AlphaForge

End-to-End Factor-Driven Quant System for Crypto Perpetuals

Factor Mining → Statistical Evaluation → Combo Search → Walk-Forward Backtest → Paper Trade → Live

What is AlphaForge?

A production-grade quantitative trading framework for crypto perpetual contracts. Unlike toy backtesting scripts, AlphaForge covers the full lifecycle with a corrected execution model:

Your Factors ──→ Rolling IC / FDR Evaluation ──→ Factor Library
                                                       ↓
                                            Greedy Combo Search
                                                       ↓
              Walk-Forward Backtest ──→ Paper Trade ──→ Live
              (next-bar-open, funding fee,
               liquidation, slippage)

Bring your own alpha. The framework handles everything else.

Why AlphaForge?

| Problem | How AlphaForge Solves It | |---------|-------------------------| | Backtests look great, live trading loses money | Corrected engine: next-bar-open execution, funding fees, liquidation, slippage | | Sharpe ratio inflated 5-10x | Honest metrics: daily-frequency Sharpe, Newey-West t-stats, FDR correction | | Combining factors is guesswork | Greedy combo search: auto-find optimal combination by forward-stepping IC | | No systematic factor evaluation | Factor pipeline: register → evaluate → pass/fail FDR → auto-register | | Walk-forward is painful | Built-in: expanding window train/test split with OOS evaluation |

Architecture

src/
├── factor_pipeline/
│   ├── factors/               # Your factor implementations (pluggable)
│   ├── evaluator.py           # Rolling IC, FDR, Newey-West
│   ├── combo_search.py        # Greedy forward-step search
│   ├── registry.py            # Factor auto-discovery via decorator
│   └── pipeline.py            # CLI orchestrator
├── backtest.py                # Corrected backtest engine
├── strategy.py                # Config-driven signal generation
├── data_fetcher.py            # OKX + FRED + CryptoPanic
├── data_store.py              # Local data management
└── risk_guardrails.py         # Position limits, drawdown stops
scripts/ ├── realtime_collector.py      # Multi-source data collector ├── paper_trade.py             # Paper trading integration └── backfillalternativedata.py

Quick Start

git clone https://github.com/warren618/AlphaForge.git
cd AlphaForge
pip install -r requirements.txt
cp .env.example .env  # fill in your API keys

1. Write Your Factors

# src/factorpipeline/factors/myfactors.py
from src.factorpipeline.registry import registerfactor
@registerfactor("mymomentum") def my_momentum(df):     return df["close"].pct_change(14)

2. Evaluate

python -m src.factor_pipeline list                          # list registered factors
python -m src.factorpipeline eval mymomentum --days 90    # rolling IC + FDR
python -m src.factor_pipeline combo --method greedy --top 5 # find best combo

3. Backtest

python runbacktest.py --config strategies/examples/momentumexample.yaml --days 90
python runbacktest.py --config strategies/examples/momentumexample.yaml --walk-forward

Backtest Engine — Corrected Execution Model

Built after a painful audit that invalidated months of backtesting results.

| Feature | Naive Approach | AlphaForge | |---------|---------------|------------| | Execution price | Close price (look-ahead) | Next-bar open | | Sharpe frequency | Annualized from 5min bars | Daily-frequency | | Funding fees | Ignored | 8h funding rate deducted | | Liquidation | Ignored | Simulated with maintenance margin | | Position sizing | Fixed notional | Fixed-fractional | | Multiple testing | No correction | FDR (Benjamini-Hochberg) | | IC t-statistic | Standard t-test | Newey-West (HAC) |

Factor Pipeline

The pipeline supports any factor you write. Just decorate with @register_factor:

| Category | Description | |----------|-------------| | Momentum | Trend-following, breakout timing | | Mean Reversion | Statistical deviation, exhaustion patterns | | Volume/Price | Order flow, volume profile analysis | | Microstructure | Funding rate, open interest dynamics | | Cross-Asset | Inter-market correlation, macro sensitivity | | Derivatives | Options flow, basis, implied volatility | | On-Chain | Positioning, leverage metrics | | Macro | Economic indicators, sentiment |

Roadmap

• [ ] Multi-exchange support (Binance, Bybit)
• [ ] ML-based factor combination
• [ ] Real-time factor dashboard
• [ ] Slippage model calibration from live fills

License

MIT

_{Built by @warren618 — HKU MSc CS (Financial Computing)}