Statistical Arbitrage
Overview
Statistical arbitrage (stat arb) exploits temporary mispricings between correlated assets using quantitative models. Pairs trading — the simplest form — goes long the underperformer and short the outperformer when their price ratio deviates beyond historical norms, profiting as the spread reverts to the mean.
Key Concepts
Pairs trading and spread analysis. Cointegration vs simple correlation. Z-score and Bollinger Band signals on the spread. Mean half-life estimation. Kalman filter for dynamic hedge ratios. Basket trading and sector-neutral portfolios. Funding rate arbitrage in crypto perpetuals.
Entry Signals
Spread Z-score exceeds ±2 standard deviations. Cointegration test confirms relationship is stationary. Entry volume on the diverging leg drying up (exhaustion). Funding rate extreme on crypto perps (long/short imbalance).
Exit Signals
Spread reverts to mean (Z-score crosses zero). Spread continues diverging past ±3 SD (stop-loss — relationship may be breaking). Cointegration p-value rises above 0.05 (pair is no longer cointegrated). Maximum holding period reached.
Best Timeframes
1H to Daily for traditional pairs trading. 1M-15M for crypto funding rate arb. Strategy-dependent — shorter timeframes require lower latency infrastructure.
Pro Tips
Cointegration is more important than correlation for pairs trading. Always test for cointegration using Engle-Granger or Johansen tests. Be aware that historically stable relationships can break permanently (regime change) — use stop-losses on the spread.
More Topics in This Category
Algorithmic Trading Strategies
Algorithmic trading uses computer programs to execute trades based on predefined rules — from simple moving average crossovers to complex machine learning models. The key advantage is removing emotion, achieving consistent execution speed, and backtesting strategies across decades of data before risking real capital.
Grid Trading
Grid trading places buy and sell limit orders at regular price intervals above and below the current market price, creating a 'grid' that profits from range-bound oscillations. Each buy order has a corresponding sell order at a higher grid level, generating profit from each completed round trip. It works best in sideways markets and can be automated easily.
Trend-Following Systems
Trend-following systems identify and ride sustained directional moves using rules-based approaches — moving average crossovers, breakout channels (Donchian, Keltner), and momentum filters. The strategy accepts many small losses for occasional outsized winners, relying on fat-tailed distribution of returns in financial markets.
Machine Learning in Trading
Machine learning applies statistical algorithms to financial market data to identify patterns, generate predictions, and optimise trading decisions without being explicitly programmed for each scenario. From simple linear regression to deep neural networks, machine learning models can process vast datasets, detect non-linear relationships, and adapt to changing market conditions — though they also carry significant risks of overfitting and data-snooping bias.