5. Analyzing and Using Optimization Results

5.1. Result Analysis

  • Sort the result table by Sharpe ratio, drawdown, and other columns to compare stability and risk-return profiles across parameter sets.

  • If supported, plot parameter–objective relationships to inspect sensitivity and local optima.

5.2. Objectives and Constraints (with Brief Explanations)

  • Optimization objective: Currently supported goals such as maximizing Sharpe ratio, minimizing drawdown, or maximizing return; see configuration or API docs.

  • Constraints: If any (e.g., max drawdown limit or minimum trade count), set them in configuration; only feasible solutions participate in ranking.

5.3. Apply Best Parameters to the Strategy

Use op.set_parameter(stg_id, pars=best_pars) to apply optimized parameters to the Operator; the same Operator can then be used for backtesting (mode=1) or live trading without manual code changes.

5.4. Caveats

  • Overfitting: In-sample optimized parameters may underperform out of sample; use out-of-sample validation or rolling optimization.

  • In-sample vs out-of-sample: Use different date ranges for optimization and validation.

  • Compute time: Larger opti_sample_count or iteration counts take longer; balance accuracy against runtime.