Abstract
Pairs trading, a quantitative strategy capitalizing on mean-reverting price spreads between correlated assets, has been widely applied to traditional markets like stocks and commodities. This study evaluates its effectiveness in cryptocurrency markets by comparing six pairs selection methods:
- Statistical models: Distance, Correlation, Cointegration, Stochastic Differential Residual (SDR)
- Evolutionary algorithms: Genetic Algorithm (GA), Non-Dominated Sorting Genetic Algorithm II (NSGA-II)
Using high-frequency data (1-, 5-, and 60-minute intervals) for 26 Binance-listed cryptocurrencies, NSGA-II outperformed all methods with an average return of 2.84% over 79 trading days (annualized ~13.8%). Among statistical models, SDR ranked highest (1.63%), while Correlation yielded negative returns (−0.48%). Z-tests confirmed significant performance differences (p < 0.01), establishing NSGA-II as the optimal choice for crypto pairs trading.
Methodology
1. Pairs Selection Techniques
Statistical Methods
- Euclidean Distance: Selects pairs with minimal normalized price divergence.
- Cointegration (ADF Test): Identifies long-term equilibrium relationships between assets.
- Correlation: Filters pairs with high price-movement synchrony (ρ > 0.8).
- SDR: Models residual spreads using CAPM/APT theory to capture arbitrage opportunities.
Evolutionary Algorithms
- GA: Single-objective optimization for portfolio selection.
- NSGA-II: Multi-objective optimization balancing returns and risk via non-dominated sorting.
2. Trading Strategy
Bollinger Bands: Triggers trades when spreads exceed ±2σ from the moving average.
- Long undervalued/short overvalued assets upon divergence.
- Close positions when spreads revert to the mean or at session end.
3. Data & Experiment Design
- Dataset: 26 cryptocurrencies (top 30% by liquidity) from Binance (Jan–Mar 2018).
- Parameters: $1,000 initial capital per trade; 5-unit rolling window for SMA/STD.
Results and Discussion
Key Findings
| Metric | NSGA-II | SDR | Distance | Cointegration | GA | Correlation |
|---|---|---|---|---|---|---|
| Avg. Return (%) | 2.84 | 1.63 | 1.42 | 0.89 | 0.86 | −0.48 |
| Max Drawdown | 1.40 | 0.72 | 0.08 | 0.38 | 0.46 | 0.64 |
- NSGA-II achieved superior returns across all frequencies (1min: 3.59%, 5min: 2.68%, 60min: 2.24%) with controlled risk.
- Correlation failed due to transient price dependencies lacking mean-reversion.
- SDR’s robust risk-adjusted returns highlight its viability for crypto markets.
Statistical Significance: Z-tests confirmed NSGA-II’s outperformance (p < 0.01) against other methods.
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FAQs
Q1: Why is NSGA-II better than traditional statistical methods?
A: NSGA-II optimizes multiple objectives (returns, risk, liquidity) simultaneously, adapting better to crypto market volatility compared to single-metric approaches like correlation.
Q2: How does high-frequency data impact pairs trading?
A: Shorter intervals (e.g., 1min) capture more arbitrage opportunities but require low-latency execution and tighter risk controls.
Q3: Can pairs trading be automated for cryptocurrencies?
A: Yes, algorithmic frameworks using Bollinger Bands or Kalman Filters can automate trade execution, though backtesting is critical.
Q4: What are the liquidity risks in crypto pairs trading?
A: Thinly traded altcoins may suffer slippage. Stick to top-volume assets (e.g., BTC, ETH pairs) to mitigate this.