> ## Documentation Index > Fetch the complete documentation index at: https://docs.dappier.com/llms.txt > Use this file to discover all available pages before exploring further. # 🪄 Activepieces Stock Market Analyst Bot: Automate Investment Research with Real-Time Data & AI-Powered Reports You can also import this template directly from here - [https://cloud.activepieces.com/templates/fb3b8HFenLKGNxe8BRJhA](https://cloud.activepieces.com/templates/fb3b8HFenLKGNxe8BRJhA) This notebook demonstrates how to set up and leverage Activepieces’ automation platform combined with Dappier and OpenAI for stock market analysis. By combining real-time data, AI reasoning, and workflow automation, this notebook walks you through an innovative approach to creating fully automated investment research reports. In this notebook, you'll explore: * **Activepieces**: A powerful no-code automation platform that enables multi-step workflows with triggers, conditional routing, and app integrations—without writing a single line of code. * **Dappier**: A platform connecting LLMs and automation tools to real-time, rights-cleared data from trusted sources, specializing in domains like stock market, finance, and news. It delivers enriched, prompt-ready data, empowering AI with verified and up-to-date information. * **OpenAI**: A leading provider of advanced AI models capable of natural language understanding, contextual reasoning, and content generation. It enables intelligent, human-like interactions and supports a wide range of applications across various domains. * **Gmail**: Email-based interaction point that triggers this automation and serves as the delivery channel for the final investment report. This setup not only demonstrates a practical application of AI-driven stock market analysis but also provides a flexible framework that can be adapted to other real-world scenarios requiring real-time data integration, automation logic, and AI-powered summarization. ## ⚙️ Starting with Setup in Activepieces To get started, head over to [Activepieces](https://cloud.activepieces.com) and create a **new flow**. This bot will trigger when a new email is received in your Gmail inbox, analyze the query using AI, fetch real-time financial data from Dappier, and send a full investment report via email. ### Step 1: Set the Trigger – New Email in Gmail Search for the **Gmail** piece and choose the **New Email** trigger. Configure the following: * **Authentication**: Connect your Gmail account. * **Label**: Set to `INBOX` to monitor all incoming messages. * **Category**: Leave it empty to include all categories. Once this trigger is set, your automation will activate whenever a new email lands in your inbox. ### Step 2: Determine Relevance – Is It a Stock Query? Add a new **OpenAI** action immediately after the trigger. Configure it as follows: * **Model**: `chatgpt-4o-latest` * **Prompt**: ```text theme={null} Return only a boolean value: true or false. Determine whether the following query mentions a stock ticker symbol or company name related to the stock market. Query: {{trigger['message']['text']}} ``` * **Max Tokens**: 2048 * **Temperature**: 0.1 ## 🔀 Conditional Routing & Ticker Extraction ### Step 3: Add a Router to Branch Logic Use the **Router** to evaluate the result from the previous OpenAI step. Configure two branches: * **Branch Name**: `Stock Analysis` * **Condition**: ```text theme={null} {{step_2}} exactly matches "true" ``` * **Branch Name**: `Otherwise` * This fallback branch will do nothing or exit the flow if the email isn't related to a stock query. ### Step 4: Extract the Stock Ticker Symbol Under the `Stock Analysis` branch, add a new **OpenAI** action. Configure it as follows: * **Model**: `chatgpt-4o-latest` * **Prompt**: ```text theme={null} Extract the stock ticker symbol from the following query. Respond with only the ticker symbol—no explanations, no extra text. query: {{trigger['message']['text']}} ``` * **Max Tokens**: 2048 * **Temperature**: 0.1 This action ensures you only extract the relevant stock ticker (e.g., `AAPL` from “Tell me about Apple”). ## 📊 Real-Time Financial Data Retrieval using Dappier With the extracted stock ticker, we'll now gather structured financial insights using **Dappier's real-time models**. For each query below, make sure to use the **`Real Time Data`** action from the Dappier piece. ### Step 5: Get Company Overview Add a **Dappier** action and select `Real Time Data`. Use the following query: ```text theme={null} Latest company overview for {{extracted_ticker}} include company profile, industry, sector, CEO, headquarters location, employee count, market capitalization and stock ticker symbol. ``` This gives a detailed snapshot of the company and its basic metadata. *** ### Step 6: Get Financial Performance Metrics Add another **Dappier** action (again using `Real Time Data`) and enter: ```text theme={null} Extract latest financial performance data for {{extracted_ticker}}, including Revenue (TTM), Net Income (TTM), Year-over-Year revenue growth, gross margin, and recent quarterly trends ``` This returns structured financial performance metrics and key indicators. *** ### Step 7: Competitive Benchmarking Add a third **Dappier** action (also using `Real Time Data`) with the query: ```text theme={null} Identify 3-5 peer companies in the same sector as {{extracted_ticker}}. Extract key metrics such as P/E ratio, revenue, stock price, and market cap. ``` This enables side-by-side comparison with industry peers. ## 📈 Real-Time Stock Snapshot & Market News We’ll now use Dappier’s specialized **Stock Market Data** action to retrieve detailed market-level insights and the latest categorized financial news. ### Step 8: Get Real-Time Stock Snapshot Add a **Dappier** action and select `Stock Market Data`. Use the following query: ```text theme={null} Include latest current price with %daily change, volume, 52-week high/low, P/E ratio, EPS, dividend yield, and chart data for 1D, 5D, 1M, YTD, and 1Y for {{extracted_ticker}} ``` This returns a structured snapshot of the company's live trading metrics and historical movement. *** ### Step 9: Get Categorized Financial News Add another **Dappier** action (also using `Stock Market Data`) with the following query: ```text theme={null} Fetch latest finanical news for {{extracted_ticker}}. Include Earnings, Analyst Ratings, Market Moves, Partnerships, and Legal/Regulatory. ``` This returns categorized, real-time news summaries, perfect for building context in your final investment report. ## 🧠 Generate Investment Report with OpenAI Now that we've collected all the required data—company profile, financial performance, peer benchmarking, real-time stock snapshot, and categorized news—we'll synthesize everything into a polished, readable investment report using OpenAI. ### Step 10: Compile the Full Report Add an **OpenAI** action and configure it with the following: * **Model**: `chatgpt-4o-latest` * **Prompt**: ````text theme={null} Compile a comprehensive investment report for **{{extracted_ticker}}**, formatted as a single HTML email body. Synthesize the outputs of all tasks: company overview, financial performance, competitive benchmarking, real-time stock snapshot, and categorized financial news. The report must include the following structured sections: 1. **Quick Summary** A concise AI-generated summary of {{extracted_ticker}} (e.g., "Apple is a global tech leader…"). 2. **Company Profile Table** Industry, Sector, CEO, HQ Location, Employees, Market Cap. 3. **Financial Performance Table** Revenue (TTM), Net Income (TTM), YoY Growth, Gross Margin. Include a short narrative on key trends below the table. 4. **Competitive Benchmarking Table** Compare {{extracted_ticker}} against 3–5 peers using: P/E, Revenue, Stock Price, Market Cap. 5. **Real-Time Stock Snapshot** Include: Price, % Change, Volume, 52-week High/Low, P/E, EPS, Dividend Yield. Present chart summaries for 1D, 5D, 1M, YTD, and 1Y as plain text descriptions or inline image URLs (if available). 6. **Categorized Financial News** Include 5 categories: - Earnings - Analyst Ratings - Market Moves - Partnerships - Legal/Regulatory Tag each news item with sentiment: 🟢 Positive, 🟡 Neutral, 🔴 Negative. 7. **Insight Section** Write 2–3 paragraphs with: - What’s going on with {{company_name}} - Why it matters - Outlook (*clearly labeled as not financial advice*) **Important formatting instructions**: - Use semantic HTML (e.g., `

`, ``, `

`) - Apply all styles via **inline CSS only** - Ensure all links are clickable (``) - Keep it responsive and compatible with major email clients (no JavaScript, no external assets) - Don't include ```html in the output. All the related information is below: {{company_overview_response}} {{financial_performance_response}} {{peer_comparison_response}} {{stock_snapshot_response}} {{news_response}} ```` This action will generate a clean, richly structured report in semantic HTML format—ready to be delivered by email. ## 📤 Delivering the Report via Gmail Now that the investment report is generated, let’s send it back to the original sender as a formatted HTML email using Gmail. ### Step 11: Send the Email Add a **Gmail** action and choose `Send Email`. Configure the email fields as follows: * **To**: ```text theme={null} {{email_sender_address}} ``` Use the dynamic reference to send it back to the email originator: ```text theme={null} {{trigger['message']['from']['value'][0]['address']}} ``` * **Subject**: ```text theme={null} Stock Market Analysis of {{extracted_ticker}} ``` * **Body**: ```text theme={null} {{investment_report_html}} ``` * **Body Type**: `HTML` * **CC/BCC/Reply-To**: Leave these empty unless needed. * **Draft**: Set to `false` to send the email immediately. Once configured, the flow will respond to every stock-related query with a professional-grade investment report, fully automated. ## 🌟 Highlights This notebook has guided you through building a fully automated stock market analysis workflow using Activepieces, OpenAI, Dappier, and Gmail. You’ve seen how to classify queries, extract ticker symbols, fetch real-time financial data, generate detailed reports, and deliver them directly to email—all without writing a single line of backend code. Key tools utilized in this notebook include: * **Activepieces**: A powerful no-code automation platform that enables app-triggered workflows with conditional logic, AI actions, and app integrations. * **OpenAI**: A leading provider of advanced AI models used here to classify stock queries, extract ticker symbols, and generate investment reports in rich HTML format. * **Dappier**: A platform connecting LLMs and automation tools to real-time, rights-cleared data from trusted sources, specializing in domains like stock market, finance, and news. It delivers enriched, prompt-ready data, empowering automations with verified and up-to-date information. * **Gmail**: Serves as both the input trigger and output channel, making the workflow seamlessly email-driven. This comprehensive setup allows you to adapt and expand the example for various scenarios requiring stock research, financial insights, or real-time data–driven automation.