📡 algo-bot

A real-time trading assistant built with Flask and Python that connects to the Alpaca Markets API to execute live trades, respond to price movements, and receive TradingView alerts via webhooks.

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🚀 Features

• 🔄 Real-Time Data via WebSocket (Alpaca IEX)
• 🛒 Market Order Execution via Alpaca Trading API
• 🧠 Trade Triggers Based on Price Movement
• 📤 Webhook Endpoint for TradingView Alerts
• 🧵 Multithreaded WebSocket Handling
• 📋 JSON-Formatted Order Logs for Easy Auditing

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🧱 Tech Stack

Tool	Purpose
Python	Core programming language
Flask	Web server and API routing
Alpaca API	Trading and market data
WebSocketApp	Real-time data stream
HTML/Jinja2	Frontend rendering (basic)
threading	Concurrent WebSocket execution

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⚙️ API & Webhook Endpoints

POST /trade

Manually submit a trade order:

curl -X POST http://localhost:5000/trade -d "symbol=AAPL&qty=1&side=buy"

POST /webhook

Handle alerts from TradingView or external systems:

{
  "symbol": "AAPL",
  "side": "buy",
  "qty": 1
}

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🧪 Strategy Logic (Example)

If QBTS price drops more than $0.05 compared to last recorded trade price → trigger a market buy.

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📂 Project Structure

algo-bot/
├── app.py               # Flask + Alpaca integration
├── templates/           # HTML templates (index, response, webhook)
├── static/              # Optional for CSS/JS
├── requirements.txt     # Dependencies

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🔒 Security Note

Do NOT commit your Alpaca API credentials. Use environment variables or a secrets manager in production.

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🎓 Educational Use

This project was built by Asmaa Abdul-Amin. It demonstrates skills aligned with Quant Developer roles, including:

• Real-time data ingestion
• RESTful API integration
• Conditional logic for trade execution
• Logging and data serialization

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🧠 Author

Asmaa Abdul-Amin
🔗 Portfolio
🐙 GitHub
💼 LinkedIn

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⚠️ This project is for educational use only. It does not constitute financial advice or real trading recommendations.