Pair Trading Strategy

Purpose

Select two highly correlated instruments (such as stocks from the same industry or BTC/ETH), monitor how far their price ratio (or spread) deviates from the mean, and trade against extreme deviations while waiting for mean reversion.

Signal Logic

1. Compute the price ratio: ratio = close_A / close_B
2. Rolling mean and standard deviation: mean = ratio.rolling(lookback).mean(), std = ratio.rolling(lookback).std()
3. Z-score: z = (ratio - mean) / std
4. Signal generation:
   • Z < -entry_z → long A, short B (ratio is too low, expected to revert)
   • Z > +entry_z → short A, long B (ratio is too high, expected to revert)
   • |Z| < exit_z → close the position (reverted back near the mean)

Implementation Notes

• Pair trading requires exactly two instruments (codes array length = 2)
• The first instrument is A (leg1), and the second is B (leg2)
• Signals for A and B are opposite: when A is long, B is short, and vice versa
• Equal-weight allocation only: A and B each take 50% of capital, with no precise hedge-ratio calculation

Parameters

Parameter	Default	Description
lookback	60	Lookback window for mean and standard deviation
entry_z	2.0	Entry Z-score threshold
exit_z	0.5	Exit Z-score threshold

Example config.json

{
  "source": "tushare",
  "codes": ["601318.SH", "601628.SH"],
  "start_date": "2023-01-01",
  "end_date": "2024-12-31",
  "initial_cash": 1000000,
  "commission": 0.001,
  "extra_fields": null
}

Cryptocurrency version:

{
  "source": "okx",
  "codes": ["BTC-USDT", "ETH-USDT"],
  "start_date": "2024-01-01",
  "end_date": "2024-12-31",
  "initial_cash": 1000000,
  "commission": 0.001,
  "extra_fields": null
}

Common Pitfalls