def test_grid_search_optimizer_as_expected(self):
# Initialize market data loading values
tickers = ['SPY']
ticker_types = ['']
data_sources = ['CSV']
start_date = pd.to_datetime('2016-01-01')
end_date = pd.to_datetime('2016-5-31')
history_window = 20
csv_data_uri = "support_files"
# Load market data
data = market_data.load_market_data(tickers, ticker_types,
data_sources, start_date, end_date,
history_window, csv_data_uri)
# Initialize grid search optimizer values
algorithm_uri = "support_files/MovingAverageDivergenceAlgorithm.py"
num_processors = 4
commission = 0.0
ticker_spreads = [0.0001]
optimizer_name = "GridSearchOptimizer"
optimization_metric = "sharpe_ratio"
optimization_metric_ascending = True
optimization_parameters = {
"ma_long_window": [10, 20, 2],
"ma_short_window": [2, 5, 2],
"open_long": [-0.25, -0.25, 1],
"close_long": [0.4, 0.4, 1]
}
time_resolution = "daily"
# Create trading algorithm
trading_algorithm = TradingAlgorithm.create_trading_algorithm(
algorithm_uri, tickers, history_window, None)
# Setup and run the optimizer
optimizer = of.create_optimizer(num_processors, optimizer_name,
trading_algorithm, commission,
ticker_spreads, optimization_metric,
optimization_metric_ascending,
optimization_parameters,
time_resolution)
results = optimizer.run(data, start_date, end_date)
# Manually compute optimal parameters
opt_params = Optimizer.get_optimal_parameters(
results.backtest_results, optimization_metric,
results.parameter_sets, optimization_metric_ascending,
time_resolution)
# Check results
self.assertEqual(opt_params, results.optimal_parameters)
self.assertTrue(
len(results.backtest_results) == len(results.parameter_sets))
self.assertEqual(4, len(results.backtest_results))
def run(self, data, start_date, end_date):
# Prepare input data for running parallel backtests
backtest_args = itertools.izip(
range(len(self.optimization_parameter_sets)),
self.optimization_parameter_sets,
itertools.repeat(self.trading_algorithm),
itertools.repeat(self.commission),
itertools.repeat(self.ticker_spreads), itertools.repeat(data),
itertools.repeat(start_date), itertools.repeat(end_date))
# Run all backtest scenarios in parallel
pool = mp.Pool(processes=self.num_processors)
backtest_results = pool.map(func=_backtest, iterable=backtest_args)
# Find optimal parameters
optimal_parameters = Optimizer.get_optimal_parameters(backtest_results, self.optimization_metric, \
self.optimization_parameter_sets, self.optimization_metric_ascending, self.frequency)
# Save results
self.results = OptimizationResults(backtest_results,
optimal_parameters,
self.optimization_parameter_sets)
return self.results
