def run(self, config):
logger.init_logger(config.log_uri)
# Load market data
log.info('Loading data...')
data = market_data.load_market_data(config.tickers, config.ticker_types, config.data_sources, \
config.start_date, config.end_date, config.history_window, config.csv_data_uri)
log.info('Data loaded!')
print
# Create the trading algorithm w/o parameters
trading_algorithm = TradingAlgorithm.create_trading_algorithm(config.algorithm_uri, config.tickers, \
config.history_window)
# Setup and run the optimizer
optimizer = of.create_optimizer(
config.num_processors, config.optimizer_name, trading_algorithm,
config.commission, config.ticker_spreads,
config.optimization_metric, config.optimization_metric_ascending,
config.optimization_parameters, config.time_resolution)
log.info('Running the optimizer...')
optimizer.run(data, config.start_date, config.end_date)
log.info('Ran optimizer!')
print
return optimizer.results
def test_load_market_data_from_all_data_sources(self):
# Setup input values for all data sources
tickers = [
'INDEX_SPY',
'SPY',
]
ticker_types = [
'YAHOO',
'dummy',
]
data_sources = [
'Quandl',
'CSV',
]
start_date = pd.to_datetime('2015-12-01')
end_date = pd.to_datetime('2016-01-29')
history_window = 0
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)
# Check results
self.assertEqual(len(data), 2)
self.assertEqual(data['INDEX_SPY'].index[0], start_date)
self.assertEqual(data['INDEX_SPY'].index[-1], end_date)
self.assertEqual(data['SPY'].index[0], start_date)
self.assertEqual(data['SPY'].index[-1], end_date)
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 setUp(self):
# Initialize market data loading values
tickers = ['SPY']
ticker_types = ['']
data_sources = ['CSV']
start_date = pd.to_datetime('1993-01-29')
end_date = pd.to_datetime('2016-5-31')
history_window = 0
csv_data_uri = "support_files"
# Load market data and save for later use
data = market_data.load_market_data(tickers, ticker_types,
data_sources, start_date, end_date,
history_window, csv_data_uri)
self.test_series = data['SPY']['Adjusted Close']
def test_load_market_data_with_non_existent_data(self):
# Setup input values for all data sources
tickers = [
'DAX',
]
ticker_types = [
'dummy',
]
data_sources = [
'CSV',
]
start_date = pd.to_datetime('1987-08-27')
end_date = pd.to_datetime('1989-02-17')
history_window = 0
csv_data_uri = './support_files'
# Load market data
with self.assertRaises(ValueError):
data = market_data.load_market_data(tickers, ticker_types, data_sources, start_date, \
end_date, history_window, csv_data_uri)
def run(self, config):
logger.init_logger(config.log_uri)
# Estimate data count needed prior to first out-of-sample period
freq_factor = self._frequency_diff_factor(config.time_resolution,
config.sample_period)
data_request_history_window = (config.in_sample_periods *
freq_factor) + config.history_window
# Load market data
log.info('Loading data...')
data = market_data.load_market_data(config.tickers, config.ticker_types, config.data_sources, \
config.start_date, config.end_date, data_request_history_window, config.csv_data_uri)
log.info('Data loaded!')
print
# Create the trading algorithm w/o parameters
trading_algorithm = TradingAlgorithm.create_trading_algorithm(config.algorithm_uri, config.tickers, \
config.history_window)
# Create the optimizer
optimizer = of.create_optimizer(config.num_processors, config.optimizer_name, trading_algorithm, config.commission, \
config.ticker_spreads, config.optimization_metric, config.optimization_metric_ascending, config.optimization_parameters,
config.time_resolution)
# Create the backtester
backtester = b.Backtester(-1, trading_algorithm, config.cash,
config.commission, config.ticker_spreads)
# Setup and run the walk forward analyzer
walk_forward_analyzer = WalkForwardAnalyzer(config.in_sample_periods, config.out_of_sample_periods, config.sample_period, \
optimizer, backtester)
log.info('Running the walk forward analyzer...')
walk_forward_analyzer.run(data, config.start_date, config.end_date,
config.cash)
log.info('Ran the walk forward analyzer!')
print
return walk_forward_analyzer.results
def run(self, config):
logger.init_logger(config.log_uri)
# Load market data
log.info('Loading data...')
data = market_data.load_market_data(config.tickers, config.ticker_types, config.data_sources, \
config.start_date, config.end_date, config.history_window, config.csv_data_uri)
log.info('Data loaded!')
print
# Create the trading algorithm
trading_algorithm = TradingAlgorithm.create_trading_algorithm(config.algorithm_uri, config.tickers, \
config.history_window, config.algorithm_parameters)
# Setup and run the backtester
backtester = Backtester(0, trading_algorithm, config.cash,
config.commission, config.ticker_spreads)
log.info('Running the backtester...')
backtester.run(data, config.start_date, config.end_date)
log.info('Ran backtester!')
print
return backtester.results
def test_load_market_data_with_same_ticker(self):
# Setup input values for all data sources
tickers = [
'SPY',
'SPY',
]
ticker_types = [
'dummy',
'dummy',
]
data_sources = [
'CSV',
'CSV',
]
start_date = pd.to_datetime('2015-12-01')
end_date = pd.to_datetime('2016-01-29')
history_window = 0
csv_data_uri = './support_files'
# Load market data
with self.assertRaises(ValueError):
data = market_data.load_market_data(tickers, ticker_types, data_sources, start_date, \
end_date, history_window, csv_data_uri)