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> **From backtesting toy β Jane Street / Two Sigma / Citadel production-grade quantitative trading platform**
**Repository**: [Premchan369/alphaforge-quant-system](https://huggingface.co/Premchan369/alphaforge-quant-system)
---
## What Makes This "Elite"
Most GitHub quant repos:
- Backtest on all data (data leakage)
- Use hand-coded RSI/MACD (no alpha mining)
- No risk management (just returns)
- No execution simulation (market orders everywhere)
- No uncertainty quantification (trading blind)
- Static models (break when markets change)
- No adversarial defense (models get exploited)
**AlphaForge v3.0 solves every single one of these.**
---
## Architecture
```
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β ALPHA FORGE v3.0 β SYSTEM MAP β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€
β β
β DATA LAYER β
β βββ market_data.py β OHLCV + features + cross-section β
β βββ news_data_integration.py β NewsAPI + RSS + GDELT + Reddit β
β βββ market_microstructure.py β Kyle's lambda, VPIN, OFI, Amihud β
β βββ limit_order_book.py β Level 2 LOB reconstruction (NEW) β
β β
β PREPROCESSING β
β βββ wavelet_denoising.py β db4 wavelets + soft thresholding β
β βββ technical_indicators.py β 30+ indicators (RSI, MACD, BB, etc.) β
β β
β ALPHA DISCOVERY β
β βββ alpha_mining.py β GP symbolic regression + LLM suggestions β
β βββ sentiment_model.py β FinBERT sentiment scoring β
β βββ alpha_model.py β XGBoost + LSTM + Transformer ensemble β
β β
β REAL-TIME INFRASTRUCTURE (NEW) β
β βββ feature_store.py β Microsecond feature compute + drift β
β βββ online_learning.py β Per-symbol adaptive models + concept driftβ
β βββ rl_execution.py β PPO Deep Hedging for optimal execution β
β β
β MODEL LAYER β
β βββ multi_task_learning.py β Joint MTL: returns + vol + portfolio β
β βββ volatility_model.py β GARCH + LSTM + skewed Student's t β
β βββ options_pricer.py β 5-layer FNN beats Black-Scholes β
β βββ stat_arb.py β Cointegration + PCA mean-reversion (NEW) β
β βββ market_making.py β Avellaneda-Stoikov quoting (NEW) β
β β
β CORRELATION & RISK (NEW) β
β βββ correlation_regime.py β DCC-GARCH + dynamic copulas β
β βββ conformal_prediction.py β Guaranteed prediction intervals β
β βββ adversarial_defense.py β FGSM attacks + watermarking (NEW) β
β βββ risk_management.py β VaR/CVaR + stress tests + compliance β
β βββ risk_engine.py β Signal risk scoring β
β βββ stress_test.py β Historical scenario stress testing β
β β
β OPTIMIZATION β
β βββ portfolio_optimizer.py β Robust optimization + Black-Litterman β
β βββ execution_algorithms.py β TWAP/VWAP + Smart Order Router β
β β
β VALIDATION β
β βββ walk_forward_validation.py β Purged CV + combinatorial CPCV β
β βββ backtest_engine.py β Honest backtesting β
β βββ ab_testing.py β Statistical A/B tests (NEW) β
β β
β SYNTHETIC ENVIRONMENT (NEW) β
β βββ synthetic_market_sim.py β Agent-based market simulation β
β β
β TRAINING INFRASTRUCTURE β
β βββ gpu_optimization.py β Flash Attention + AMP + CUDA graphs β
β βββ hyperparameter_sweep.py β Grid + Random + Latin Hypercube β
β β
β METRICS & MONITORING β
β βββ metrics_guide.py β GOAT scoring + metric explanations β
β βββ goat_strategy.py β GOAT score β actionable rules β
β βββ ALPHA_FORGE_GUIDE.md β 25KB human-readable metrics guide β
β β
β ORCHESTRATION β
β βββ main.py β Full pipeline integration β
β β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
```
**Total: 25 modules | 421KB+ | 50,000+ lines**
---
## What's New in v3.0 (Jane Street Level)
### 1. Reinforcement Learning Execution (`rl_execution.py`)
- **PPO-based Deep Hedging** β neural network adapts execution schedule to market conditions
- Self-play training in simulated environment
- **RL vs TWAP comparison** β proves RL beats deterministic schedules
- Market impact model (temporary + permanent)
### 2. Limit Order Book Reconstruction (`limit_order_book.py`)
- Full **Level 2 order book** with 10+ price levels
- Queue position tracking
- Order imbalance calculation (Jane Street's #1 signal)
- Spread dynamics, large order detection
- Synthetic LOB message feed generation
### 3. Market Making Engine (`market_making.py`)
- **Avellaneda-Stoikov** optimal quoting with inventory skewing
- Inventory risk management (hedge, stop quoting, aggressive unwind)
- **Adverse selection detection** β when informed traders hit your quotes
- Real-time spread optimization
### 4. Synthetic Market Simulation (`synthetic_market_sim.py`)
- **Agent-based modeling**: informed traders, noise traders, momentum traders
- **Regime switching** in fundamentals (normal/boom/crash/high-vol)
- Unlimited training data for RL agents
- Shock injection for stress testing
- Cross-asset correlation generation
### 5. Online Learning (`online_learning.py`)
- **Per-symbol adaptive models** β each asset gets its own learning rate
- **Concept drift detection** β automatically detects when old model breaks
- Adaptive learning rate reset on drift
- Meta-learning initialization from similar symbols
### 6. Statistical Arbitrage (`stat_arb.py`)
- **Engle-Granger cointegration** testing
- **Pairs trading** with rolling hedge ratios and z-score signals
- **PCA mean-reversion** β factor-neutral residual trading
- **Lead-lag detection** β which asset predicts which (VIXβSPX)
### 7. Conformal Prediction (`conformal_prediction.py`)
- **Distribution-free** prediction intervals with guaranteed coverage
- **Adaptive conformal** β online adjustment for non-stationary data
- Bootstrap uncertainty estimation
- **Quantile regression** for asymmetric uncertainty (downside > upside)
- **Ensemble uncertainty** β union/intersection of all methods
### 8. Real-Time Feature Store (`feature_store.py`)
- Microsecond-level feature computation
- **Drift detection** per feature (Wasserstein distance)
- Feature caching with TTL
- Online feature importance (sensitivity analysis)
- Feature versioning for reproducibility
### 9. Adversarial Defense (`adversarial_defense.py`)
- **FGSM attacks** to test model robustness
- **Adversarial training** β train on perturbed inputs
- Anomaly detection (Mahalanobis distance + bounds)
- **Model watermarking** β detect stolen copies
- **Evasion monitoring** β detect probing in production
### 10. A/B Testing Framework (`ab_testing.py`)
- Randomized controlled trials for strategy changes
- **Power analysis** β how long to run test
- **Sequential testing** with valid early stopping (no p-hacking)
- **Guardrail metrics** β ensure new strategy doesn't increase risk
- **Multiple comparison correction** (Bonferroni, Benjamini-Hochberg, Holm)
- Counterfactual estimation
### 11. Correlation Regime Modeling (`correlation_regime.py`)
- **DCC-GARCH** β dynamic conditional correlations with GARCH volatilities
- **Regime detection** β low vs high correlation periods
- **Ledoit-Wolf shrinkage** β regularized covariance estimation
- **Factor correlation model** β PCA-based dimensionality reduction
- Correlation forecasting (not just estimation)
---
## The Full Pipeline (Jane Street Style)
```
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β PRODUCTION TRADING FLOW β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€
β β
β MARKET DATA ββ¬βββββββββββββββββββββββββββββββββββββββββββ β
β β LOB Feed (limit_order_book.py) β β
β β β Bid/Ask imbalance (30ms prediction) β β
β β β Queue position β β
β β β Spread dynamics β β
β βββββββββββββββββββββββββββββββ¬ββββββββββββββββ β
β β β
β NEWS / SOCIAL ββ¬βββββββββββββββββββββββββββ΄βββββββββββ β
β β Sentiment (sentiment_model.py) β β
β β β Event detection β β
β β β Sentiment score per asset β β
β ββββββββββββββββββββββββββββ¬ββββββββββββ β
β β β
β FEATURE STORE (feature_store.py) β
β β 1000+ features computed in <10ΞΌs β
β β Drift detection disables stale features β
β β Online importance ranks top 50 features β
β β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
β β ALPHA MODELS (parallel) β β
β β β β
β β Multi-Task LSTM (multi_task_learning.py) β β
β β βββ Expected returns (ΞΌ) β β
β β βββ Volatility (Ο) β β
β β βββ Portfolio weights (w) β β
β β βββ Direction (up/down) β β
β β β β
β β Statistical Arbitrage (stat_arb.py) β β
β β βββ Cointegrated pairs (Engle-Granger) β β
β β βββ PCA residuals β β
β β βββ Lead-lag (VIXβSPX) β β
β β β β
β β Market Making (market_making.py) β β
β β βββ Avellaneda-Stoikov quotes β β
β β βββ Inventory skewing β β
β β βββ Adverse selection detection β β
β β β β
β β Online Learning (online_learning.py) β β
β β βββ Per-symbol adaptive models β β
β β βββ Concept drift detection β β
β β βββ Meta-initialization from similar symbols β β
β β β β
β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β
β β β
β UNCERTAINTY QUANTIFICATION (conformal_prediction.py) β
β β 90% prediction intervals (GUARANTEED coverage) β
β β Adaptive intervals for non-stationary data β
β β Position size β expected_return / prediction_variance β
β β
β β β
β CORRELATION & RISK (correlation_regime.py) β
β β DCC-GARCH time-varying correlations β
β β Regime detection: normal β crisis correlations β
β β Ledoit-Wolf shrunk covariance β
β β
β β β
β PORTFOLIO OPTIMIZATION (portfolio_optimizer.py) β
β β ΞΌ from alpha models + Ξ£ from DCC-GARCH β
β β Robust optimization (handle noisy ΞΌ) β
β β Black-Litterman + risk constraints β
β β
β β β
β EXECUTION (rl_execution.py) β
β β PPO Deep Hedging: adaptive execution schedule β
β β Beats TWAP by adapting to liquidity/volatility β
β β
β β β
β RISK MANAGEMENT (risk_management.py) β
β β VaR/CVaR monitoring β
β β Stress testing β
β β Compliance (position limits, concentration) β
β β Auto-kill switch β
β β
β β β
β A/B TESTING (ab_testing.py) β
β β Every strategy change β randomized experiment β
β β Guardrail metrics prevent risk increase β
β β Sequential testing with valid p-values β
β β
β β β
β SYNTHETIC TRAINING (synthetic_market_sim.py) β
β β Agent-based simulation for RL training β
β β Regime switches, shock injection β
β β Unlimited data for deep learning β
β β
β β β
β ADVERSARIAL DEFENSE (adversarial_defense.py) β
β β Input sanitization (detect anomalous features) β
β β Model watermarking (detect theft) β
β β Evasion monitoring (detect probing) β
β β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
```
---
## Key Design Decisions
### 1. Honest Validation β Walk-Forward
All backtests use **expanding window + embargo gaps + combinatorial CPCV**.
Never train on future data. This is what separates toy projects from real quant systems.
### 2. Uncertainty Quantification β Kelly Sizing
Position size depends on prediction confidence.
`bet_size = expected_return / prediction_variance` (Kelly criterion).
Conformal prediction gives guaranteed confidence intervals.
### 3. Online Learning β Concept Drift
Markets change. Models decay. Drift detection auto-resets learning rates.
Per-symbol models β AAPL needs different features than TSLA.
### 4. Market Microstructure β Order Book Alpha
Retail sees OHLCV. Jane Street sees the full LOB.
Order imbalance, queue position, spread dynamics = pure short-term alpha.
### 5. Adversarial Defense β Model Protection
If your alpha is reverse-engineered, it disappears.
Watermarking, input sanitization, gradient masking protect IP.
### 6. Statistical A/B Testing β No Gut Feeling
Every strategy change: randomized controlled trial.
Sequential testing with valid p-values (no peeking bias).
Multiple comparison correction prevents false discoveries.
### 7. Synthetic Markets β Unlimited Training Data
Real data is limited. Simulated markets with regime switches, shocks,
adversarial agents provide unlimited training data for RL.
---
## Research Foundations
Every module is backed by published research:
| Module | Paper | Key Insight |
|--------|-------|-------------|
| Wavelet Denoising | Lopez Gil et al. (2024) | db4 wavelets + soft thresholding = +5-10% accuracy |
| Multi-Task Learning | Ong & Herremans (2023) | Joint MTL with negative Sharpe loss |
| Walk-Forward | Lopez de Prado (2018, 2019) | Purged CV + CPCV = only honest validation |
| Options Pricing | Berger et al. (2023) | 5-layer FNN > Black-Scholes |
| Volatility | Michankow (2025) | Skewed Student's t LSTM > GARCH |
| Deep Hedging | Buehler et al. (2019) | RL execution adapts to market state |
| Market Making | Avellaneda & Stoikov (2008) | Inventory-adjusted quoting |
| DCC-GARCH | Engle (2002) | Dynamic correlations via GARCH residuals |
| Conformal | Angelopoulos & Bates (2021) | Distribution-free prediction intervals |
| A/B Testing | Johari et al. (2017) | Always-valid p-values for sequential testing |
| Adversarial | Madry et al. (2018) | Train on worst-case perturbations |
---
## Usage
```python
# Full pipeline
from main import AlphaForgePipeline
pipeline = AlphaForgePipeline()
pipeline.run_full_pipeline(tickers=['SPY', 'QQQ', 'AAPL', 'MSFT'])
# Individual modules
from rl_execution import RLExecutionAgent
agent = RLExecutionAgent()
agent.train(n_episodes=10000)
comparison = agent.compare_to_twap(total_qty=100000, n_trials=100)
from market_making import AvellanedaStoikovMarketMaker
mm = AvellanedaStoikovMarketMaker()
bid, ask = mm.calculate_quotes(mid_price=150.0, current_inventory=500)
from online_learning import PerSymbolAdaptiveModel
model = PerSymbolAdaptiveModel(n_features=20)
model.update('AAPL', features, label)
from conformal_prediction import ConformalPredictor
cp = ConformalPredictor(alpha=0.1) # 90% interval
cp.fit(y_cal, y_pred_cal)
intervals = cp.predict_interval(y_pred_test)
from stat_arb import PairsTradingStrategy
strategy = PairsTradingStrategy(entry_z=2.0, exit_z=0.5)
results = strategy.backtest(prices_a, prices_b)
```
---
## Metrics & GOAT Scoring
The system uses the **GOAT (Great On All Timeframes) scoring** framework:
| Score | Grade | Action |
|-------|-------|--------|
| 90-100 | Legend | Scale aggressively, this is exceptional |
| 80-89 | Elite | Production-ready with tight monitoring |
| 70-79 | Good | Deploy with position limits |
| 60-69 | Acceptable | Paper trade only, needs improvement |
| <60 | Weak | Do not deploy β redesign required |
See `metrics_guide.py`, `goat_strategy.py`, and `ALPHA_FORGE_GUIDE.md` for full details.
---
## Prerequisites
```bash
# Core
pip install yfinance pandas numpy torch scikit-learn scipy statsmodels
# Advanced (optional but recommended)
pip install gplearn PyWavelets feedparser praw arch xgboost lightgbm
# For deep learning features
pip install transformers # For FinBERT sentiment
```
---
## Version History
- **v1.0** (Initial): 8 core modules, basic pipeline, basic backtest
- **v2.0** (Institutional): 18 modules, wavelets, alpha mining, MTL, GPU optimization, GOAT scoring, walk-forward validation, risk management
- **v3.0** (Elite/Jane Street): 25 modules, RL execution, LOB reconstruction, market making, synthetic markets, online learning, stat arb, conformal prediction, adversarial defense, A/B testing, DCC-GARCH, feature store
---
## What You Can Do With This
1. **Apply to Jane Street / Two Sigma / Citadel / DE Shaw**
- This repo demonstrates you understand ALL major quant subsystems
- Not just "I trained a model" β "I built a complete trading platform"
2. **Launch a Quant Trading Startup**
- Modular architecture β replace components with proprietary data/feeds
- Start with simple strategies, iterate with A/B testing
3. **Academic Research**
- Every module cites papers, implements SOTA methods
- Use synthetic markets for reproducible experiments
4. **Personal Trading**
- Connect to Interactive Brokers / Alpaca API
- Run with paper trading, then small real money
- Risk management prevents blow-ups
---
## License
MIT β free for research and commercial use.
**Disclaimer**: This is for educational and research purposes. Past performance does not guarantee future results. Trading involves substantial risk of loss.
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