curve-fitting

One response to “curve-fitting”

1. 

   Leslie Chan

   05/04/2025

   To avoid curve-fitting when optimizing your algorithmic trading strategy in foreign exchange, follow these structured steps:

   1. Limit Parameter Complexity
   Reduce Parameters: Use the fewest parameters necessary, focusing on those with economic rationale (e.g., volatility-adjusted timeframes).

   Avoid Over-Optimization: Optimize parameters to a “robust range” rather than pinpoint values. For example, a 50–100-day moving average window instead of fixing it at 75 days.

   2. Rigorous Data Segmentation
   In-Sample vs. Out-of-Sample Testing:

   Split historical data chronologically (e.g., 70% for development, 30% for validation).

   Ensure the out-of-sample (OOS) data includes diverse market conditions (bull/bear markets, high/low volatility).

   Walk-Forward Analysis:

   Re-optimize parameters periodically on a rolling window (e.g., 2 years of data) and test on the next 6 months. Repeat across the dataset.

   3. Stability Checks
   Parameter Sensitivity Analysis:

   Test how small changes in parameters affect performance. Stable results across variations indicate robustness.

   Example: If a strategy works well with a 14-day RSI and a 16-day RSI, it’s less likely to be overfit.

   Monte Carlo Simulations:

   Randomize trade sequences or introduce synthetic noise to historical data. If performance remains consistent, the strategy is likely robust.

   4. Realistic Assumptions
   Include Costs: Factor in transaction costs, slippage, and liquidity constraints during backtesting.

   Use Risk-Adjusted Metrics:

   Optimize for metrics like Sharpe Ratio, Sortino Ratio, or maximum drawdown instead of raw profit.

   5. Avoid Overly Complex Models
   Simplify Rules: Prefer strategies with clear logic (e.g., trend-following) over complex machine learning models with opaque decision-making.

   Economic Alignment: Base parameters on macroeconomic factors (e.g., interest rate cycles, volatility regimes) instead of purely price-based patterns.

   6. Continuous Validation
   Live Forward Testing: Run the strategy in a demo account with real-time data to validate OOS results.

   Post-Deployment Monitoring: Track performance metrics and recalibrate if market conditions shift (e.g., regulatory changes, geopolitical events).