Quant Engine

📈 Quant Engine

K.I.T.'s Quantitative Trading Brain - Wall Street algorithms for everyone!

Features

📊 Statistical Arbitrage

• Pairs trading with cointegration
• Mean reversion on spreads
• Dynamic hedge ratios
• Z-score based entry/exit

🚀 Momentum Strategies

• Cross-sectional momentum
• Time-series momentum
• Momentum factor portfolios
• Breakout detection

📉 Mean Reversion

• Bollinger Band strategies
• RSI extreme detection
• VWAP reversion
• Overnight gap strategies

🎯 Factor Models

• Multi-factor alpha models
• Risk factor decomposition
• Factor rotation strategies
• Custom factor construction

🔬 Backtesting

• Walk-forward analysis
• Monte Carlo simulation
• Transaction cost modeling
• Slippage estimation

Usage

from quant_engine import QuantEngine

engine = QuantEngine()

# Statistical arbitrage
pairs = await engine.find_cointegrated_pairs(
    symbols=["BTC", "ETH", "SOL", "AVAX"],
    lookback=90  # days
)

for pair in pairs:
    print(f"{pair.asset1}/{pair.asset2}")
    print(f"  Cointegration: {pair.coint_pvalue:.4f}")
    print(f"  Hedge ratio: {pair.hedge_ratio:.4f}")
    print(f"  Current Z-score: {pair.zscore:.2f}")

# Get trading signal
signal = await engine.get_stat_arb_signal(
    pair=pairs[0],
    entry_zscore=2.0,
    exit_zscore=0.5
)

# Momentum strategy
momentum = await engine.momentum_scan(
    symbols=["BTC", "ETH", "SOL", "AVAX", "DOT"],
    lookback=20  # days
)

print(f"Top momentum: {momentum[0].symbol} ({momentum[0].return_pct:.1%})")

# Backtest strategy
results = await engine.backtest(
    strategy="mean_reversion",
    symbol="BTC/USDT",
    start_date="2023-01-01",
    end_date="2024-01-01"
)

print(f"Sharpe Ratio: {results.sharpe_ratio:.2f}")
print(f"Max Drawdown: {results.max_drawdown:.1%}")
print(f"Win Rate: {results.win_rate:.1%}")

Strategies

Configuration

quant_engine:
  stat_arb:
    entry_zscore: 2.0
    exit_zscore: 0.5
    stop_zscore: 4.0
    lookback: 90
    
  momentum:
    lookback_days: [5, 10, 20, 60]
    rebalance_freq: "weekly"
    top_n: 5
    
  mean_reversion:
    bollinger_period: 20
    bollinger_std: 2.0
    rsi_period: 14
    rsi_oversold: 30
    rsi_overbought: 70
    
  backtesting:
    initial_capital: 100000
    commission: 0.001
    slippage: 0.0005

Risk Metrics

• Sharpe Ratio
• Sortino Ratio
• Maximum Drawdown
• Value at Risk (VaR)
• Expected Shortfall (ES)
• Beta exposure

Dependencies

• numpy>=1.24.0
• pandas>=2.0.0
• scipy>=1.11.0
• statsmodels>=0.14.0
• scikit-learn>=1.3.0