# Quant Engine

> 📈 Quant Engine

- Skill: `signal-execution-labs/quant-engine` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add signal-execution-labs/quant-engine`
- Raw SKILL.md: https://api.skillmd.com/api/skills/signal-execution-labs/quant-engine/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Signal-Execution-Labs (https://skillmd.com/u/signal-execution-labs)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/signal-execution-labs/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

```python
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

| Strategy | Type | Avg Return | Sharpe | Win Rate |
|----------|------|------------|--------|----------|
| Stat Arb | Market Neutral | 15-25% | 1.5-2.5 | 55-60% |
| Momentum | Trend | 20-40% | 1.0-2.0 | 45-55% |
| Mean Reversion | Counter-trend | 10-20% | 1.2-1.8 | 60-70% |
| Factor | Multi-factor | 15-30% | 1.5-2.5 | 50-60% |

## Configuration

```yaml
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