Upload README.md

README.md

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+# 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
+
+```bash
+git clone https://huggingface.co/Premchan369/alphaforge-quant-system
+cd alphaforge-quant-system
+pip install -r requirements.txt
+```
+
+## Usage
+
+### Train Alpha Model
+```bash
+python main.py --mode train --tickers SPY QQQ AAPL MSFT --epochs 50
+```
+
+### Run Full Backtest
+```bash
+python main.py --mode backtest --start 2020-01-01 --end 2024-01-01
+```
+
+### Train Options Model
+```bash
+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