Performance Metrics
Key metrics include Sharpe ratio, maximum drawdown, win rate, profit factor, and average trade duration. Compare against baseline strategies and adjust parameters based on out-of-sample results.
| Metric | Value | Benchmark |
|---|---|---|
| Sharpe Ratio | 1.85 | 1.20 |
| Max Drawdown | -12.4% | -18.0% |
| Win Rate | 58.2% | 52.0% |
| Profit Factor | 1.72 | 1.45 |
Configuration Example
Configuration for pairs trading algorithm uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.
# Example configuration for Pairs Trading Algorithm
strategy:
name: pairs-trading-algorithm
timeframe: 5m
indicators:
- rsi
- ema_fast
- ema_slow
entry_conditions:
- rsi_oversold
- ema_crossover_bullish
exit_conditions:
- rsi_overbought
- stop_loss_hit
risk:
max_position_size: 0.02
stop_loss_pct: 0.015
Overview
This guide covers pairs trading algorithm implementation in automated trading systems. The approach combines established patterns with operational considerations drawn from production deployments.
Core Concepts
Understanding pairs trading algorithm requires familiarity with market microstructure, order flow dynamics, and execution latency. The following sections detail each component.
Production Notes
In production, pairs trading algorithm requires monitoring for API rate limits, database connection pooling, log rotation, alert thresholds, and automated restart procedures. Document runbooks for incident response.