🤖 TradeAgent - AI-Powered Algorithmic Trading System

TradeAgent is a production-ready hybrid AI trading system that combines deterministic technical analysis with LLM-powered sentiment analysis. Built for Alpaca's paper trading API, it features adaptive parameter optimization, portfolio correlation monitoring, and comprehensive risk management.

✨ Key Features

🧠 Intelligent Strategies

• Momentum Trading: RSI, MACD, volume-based signals with dynamic thresholds
• News Sentiment Analysis: Claude 3.5 Sonnet analyzes 90+ articles per ticker
• Sentiment Trend Tracker: Detects sentiment momentum shifts and inflection points
• Defensive Core: Monthly rebalancing of VTI/VGK/GLD allocation

📊 Adaptive & Self-Learning

• Adaptive Parameter Optimizer: Grid search optimization based on rolling 30-day performance
• ML Training Pipeline: Collects labeled data for future ensemble models
• Performance Analytics: Daily & weekly reports with Sharpe ratio tracking

🛡️ Risk Management

• Portfolio Correlation Monitor: Blocks over-concentrated positions (max 40% per sector)
• Dynamic Position Sizing: Kelly Criterion with half-Kelly safety (3-15% per position)
• Circuit Breakers: -3% daily loss limit, automatic trading halt
• Stop-Loss & Take-Profit: Bracket orders on all momentum entries

🗄️ Data Infrastructure

• Multi-Source News: Yahoo Finance, Finnhub, Alpha Vantage, NewsAPI
• Persistent Logging: All trades, signals, and LLM analyses stored in Supabase
• Complete Audit Trail: Track parameter changes, performance metrics, reasoning

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🚀 Quick Start

Prerequisites

• Python 3.12+
• Alpaca Paper Trading Account (Sign up)
• Supabase Account (Sign up)
• OpenRouter API Key (Sign up) (optional, for LLM features)

Installation

1. Clone the repository ```bash git clone https://github.com/enving/TradeAgent.git cd TradeAgent ```

2. Create virtual environment ```bash python3 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate ```

3. Install dependencies ```bash pip install -r requirements.txt ```

4. Configure environment variables ```bash cp .env.example .env

Edit .env with your API keys

```

Required variables: ```env ALPACA_API_KEY=your_alpaca_key ALPACA_SECRET_KEY=your_alpaca_secret SUPABASE_URL=your_supabase_url SUPABASE_KEY=your_supabase_anon_key ```

Optional (for LLM features): ```env OPENROUTER_API_KEY=your_openrouter_key NEWS_API_KEY=your_newsapi_key FINNHUB_API_KEY=your_finnhub_key ENABLE_LLM_FEATURES=true ```

5. Set up Supabase database

Run these SQL migrations in your Supabase SQL Editor (in order):

1. database/migrations/create_ml_training_data.sql

2. database/migrations/add_news_and_llm_logging_v2.sql

3. database/migrations/add_parameter_changes.sql

4. Test the installation ```bash python3 test_integration.py ```

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🐳 Docker Deployment (Recommended for Production)

Run the bot autonomously with automated scheduling!

Quick Start

```bash

1. Build Docker image

./run_docker.sh build

2. Test run once

./run_docker.sh once

3. Start daily scheduler (runs at 9:35 AM ET on weekdays)

./run_docker.sh schedule

4. Monitor logs

./run_docker.sh logs

5. Stop scheduler

./run_docker.sh stop ```

What You Get

• ✅ Autonomous operation - Runs daily at 9:35 AM ET automatically
• ✅ Isolated environment - No dependency conflicts
• ✅ Auto-restart - Recovers from failures automatically
• ✅ Cross-platform - Works on Linux, macOS, Windows
• ✅ Easy updates - `git pull && ./run_docker.sh build`

Alternative Deployment Options

For native Linux deployment or cron-based scheduling, see detailed guide:

• deployment/README.md - Full deployment guide with SystemD and Cron options

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📖 Usage

Run Daily Trading Loop

```bash python3 -m src.main ```

The bot will:

1. Check market hours
2. Scan for momentum signals (15 tickers)
3. Analyze news sentiment (if LLM enabled)
4. Apply risk filters (correlation, sector limits)
5. Execute trades with bracket orders
6. Monitor exit conditions for open positions
7. Log performance metrics to Supabase

Pre-Market Orders

```bash python3 -c "import asyncio; from src.main import daily_trading_loop; asyncio.run(daily_trading_loop(allow_premarket=True))" ```

Optimize Strategy Parameters

After collecting 30+ trades: ```bash

Run adaptive optimizer

python3 run_optimizer.py --strategy momentum --lookback 30

View current parameters

python3 run_optimizer.py --show ```

Test Components

```bash

Test correlation monitor

python3 test_correlation_monitor.py

Test sentiment tracker

python3 test_sentiment_tracker.py ```

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🏗️ Architecture

``` TradeAgent/ ├── src/ │ ├── strategies/ # Trading strategies │ ├── risk/ # Risk management │ ├── ml/ # Machine learning │ ├── llm/ # LLM integration │ ├── core/ # Core logic │ ├── adapters/ # External APIs │ ├── models/ # Data models │ └── database/ # Supabase client ├── database/migrations/ # SQL migrations └── tests/ # Tests ```

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📊 Trading Strategies

1. Momentum Strategy

Entry: RSI 45-75, MACD > 0, Volume > 1.1x avg
Exit: Stop -3%, Target +8%, RSI > 80

2. News Sentiment (Optional)

Entry: Sentiment > 0.6, High impact, OR inflection detected
Claude 3.5 Sonnet analyzes 90+ articles/ticker

3. Defensive Core

Allocation: 50% VTI, 30% VGK, 20% GLD
Rebalance: Monthly, first trading day

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🔬 Advanced Features

Adaptive Parameter Optimization

Tests 27+ parameter combinations on 30-day performance, selects optimal Sharpe ratio.

Sentiment Trend Tracker

Detects sentiment momentum via linear regression, blocks volatile signals.

Portfolio Correlation Monitor

Prevents over-concentration: max 0.7 correlation, 40% sector limit.

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🧪 Testing

```bash

Integration test

python3 test_integration.py

Component tests

python3 test_correlation_monitor.py python3 test_sentiment_tracker.py ```

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⚠️ Disclaimer

Educational purposes only. Not financial advice.

• Paper trading only
• No profitability guarantees
• Trading involves risk of loss
• Use at your own risk

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📄 License

MIT License - see LICENSE

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Built with ❤️ by algorithmic traders, for algorithmic traders.