AlphaForge

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

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

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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
└── backfill_alternative_data.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/factor_pipeline/factors/my_factors.py
from src.factor_pipeline.registry import register_factor

@register_factor("my_momentum")
def my_momentum(df):
    return df["close"].pct_change(14)

2. Evaluate

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

3. Backtest

python run_backtest.py --config strategies/examples/momentum_example.yaml --days 90
python run_backtest.py --config strategies/examples/momentum_example.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

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