feat: Add core trading modules for risk management, backtesting, and execution algorithms, alongside a new ML transparency widget and related frontend dependencies.

docs/user_manual/backtesting.md

@@ -49,14 +49,57 @@ After a backtest completes, you can export the results:
 
 Both exports are automatically named with the current date for easy organization.
 
+## Advanced Backtesting Features
+
+### Walk-Forward Analysis
+
+Walk-forward analysis provides robust parameter optimization by using rolling windows:
+
+1. **Training Period**: Strategy parameters are optimized on training data (e.g., 90 days)
+2. **Testing Period**: Optimized parameters are tested on out-of-sample data (e.g., 30 days)
+3. **Rolling Window**: Window advances by step size (e.g., 30 days) and process repeats
+
+This method prevents overfitting and provides more realistic performance estimates than single-period optimization.
+
+**Benefits**:
+- Prevents overfitting to historical data
+- Tests strategy robustness across different market conditions
+- Provides confidence intervals for parameter estimates
+- Validates strategy performance on unseen data
+
+### Monte Carlo Simulation
+
+Monte Carlo simulation tests strategy robustness by running thousands of random scenarios:
+
+- **Random Parameter Variation**: Tests strategy performance across parameter ranges
+- **Statistical Analysis**: Provides distribution of returns, Sharpe ratios, and drawdowns
+- **Confidence Intervals**: Shows expected performance ranges (e.g., 95% confidence)
+- **Risk Assessment**: Identifies worst-case scenarios and tail risks
+
+Use Monte Carlo simulation to:
+- Validate strategy robustness
+- Assess parameter sensitivity
+- Understand potential downside risks
+- Estimate performance under various market conditions
+
 ## Parameter Optimization
 
-Parameter optimization allows you to automatically find the best strategy parameters. This feature requires backend API support and will be available once the optimization endpoints are implemented.
+Parameter optimization allows you to automatically find the best strategy parameters using multiple algorithms:
 
-The UI includes an information card explaining this feature. When available, you'll be able to:
+- **Grid Search**: Exhaustive search across parameter grid (best for small parameter spaces)
+- **Bayesian Optimization**: Efficient exploration using Gaussian process (best for expensive evaluations)
+- **Genetic Algorithms**: Evolutionary search that finds good solutions efficiently
+
+Optimization metrics include:
+- Sharpe Ratio (risk-adjusted returns)
+- Total Return
+- Maximum Drawdown
+- Win Rate
+
+When available via the backend API, you'll be able to:
 - Select parameters to optimize
 - Set parameter ranges
-- Choose optimization method (Grid Search, Genetic Algorithm, Bayesian Optimization)
+- Choose optimization method
 - View optimization progress
 - Compare optimization results