Strategy Optimization¶

Find optimal parameters using grid search and Bayesian optimization.

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📋 Notebook Information

• RustyBT Version: 0.1.2+
• Last Validated: 2025-11-07
• API Compatibility: Verified ✅
• Documentation: API Reference

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In [1]:

from rustybt.analytics import setup_notebook

setup_notebook()

from rustybt.analytics import setup_notebook setup_notebook()

✅ Notebook environment configured successfully
   - Async/await support enabled
   - Pandas display options optimized
   - Progress bars configured

Grid Search Example¶

In [2]:

# Example: Grid search optimization
import numpy as np
from rustybt.optimization import (
    Optimizer,
    ParameterSpace,
    DiscreteParameter,
    ObjectiveFunction,
)
from rustybt.optimization.search import GridSearchAlgorithm

# Define parameter space
param_space = ParameterSpace(
    parameters=[
        DiscreteParameter(name='fast_period', min_value=10, max_value=30, step=10),
        DiscreteParameter(name='slow_period', min_value=50, max_value=100, step=25),
    ]
)

# Define backtest function
# This should run your strategy with the given parameters and return results
def backtest_with_params(params):
    """
    Run backtest with given parameters.
    
    Returns a dict with at least 'sharpe_ratio' key.
    In practice, this would call run_algorithm() with your strategy.
    """
    # Example: return results from run_algorithm()
    # results = run_algorithm(
    #     start=pd.Timestamp('2020-01-01', tz='utc'),
    #     end=pd.Timestamp('2023-12-31', tz='utc'),
    #     initialize=lambda context: MyStrategy().initialize(
    #         context, 
    #         fast_period=params['fast_period'],
    #         slow_period=params['slow_period']
    #     ),
    #     handle_data=MyStrategy().handle_data,
    #     capital_base=100000.0,
    #     bundle='yfinance',
    # )
    # return {'sharpe_ratio': results['sharpe'].iloc[-1]}
    
    # Placeholder return for demonstration
    return {
        'sharpe_ratio': np.random.uniform(0.5, 2.0),
        'total_return': np.random.uniform(0.1, 0.5),
        'max_drawdown': np.random.uniform(-0.3, -0.1),
    }

# Create optimizer
search_algo = GridSearchAlgorithm(parameter_space=param_space)
objective = ObjectiveFunction(metric="sharpe_ratio")

optimizer = Optimizer(
    parameter_space=param_space,
    search_algorithm=search_algo,
    objective_function=objective,
    backtest_function=backtest_with_params,
    max_trials=12,  # 3 fast_period values * 4 slow_period values
)

# Run optimization
# best_result = optimizer.optimize()
# print(f"Best parameters: {best_result.parameters}")
# print(f"Best Sharpe ratio: {best_result.score}")

# Example: Grid search optimization import numpy as np from rustybt.optimization import ( Optimizer, ParameterSpace, DiscreteParameter, ObjectiveFunction, ) from rustybt.optimization.search import GridSearchAlgorithm # Define parameter space param_space = ParameterSpace( parameters=[ DiscreteParameter(name='fast_period', min_value=10, max_value=30, step=10), DiscreteParameter(name='slow_period', min_value=50, max_value=100, step=25), ] ) # Define backtest function # This should run your strategy with the given parameters and return results def backtest_with_params(params): """ Run backtest with given parameters. Returns a dict with at least 'sharpe_ratio' key. In practice, this would call run_algorithm() with your strategy. """ # Example: return results from run_algorithm() # results = run_algorithm( # start=pd.Timestamp('2020-01-01', tz='utc'), # end=pd.Timestamp('2023-12-31', tz='utc'), # initialize=lambda context: MyStrategy().initialize( # context, # fast_period=params['fast_period'], # slow_period=params['slow_period'] # ), # handle_data=MyStrategy().handle_data, # capital_base=100000.0, # bundle='yfinance', # ) # return {'sharpe_ratio': results['sharpe'].iloc[-1]} # Placeholder return for demonstration return { 'sharpe_ratio': np.random.uniform(0.5, 2.0), 'total_return': np.random.uniform(0.1, 0.5), 'max_drawdown': np.random.uniform(-0.3, -0.1), } # Create optimizer search_algo = GridSearchAlgorithm(parameter_space=param_space) objective = ObjectiveFunction(metric="sharpe_ratio") optimizer = Optimizer( parameter_space=param_space, search_algorithm=search_algo, objective_function=objective, backtest_function=backtest_with_params, max_trials=12, # 3 fast_period values * 4 slow_period values ) # Run optimization # best_result = optimizer.optimize() # print(f"Best parameters: {best_result.parameters}") # print(f"Best Sharpe ratio: {best_result.score}")