Python FastAPI Developer for AI-Integrated MT5 Trading System (Data + API + Model Pipeline)

We are building a Python-based backend that integrates with a MetaTrader 5 Expert Advisor (EA).
The EA sends structured market data (pair, session, ATR, volume, previous highs/lows, etc.) to a FastAPI server, and the server responds with AI-driven trade recommendations and parameters.
This backend will act as the “brain” of the EA, managing all decision logic, learning models, and data storage.

This is a serious build, not a small script — we’re creating a scalable, production-ready AI trading infrastructure.

🧠 Core Objectives

• Develop a FastAPI backend that receives JSON data from MT5 and returns trading bias + risk parameters.

• Build an internal data processing pipeline that:

  • Cleans, normalizes, and stores incoming candle/trade data.

  • Appends trade outcomes to database for continuous retraining.

• Integrate AI/ML logic to analyze new inputs and determine optimal strategy parameters for each session.

• Support multiple currency pairs and sessions (London, New York).

• Host a simple admin panel or API key system to manage user access and licenses.

⚙️ Technical Requirements

• Language: Python 3.10+

• Framework: FastAPI (preferred)

• Database: MySQL / PostgreSQL

• Integration: Must handle HTTPS POST requests from EA (JSON format)

• AI Component: Use historical data + outcome labels for retraining (e.g., LightGBM, CatBoost, or TensorFlow).

• Data I/O: Must store candle data (OHLCV), ATR, session time, and order outcome results.

• Scalability: Ability to serve multiple EA instances simultaneously with low latency (<200 ms).

• Security: API key authentication per user.

📊 Example Workflow

1. EA sends a JSON packet:

2. Backend model interprets data → selects the most probable setup for that session.

3. Server returns:

    

        

            

        
    


    

        {
  "direction": "buy",
  "risk_percent": 0.03,
  "tp_rr": 4.0,
  "entry_zone": "1.0705-1.0715",
  "confidence": 0.82
}

    

4. EA executes trade and logs results → sent back to the API for retraining.

💾 Deliverables

• FastAPI backend with POST/GET endpoints.

• Database schema for storing trades and session data.

• Sample AI model integration (can start with basic logic; we’ll scale).

• Log and retrain function (auto-update nightly).

• Clear documentation and code comments.

• Basic Docker or deployment guide for hosting (optional bonus).

🧠 Skills Required

• ✅ Python (FastAPI, Pandas, Numpy)

• ✅ MySQL or PostgreSQL (data storage and trade logs)

• ✅ Data mining and normalization (OHLCV, indicators)

• ✅ REST API design (JSON, authentication)

• ✅ Basic ML integration (LightGBM, Scikit-learn, or TensorFlow)

• ✅ Forex knowledge (candles, ATR, session logic)

💰 Budget & Timeline

• Budget: $1,200 – $2,000 (depending on experience and performance)

• Timeline: 2–3 weeks

• Long-term work available (model retraining, scaling, and dashboard builds).

🔒 Additional Notes

This project will directly power a commercial trading system.
Code must be modular, clean, and extensible — no shortcuts or unstructured scripts.
You’ll collaborate briefly with our MQL5 EA developer, so communication and clear handoff are important.

🚀 To Apply

Please include:

1. Examples of APIs or trading data pipelines you’ve built (FastAPI, Flask, Django, etc.).

2. A short explanation of how you’d structure model retraining and data storage.

3. Availability and estimated completion time.