AlphaForge - Multi-Asset Quantitative Trading System

A comprehensive quantitative trading system that combines price-based alpha signals, financial sentiment analysis, volatility forecasting, portfolio optimization, and ML-based options pricing.

Features

1. Multi-Asset Alpha Model

LSTM neural network for sequential pattern recognition
Transformer architecture for attention-based forecasting
XGBoost ensemble for robust feature-based predictions
Ensemble combining all three with IC-weighted blending
IC Tracking: Information Coefficient monitoring over time
Feature Drift Detection: XGBoost importance divergence tracking

2. News + Sentiment Alpha (FinBERT)

Uses ProsusAI/finbert for financial sentiment analysis
Converts news/social media into numerical alpha signals
Confidence-weighted aggregation per asset per day
Synthetic news generation for testing

3. Volatility Forecasting Engine

GARCH(1,1) with Student-t errors for baseline
LSTM with skewed Student's t distributional output
EWMA covariance matrix construction
Positive definite enforcement

4. Portfolio Optimizer

Mean-variance optimization with transaction costs
Max Sharpe ratio optimization
Minimum volatility with return constraints
Robust optimization with uncertainty sets
Black-Litterman model for incorporating views
Efficient frontier computation

5. Options Pricing with ML

4-layer neural network (256-128-64-32)
Black-Scholes baseline for comparison
Implied volatility prediction
Mispricing detection for arbitrage signals
Synthetic data generation for training

6. Backtest Engine

Transaction cost and slippage simulation
Comprehensive metrics:
- Sharpe, Sortino, Calmar ratios
- Max drawdown, win rate
- Alpha, Beta, Information Ratio
- Turnover and cost analysis
Regime detection (bull/bear/high-vol)
Rolling performance metrics

Installation

git clone https://huggingface.co/Premchan369/alphaforge-quant-system
cd alphaforge-quant-system
pip install -r requirements.txt

Usage

Train Alpha Model

python main.py --mode train --tickers SPY QQQ AAPL MSFT --epochs 50

Run Full Backtest

python main.py --mode backtest --start 2020-01-01 --end 2024-01-01

Train Options Model

python main.py --mode options

Pipeline Architecture

Market Data (OHLCV)
    |
    +---> Technical Indicators (RSI, MACD, Bollinger, etc.)
    +---> Cross-Asset Features (beta, correlation, spreads)
    |
    v
Alpha Model (LSTM + Transformer + XGBoost Ensemble)
    |---> Predicted Returns (mu)
    |---> IC Tracking
    |
News Data
    |
    v
Sentiment Model (FinBERT)
    |---> Sentiment Alpha (S_t)
    |
    v
Combined Alpha = w1 * Price Alpha + w2 * Sentiment Alpha

Market Data
    |
    v
Volatility Engine (GARCH + LSTM)
    |---> Covariance Matrix (Sigma)
    |
    v
Portfolio Optimizer (Mean-Variance / Max Sharpe / Robust)
    |---> Optimal Weights (w)
    |
    v
Backtest Engine
    |---> PnL, Sharpe, Drawdown, etc.

Key Metrics

Metric	Description
IC	Information Coefficient (rank correlation between predicted and actual returns)
Sharpe	Risk-adjusted return (excess return / volatility)
Sortino	Downside risk-adjusted return
Max DD	Maximum peak-to-trough decline
Calmar	Annualized return / max drawdown
Alpha	Excess return vs benchmark
Beta	Market sensitivity

File Structure

File	Description
`main.py`	Entry point and orchestration
`market_data.py`	Data fetching and feature engineering
`alpha_model.py`	LSTM/Transformer/XGBoost ensemble
`sentiment_model.py`	FinBERT sentiment analysis
`volatility_model.py`	GARCH + LSTM volatility forecasting
`portfolio_optimizer.py`	Mean-variance and robust optimization
`options_pricer.py`	ML options pricing and mispricing detection
`backtest_engine.py`	Backtesting with comprehensive metrics

Research Backing

Alpha Models: xLSTM-TS with wavelet denoising (Lopez Gil et al., 2024)
Sentiment: FinBERT (Araci, 2019) with ChatGPT benchmarking (Fatouros et al., 2023)
Volatility: LSTM with skewed Student's t (Michankow, 2025)
Portfolio: Multi-task learning for joint optimization (Ong & Herremans, 2023)
Options: 5-layer FNN outperforming Black-Scholes (Berger et al., 2023)

License

MIT