metadata
license: mit
tags:
- quant-trading
- alpha-model
- portfolio-optimization
- volatility-forecasting
- sentiment-analysis
- machine-learning
- financial-ai
- k2-think-v2
- multi-market
- cross-asset
language:
- en
AlphaForge v3.1 โ Multi-Market Institutional-Grade Quantitative Trading System
A research-backed, modular, institutional-grade quantitative trading framework supporting 9 global markets.
Built for the Build with K2 Think V2 Challenge by MBZUAI.
๐ Quick Start
git clone https://huggingface.co/Premchan369/alphaforge-quant-system
pip install -r requirements.txt
python main.py --mode full --tickers SPY QQQ AAPL
๐ Live Demo
AlphaForge x K2 Think V2 โ 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.
๐ Multi-Market Coverage
| Market | Suffix | Examples | Currency | Session |
|---|---|---|---|---|
| ๐บ๐ธ US Equities | (none) | AAPL, TSLA, SPY, NVDA | USD | 09:30-16:00 ET |
| ๐ฌ๐ง UK Equities | .L | SHEL.L, ULVR.L, AZN.L | GBP | 08:00-16:30 GMT |
| ๐ฉ๐ช Germany Equities | .DE | SAP.DE, SIE.DE, ALV.DE | EUR | 09:00-17:30 CET |
| ๐ฏ๐ต Japan Equities | .T | 7203.T, 9984.T, 6758.T | JPY | 09:00-15:00 JST |
| ๐จ๐ณ China Equities | .SS/.SZ | 600519.SS, 000858.SZ | CNY | 09:30-15:00 CST |
| ๐ฎ๐ณ India Equities | .NS | RELIANCE.NS, TCS.NS, INFY.NS | INR | 09:15-15:30 IST |
| โฟ Crypto | -USD | BTC-USD, ETH-USD, SOL-USD | USD | 24/7 |
| ๐ฑ Forex | =X | EURUSD=X, GBPUSD=X, USDJPY=X | USD | 24/5 |
| ๐ข 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.
๐ง 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 (ฮผ) across 9 global markets
- Analyze financial sentiment via FinBERT and LLM embeddings
- Forecast volatility (ฯ) and covariance matrices (ฮฃ)
- 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
๐ Architecture
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ MULTI-MARKET DATA LAYER โ
โ US โ UK โ DE โ JP โ CN โ IN โ Crypto โ Forex โ Commodities โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ
โผ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ MARKET-SPECIFIC NORMALIZATION โ
โ Suffix handling โ Currency โ Session timing โ Local holidays โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ
โผ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ UNIFIED ANALYSIS PIPELINE โ
โ Technical Indicators โ Regime Detection โ Risk Metrics โ
โ Position Sizing โ Liquidity Analysis โ Event Calendar โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ
โผ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ CROSS-MARKET PORTFOLIO โ
โ Auto-detect market โ Mixed-asset optimization โ Tx cost model โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ 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 |
๐ 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) |
๐ 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 |
๐ 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
๐ Usage
Single Market Analysis
# 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
# 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
๐ค 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
๐ License
MIT License โ see LICENSE
๐ 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