Range Bound Trading

Page 83 of 220 · Trading bot field notes and system logs

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

Implementation

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

Code Structure

The code structure separates concerns: data ingestion, signal generation, risk checks, and order submission. This modularity enables testing and reuse across strategies.

# 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

Risk Considerations

Risk management for range bound trading addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.

Important: Always validate range bound trading logic on out-of-sample data before live deployment. Paper trading reveals execution issues that backtests miss.

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.

MetricValueBenchmark
Sharpe Ratio1.851.20
Max Drawdown-12.4%-18.0%
Win Rate58.2%52.0%
Profit Factor1.721.45