Development of Automated Trading Alert Bot for Large Investments in Small-Cap Stocks

Python Uzmanlar Diğer Python

Şartname

Development of Automated Trading Alert Bot for Large Investments in Small-Cap Stocks


🧠 Project Objective:
To build a fully automated trading bot that monitors real-time financial data and alerts the user when large institutional investments (e.g., $100M or more) are made in small-cap stocks. The goal is to empower better trading decisions based on timely, high-impact activity from major market players.


📦 Scope of Work:

  1. Real-Time Data Monitoring

    • Monitor SEC filings (e.g., Form 13D, 13G, Form 4) using sources like EDGAR.

    • Track block trades and unusual volume transactions from institutional players.

    • Scrape or connect to financial news APIs to detect high-signal headlines related to investments.

  2. Alert Logic & Thresholds

    • Automatically detect and filter investments ≥ $100 million in small-cap stocks.

    • Support dynamic threshold configuration (e.g., user can change $100M to $50M).

    • Include filters by sector, exchange (e.g., NASDAQ, NYSE), or market cap range.

  3. Notification System

    • Deliver instant alerts via:

      • Email

      • SMS (via Twilio or similar)

      • App push notifications (Firebase, Pushover, or web notifications)

    • Alert content should include:

      • Company name, ticker, amount invested, filing source, and timestamp.

      • Optional: sentiment snapshot or insider activity context.

  4. User Dashboard (Web-Based UI)

    • Simple, responsive frontend showing:

      • Real-time alerts

      • Historical signal logs

      • Visual filters (sector, size, exchange)

      • Optional charts for trade heatmaps, volume trends, or market sentiment

    • Authentication/login system (basic security layer).

  5. Technical Features

    • Modular architecture (separate data handler, logic engine, UI).

    • Secure handling of API keys and tokens.

    • Scheduled background jobs or WebSocket-based real-time monitoring.

    • Error handling and event logging.

  6. Deployment & Documentation

    • Deploy on cloud server (e.g., AWS, DigitalOcean, or Heroku).

    • Provide:

      • Clean, well-commented codebase

      • Full setup guide

      • API integration instructions

      • Simple user manual or walkthrough


🔧 Preferred Tech Stack:
(You may suggest alternatives)

  • Backend: Python (FastAPI, Flask, or Django)

  • Frontend: React.js or Vue.js

  • Database: PostgreSQL or MongoDB

  • Notifications: SMTP (email), Twilio (SMS), Firebase (push)

  • Deployment: Docker, AWS EC2, or Heroku


📊 Optional Add-Ons (Future Milestones):

  • Sentiment analysis on news headlines using NLP.

  • Insider trading report integration.

  • Machine learning-based alert ranking.

  • Portfolio or watchlist integration.


💬 Additional Questions to Address in Proposal:

  1. Which data sources will you use for filings, trades, and news?

  2. Can you show me past related work (dashboards, bots, financial tools)?

  3. Will the bot be capable of monitoring both U.S. and non-U.S. stocks?

  4. How easy will it be to adjust key thresholds or filters?

  5. What are the estimated costs of third-party APIs used?


✅ Final Deliverables:

  • Fully functional, deployed trading alert system.

  • Source code (GitHub or zip).

  • Documentation for setup, use, and customization.

  • Demo/walkthrough session (if possible).


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