Range Bound Trading

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.

Overview

This guide covers range bound trading implementation in automated trading systems. The approach combines established patterns with operational considerations drawn from production deployments.

Configuration Example

Configuration for range bound trading uses YAML or JSON format. Key parameters include timeframes, signal thresholds, position sizing rules, and risk limits.

# Example configuration for Range Bound Trading
strategy:
  name: range-bound-trading
  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

Common Issues

Common issues with range bound trading include look-ahead bias in backtesting, overfitting to historical regimes, inadequate handling of exchange downtime, and insufficient logging for debugging.

Implementation

Implementation of range bound trading follows a modular architecture. Configuration files define parameters, strategy modules handle logic, and exchange adapters manage execution.