FinGPT Trader 🔬📈

A quantitative trading system integrating large language models (Falcon-7B) with statistical arbitrage strategies. Combines natural language processing for market sentiment analysis with traditional quantitative methods for market inefficiency detection.

System Overview

• Quantitative Analysis Engine

  • Statistical arbitrage detection
  • Order book imbalance analysis
  • Market microstructure modeling

• NLP-Enhanced Market Analysis

  • Sentiment extraction using Falcon-7B
  • News impact quantification
  • Text-based signal generation

• Systematic Trading Framework

  • Event-driven architecture
  • Asynchronous execution
  • Multi-asset portfolio optimization

Core Features 🚀

• AI-Driven Market Analysis

  • Real-time sentiment analysis using Falcon-7B
  • Pattern recognition in order book data
  • Market inefficiency detection using ML

• Smart Portfolio Management

  • Dynamic position sizing based on Kelly Criterion
  • Tax-aware trading with loss harvesting
  • Risk-adjusted rebalancing

• Multi-Exchange Support

  • Binance integration (testnet/mainnet)
  • Advanced connection management
  • Rate limit handling

System Architecture 🏗️

graph TD
    A[Falcon-7B Model] -->|Sentiment Analysis| B[Market Analysis Engine]
    B -->|Signals| C[Trading Core]
    D[Binance API] -->|Market Data| B
    C -->|Orders| D
    E[Risk Manager] -->|Limits| C
    F[Portfolio Manager] -->|Positions| C
    G[Tax Optimizer] -->|Strategy| F

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Quick Start 🚦

1. Environment Setup

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# Install dependencies
pip install -r requirements.txt

2. Configuration

# Set up your .env file
cp .env.example .env
# Edit with your API keys and tokens:
# - BINANCE_API_KEY
# - BINANCE_SECRET_KEY
# - HUGGINGFACE_TOKEN

3. Run Trading System

# Development mode (testnet)
python main.py

# With specific pairs
python scripts/run_trader.py --pairs BTCUSDT,ETHUSDT

Unique Features 💫

1. AI-Enhanced Market Analysis

# Example of sentiment-adjusted trading signal
sentiment_score = await analyzer.get_sentiment(news_data)
market_signal = detector.analyze_inefficiencies(
    orderbook_data=depth,
    sentiment_weight=sentiment_score
)

2. Advanced Risk Management

• Dynamic VaR calculation
• Sentiment-adjusted position sizing
• Real-time drawdown monitoring

3. Tax-Aware Trading

• Automated tax-loss harvesting
• Wash sale prevention
• Tax efficiency optimization

Mathematical Framework 📐

Sentiment-Adjusted Market Inefficiency Detection

The system uses a composite signal model:

$$ S(t) = \alpha M(t) + \beta I(t) + \gamma L(t) $$

Where:

• $S(t)$ is the final trading signal
• $M(t)$ is market microstructure score
• $I(t)$ is orderbook imbalance
• $L(t)$ is NLP sentiment score
• $\alpha, \beta, \gamma$ are weights

Order Book Imbalance

$$ I(t) = \frac{\sum_{i=1}^n V^b_i - \sum_{i=1}^n V^a_i}{\sum_{i=1}^n V^b_i + \sum_{i=1}^n V^a_i} $$

Where:

• $V^b_i$ is bid volume at level i
• $V^a_i$ is ask volume at level i
• $n$ is depth of orderbook

Position Sizing with Kelly Criterion

Modified Kelly formula incorporating sentiment:

$$ f^* = \frac{p(1+r) - q}{r} \cdot (1 + \lambda|S|) $$

Where:

• $f^* is optimal fraction
• $p$ is win probability
• $q = 1-p$ is loss probability
• $r$ is win/loss ratio
• $S$ is sentiment score
• $\lambda$ is sentiment sensitivity

Risk Management

Value at Risk with sentiment adjustment:

$$ VaR_{\alpha}(S) = -\inf{l \in \mathbb{R}: P(L \leq l | S) \geq \alpha} $$

Portfolio risk score:

$$ R = \sqrt{w^T\Sigma w} \cdot (1 + \delta|S|) $$

Where:

• $w$ is portfolio weights
• $\Sigma$ is covariance matrix
• $S$ is sentiment score
• $\delta$ is risk adjustment factor

Tax-Loss Harvesting

Harvesting threshold with wash sale prevention:

$$ H(p, t) = \begin{cases} 1 & \text{if } \frac{p_t - p_0}{p_0} < \tau \text{ and } t > T_{wash} \\ 0 & \text{otherwise} \end{cases} $$

Where:

• $p_t$ is current price
• $p_0$ is entry price
• $\tau$ is loss threshold
• $T_{wash}$ is wash sale period

Development Status 🔄

Currently in active development with focus on:

• WebSocket connection stability
• Enhanced error recovery
• Performance optimization
• Advanced backtesting framework

Contributing 🤝

1. Fork the repository
2. Create your feature branch
3. Test thoroughly using testnet
4. Submit a pull request

Trading Strategies 📊

Current implementation includes:

• Sentiment-driven market making
• Statistical arbitrage
• Tax-loss harvesting
• Portfolio rebalancing

Warning ⚠️

This is a complex trading system. Always:

• Start with testnet trading
• Use small position sizes
• Monitor system performance
• Understand the risks involved

License 📄

MIT License - See LICENSE

Acknowledgments 🙏

• Falcon-7B for NLP capabilities
• python-binance for exchange connectivity
• FinRL for inspiration