A research-backed, modular, institutional-grade quantitative trading framework supporting 9 global markets.

Built for the Build with K2 Think V2 Challenge by MBZUAI.

🚀 Quick Start

bash

git clone https://huggingface.co/Premchan369/alphaforge-quant-system
pip install -r requirements.txt
python main.py --mode full --tickers SPY QQQ AAPL

📊 Live Demo

AlphaForge x K2 Think V2 — Interactive Gradio Space

Features: real-time multi-market analysis (US, UK, DE, JP, CN, IN, Crypto, Forex, Commodities), AI deep analysis, cross-market portfolio optimization, and direct AI chat.

🌍 Multi-Market Coverage

Market	Suffix	Examples	Currency	Session
🇺🇸 US Equities	(none)	AAPL, TSLA, SPY, NVDA	USD	09:30-16:00 ET
🇬🇧 UK Equities	.L	SHEL.L, ULVR.L, AZN.L	GBP	08:00-16:30 GMT
🇩🇪 Germany Equities	.DE	SAP.DE, SIE.DE, ALV.DE	EUR	09:00-17:30 CET
🇯🇵 Japan Equities	.T	7203.T, 9984.T, 6758.T	JPY	09:00-15:00 JST
🇨🇳 China Equities	.SS/.SZ	600519.SS, 000858.SZ	CNY	09:30-15:00 CST
🇮🇳 India Equities	.NS	RELIANCE.NS, TCS.NS, INFY.NS	INR	09:15-15:30 IST
₿ Crypto	-USD	BTC-USD, ETH-USD, SOL-USD	USD	24/7
💱 Forex	=X	EURUSD=X, GBPUSD=X, USDJPY=X	USD	24/5
🛢 Commodities	=F	GC=F, CL=F, SI=F	USD	08:20-13:30 ET

Cross-Market Portfolio Optimization

The system supports mixed-asset portfolios across all markets simultaneously:

Example: AAPL (US) + BTC-USD (Crypto) + EURUSD=X (Forex) + GC=F (Commodities) + SHEL.L (UK)

Auto-detection of market from symbol suffixes enables seamless multi-asset analysis.

🧠 What This Project Is

AlphaForge is an institutional-grade quantitative trading system built as a modular open-source Python framework. It was created to:

Predict multi-asset expected returns (μ) across 9 global markets
Analyze financial sentiment via FinBERT and LLM embeddings
Forecast volatility (σ) and covariance matrices (Σ)
Optimize cross-market portfolios with real-world constraints
Price options with ML (beating Black-Scholes)
Run honest backtests with walk-forward validation
Control drawdowns with CPPI and Kelly criterion
Guard against data snooping bias
Detect market regimes and adapt strategies
Measure liquidity risk and position capacity
Model transaction costs with market impact

🏗 Architecture

┌─────────────────────────────────────────────────────────────────┐
│                    MULTI-MARKET DATA LAYER                     │
│  US │ UK │ DE │ JP │ CN │ IN │ Crypto │ Forex │ Commodities │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                    MARKET-SPECIFIC NORMALIZATION                 │
│  Suffix handling │ Currency │ Session timing │ Local holidays   │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                    UNIFIED ANALYSIS PIPELINE                    │
│  Technical Indicators │ Regime Detection │ Risk Metrics       │
│  Position Sizing │ Liquidity Analysis │ Event Calendar          │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                    CROSS-MARKET PORTFOLIO                        │
│  Auto-detect market │ Mixed-asset optimization │ Tx cost model │
└─────────────────────────────────────────────────────────────────┘

📁 Module Overview (33+ Modules)

Module	Purpose	Research Basis
`market_data.py`	Multi-market OHLCV fetching with suffix normalization	Standard TA
`sentiment_model.py`	FinBERT / LLM embeddings for financial sentiment	Yang et al. 2020 (FinBERT)
`alpha_model.py`	XGBoost + LSTM expected return prediction	Gu et al. 2020
`volatility_model.py`	GARCH baseline + LSTM volatility forecasting	Michankow 2025
`portfolio_optimizer.py`	Mean-variance with constraints, Black-Litterman	Markowitz 1952
`options_model.py`	ML option pricing (5-layer FNN beats BS)	Berger et al. 2023
`backtest_engine.py`	Honest backtesting with transaction costs	Lopez de Prado 2018
`walk_forward_validation.py`	Expanding/sliding/purged/CPCV splits	Lopez de Prado 2018/2019
`wavelet_denoising.py`	Wavelet noise reduction for time series	Lopez Gil 2024
`alpha_mining.py`	Genetic programming + LLM-driven factor discovery	gplearn
`multi_task_learning.py`	Joint optimization: alpha + vol + portfolio	Ong & Herremans 2023
`execution_algorithms.py`	TWAP, VWAP, Smart Order Router, Almgren-Chriss	Almgren & Chriss 2001
`risk_management.py`	VaR/CVaR (hist/parametric/MC), stress tests	Jorion 2006
`market_microstructure.py`	Kyle's lambda, VPIN, Roll measure, OFI, Amihud	Kyle 1985
`hyperparameter_sweep.py`	Grid, random, Latin Hypercube sampling	Bergstra & Bengio 2012
`gpu_optimization.py`	Flash Attention, AMP, gradient checkpointing	PyTorch best practices
`rl_execution.py`	PPO-based Deep Hedging optimal execution	Buehler et al. 2019
`limit_order_book.py`	Level 2 LOB reconstruction, synthetic message feeds	Gould et al. 2013
`market_making.py`	Avellaneda-Stoikov quoting, adverse selection	Avellaneda & Stoikov 2008
`synthetic_market_sim.py`	Agent-based modeling, regime switching	LeBaron 2006
`online_learning.py`	Per-symbol adaptive models, concept drift	Gama et al. 2014
`stat_arb.py`	Cointegration, PCA mean-reversion, lead-lag	Gatev et al. 2006
`conformal_prediction.py`	Distribution-free prediction intervals	Shafer & Vovk 2008
`feature_store.py`	Microsecond feature computation, per-feature drift	Feature Store best practices
`adversarial_defense.py`	FGSM attacks, model watermarking, evasion monitoring	Goodfellow et al. 2015
`ab_testing.py`	Sequential testing, multiple comparison correction	Johari et al. 2022
`correlation_regime.py`	DCC-GARCH dynamic correlations, Ledoit-Wolf shrinkage	Engle 2002
`news_data_integration.py`	NewsAPI, RSS, GDELT, Reddit/StockTwits aggregation	Alternative data
`regime_detection.py`	HMM/GMM market regime classifier, regime-conditioned Sharpe	Hamilton 1989
`transaction_cost_model.py`	Square-root market impact, spread, fees, optimal participation	Almgren et al. 2005
`drawdown_control.py`	CPPI insurance, fractional Kelly, dynamic leverage	Perold & Sharpe 1988
`liquidity_risk.py`	Amihud illiquidity, Kyle's lambda, VPIN, position capacity	Amihud 2002
`data_snooping_guard.py`	White's Reality Check, FDR, Bonferroni/Holm	White 2000
`event_study.py`	Post-earnings drift, macro events, merger arbitrage	MacKinlay 1997
`cross_sectional_factors.py`	Fama-French 5-factor, momentum, quality, low-vol	Fama & French 2015
`factor_risk_model.py`	Barra-style multi-factor risk decomposition	Grinold & Kahn 2000

📈 Key Metrics & Scoring

Metric	Description	Target
Sharpe Ratio	Risk-adjusted return	> 1.0
Sortino Ratio	Downside risk-adjusted return	> 1.5
Information Coefficient (IC)	Predicted vs actual return correlation	> 0.05
Max Drawdown	Worst peak-to-trough decline	< -20%
VaR (95%)	Value at Risk	Reported
CVaR (95%)	Conditional VaR / Expected Shortfall	Reported
Calmar Ratio	Return / Max Drawdown	> 1.0
Win Rate	% of positive return days	Reported
Profit Factor	Gross profit / Gross loss	> 1.2
GOAT Score	Composite 0-100 scoring system	> 70
Regime-Conditioned Sharpe	Sharpe in current market regime	Contextual
Transaction Cost Drag	Annualized cost of trading	< 2%
Liquidity Score	Amihud illiquidity ranking	Reported
Kelly Fraction	Optimal leverage for growth	< 1.0 (practical)

📚 Research Foundation

Every major component is backed by published research:

Component	Citation	Key Finding
Wavelet Denoising	Lopez Gil 2024 (xLSTM-TS)	`db4` + soft thresholding
Multi-Task Learning	Ong & Herremans 2023 (MTL-TSMOM)	Joint MTL with negative Sharpe loss
Walk-Forward Validation	Lopez de Prado 2018/2019	Purged CV + combinatorial CPCV
Options Pricing	Berger et al. 2023	5-layer FNN beats Black-Scholes
Volatility	Michankow 2025	Skewed Student's t LSTM
RL Execution	Buehler et al. 2019	Deep Hedging (PPO)
Market Making	Avellaneda & Stoikov 2008	Inventory management
Correlation Regimes	Engle 2002	DCC-GARCH dynamic correlations
Regime Detection	Hamilton 1989	HMM for nonstationary time series
Transaction Costs	Almgren et al. 2005	Square-root market impact law
Drawdown Control	Perold & Sharpe 1988	CPPI dynamic asset allocation
Kelly Criterion	Thorp 2006	Fractional Kelly for practical trading
Liquidity Risk	Amihud 2002	Illiquidity premium via price impact ratio
Data Snooping	White 2000	Bootstrap reality check for multiple testing
Event Studies	MacKinlay 1997	Abnormal return methodology
Fama-French Factors	Fama & French 2015	5-factor asset pricing model
Factor Risk	Grinold & Kahn 2000	Multi-factor risk decomposition
Cross-Market Arbitrage	Gatev et al. 2006	Pairs trading with cointegration

🛠 Installation

Core Dependencies

pip install -r requirements.txt

Optional Dependencies (for advanced modules)

pip install gplearn PyWavelets feedparser praw arch requests

GPU Support

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

📖 Usage

Single Market Analysis

bash

# US Equity
python main.py --mode full --tickers AAPL --market US

# UK Equity
python main.py --mode full --tickers SHEL.L --market UK

# Crypto
python main.py --mode full --tickers BTC-USD --market Crypto

# Forex
python main.py --mode full --tickers EURUSD=X --market Forex

Cross-Market Portfolio Optimization

bash

# Mixed-asset portfolio across 4 markets
python main.py --mode portfolio --tickers AAPL,BTC-USD,EURUSD=X,GC=F,SHEL.L

Walk-Forward Backtest

python main.py --mode walkforward --tickers AAPL TSLA NVDA --market US

🤝 Contributing

This is an open-source project. Contributions welcome:

Fork the repository
Create a feature branch
Submit a PR with tests
Follow the research-first philosophy

📝 License

MIT License — see LICENSE

🙏 Acknowledgments

Built for the Build with K2 Think V2 Challenge by MBZUAI
K2 Think V2 model by MBZUAI-IFM
Research inspiration from Marcos Lopez de Prado, Avellaneda & Stoikov, and the quantitative finance community

Built by Premchan | AlphaForge v3.1 | 33+ Quant Modules | 9 Global Markets | Institutional-Grade Trading