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.
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.
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).
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.
1H to Daily for traditional pairs trading. 1M-15M for crypto funding rate arb. Strategy-dependent — shorter timeframes require lower latency infrastructure.
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.
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.
Backtesting applies a trading strategy to historical data to evaluate how it would have performed, while Monte Carlo simulation randomises the order of those historical trades across thousands of iterations to understand the range of possible outcomes. Together, these techniques provide both an estimate of expected performance and a probabilistic view of risk, drawdown, and return distributions.
Execution algorithms such as VWAP (Volume-Weighted Average Price) and TWAP (Time-Weighted Average Price) are automated strategies designed to execute large orders with minimal market impact. VWAP algorithms slice orders to match historical volume patterns throughout the day, while TWAP algorithms distribute orders evenly over time. These tools help institutional and advanced traders achieve better average execution prices on large positions.
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.