_low:.2f}.\n The buying momentum is {buying_momentum:.2f} and selling momentum is {selling_momentum:.2f}.\n Based on this data, provide insights into the current stock trend and the general market sentiment.\n The insights should not be longer than 100 words and should not have an introduction.\n \"\"\"\n response = ollama.chat(\n model=\"llama3\",\n messages=[{\"role\": \"user\", \"content\": prompt}]\n )\n response_text = response['message']['content'].strip()\n print(\"Natural Language Insight:\", response_text)","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"AUTO","lang":"python"},"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_26":{"__typename":"Paragraph","id":"37fcbf16f7ba_26","name":"1967","type":"P","href":null,"layout":null,"metadata":null,"text":"This function summarizes the stock performance in plain English using technical indicators and trends from the previous 5 minutes.","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_27":{"__typename":"Paragraph","id":"37fcbf16f7ba_27","name":"c0b0","type":"H4","href":null,"layout":null,"metadata":null,"text":"Step 6: Setting up the Streamlit UI","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_28":{"__typename":"Paragraph","id":"37fcbf16f7ba_28","name":"52a8","type":"P","href":null,"layout":null,"metadata":null,"text":"Now let’s set up a basic UI with Streamlit to display stock updates and insights in real time.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":33,"end":42,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_29":{"__typename":"Paragraph","id":"37fcbf16f7ba_29","name":"f353","type":"PRE","href":null,"layout":null,"metadata":null,"text":"message = st.chat_message(\"assistant\")\nmessage.write(timestamp)\nmessage.write(response_text)","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"AUTO","lang":"lua"},"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_30":{"__typename":"Paragraph","id":"37fcbf16f7ba_30","name":"656a","type":"PRE","href":null,"layout":null,"metadata":null,"text":"streamlit run app.py","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":{"__typename":"CodeBlockMetadata","mode":"AUTO","lang":"typescript"},"iframe":null,"mixtapeMetadata":null},"ImageMetadata:0*R8YN0Od20TVNYO6e.png":{"__typename":"ImageMetadata","id":"0*R8YN0Od20TVNYO6e.png","originalHeight":1520,"originalWidth":1400,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:37fcbf16f7ba_31":{"__typename":"Paragraph","id":"37fcbf16f7ba_31","name":"7fc1","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:0*R8YN0Od20TVNYO6e.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_32":{"__typename":"Paragraph","id":"37fcbf16f7ba_32","name":"b245","type":"P","href":null,"layout":null,"metadata":null,"text":"And that’s how you can build your very own real-time stock advisor!","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_33":{"__typename":"Paragraph","id":"37fcbf16f7ba_33","name":"5978","type":"H4","href":null,"layout":null,"metadata":null,"text":"Improvements and Future Enhancements","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_34":{"__typename":"Paragraph","id":"37fcbf16f7ba_34","name":"d421","type":"P","href":null,"layout":null,"metadata":null,"text":"This project is a prototype focused on trends from the last trading day. You can enhance it by incorporating historical data, news integration, or advanced models like time series neural networks. Here are some ideas for improvement:","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_35":{"__typename":"Paragraph","id":"37fcbf16f7ba_35","name":"200c","type":"OLI","href":null,"layout":null,"metadata":null,"text":"Historical Data Integration: Currently, the app processes only the previous trading day’s data. You can enhance it by integrating a rolling window of historical data over weeks or months.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":27,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_36":{"__typename":"Paragraph","id":"37fcbf16f7ba_36","name":"b811","type":"OLI","href":null,"layout":null,"metadata":null,"text":"News Integration: Stock trends are often influenced by news events. Adding a real-time news feed could provide better context.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":16,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_37":{"__typename":"Paragraph","id":"37fcbf16f7ba_37","name":"a20d","type":"OLI","href":null,"layout":null,"metadata":null,"text":"Advanced Time Series Forecasting: You could further improve insights by incorporating time-series neural networks for more accurate trend predictions.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":32,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_38":{"__typename":"Paragraph","id":"37fcbf16f7ba_38","name":"713d","type":"OLI","href":null,"layout":null,"metadata":null,"text":"Custom Alerts: Implementing customizable alerts based on significant stock movements or indicators like RSI could make this app more practical for day traders.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"STRONG","start":0,"end":13,"href":null,"anchorType":null,"userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_39":{"__typename":"Paragraph","id":"37fcbf16f7ba_39","name":"f797","type":"P","href":null,"layout":null,"metadata":null,"text":"The full source code and the Jupyter Notebook are available in the GitHub repository. Feel free to reach out if you have ideas for optimizing prompts or other improvements.","hasDropCap":null,"dropCapImage":null,"markups":[{"__typename":"Markup","type":"A","start":67,"end":84,"href":"https:\u002F\u002Fgithub.com\u002FtapanBabbar9\u002Fllama3\u002Ftree\u002Fmain\u002Fstock-trader","anchorType":"LINK","userId":null,"linkMetadata":null}],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"ImageMetadata:1*mD5ZyPIE4wi_zZenb0Rbbw.gif":{"__typename":"ImageMetadata","id":"1*mD5ZyPIE4wi_zZenb0Rbbw.gif","originalHeight":270,"originalWidth":480,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:37fcbf16f7ba_40":{"__typename":"Paragraph","id":"37fcbf16f7ba_40","name":"1ec5","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:1*mD5ZyPIE4wi_zZenb0Rbbw.gif"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_41":{"__typename":"Paragraph","id":"37fcbf16f7ba_41","name":"fe71","type":"H4","href":null,"layout":null,"metadata":null,"text":"A Message from InsiderFinance","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"ImageMetadata:0*10x5_2smmKq8oIlf.png":{"__typename":"ImageMetadata","id":"0*10x5_2smmKq8oIlf.png","originalHeight":52,"originalWidth":301,"focusPercentX":null,"focusPercentY":null,"alt":null},"Paragraph:37fcbf16f7ba_42":{"__typename":"Paragraph","id":"37fcbf16f7ba_42","name":"2f53","type":"IMG","href":null,"layout":"INSET_CENTER","metadata":{"__ref":"ImageMetadata:0*10x5_2smmKq8oIlf.png"},"text":"","hasDropCap":null,"dropCapImage":null,"markups":[],"codeBlockMetadata":null,"iframe":null,"mixtapeMetadata":null},"Paragraph:37fcbf16f7ba_43":{"__typename":"Paragraph","id":"37fcbf16f7ba_43","name":"dfe6","type":"P","href":null,"layout":null,"metadata":null,"text":"Thanks for being a part of our community! 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