Sensitivity Analysis

Implementation

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

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.

Configuration Example

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

# Example configuration for Sensitivity Analysis
strategy:
  name: sensitivity-analysis
  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 sensitivity analysis addresses position limits, stop loss placement, correlation exposure, and worst-case drawdown scenarios. Regular monitoring prevents drift from intended behavior.

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 Sensitivity Analysis
strategy:
  name: sensitivity-analysis
  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