def test_transaction_cost(self):
strat = BuyAndHoldStrategy()
sim = StockMarketStrategySim("test", datetime.date(2015,1,1), 1, strat)
self.assertEqual(sim._get_transaction_cost(1), 10.0,
"Minimum value test failed")
self.assertEqual(sim._get_transaction_cost(140000), 146.5,
"Near-max value test failed")
self.assertEqual(sim._get_transaction_cost(150000), 150,
"Max value test failed")
def grid_search(strategy_class, param_grid):
"""Performs a grid search of all possible combinations or parameters
in param_grid.
Prints the best combination to screen when done.
Input:
- strategy: concrete subclass of AbstractStrategy (not instantiated)
- param_grid: a lists of dicts in form: [{'kwarg': [options]}]
e.g. [{'rsi_days': [7, 14, 21], {'rsi_buy_threshold': [30]}]
The current maximum is 4 keyword args.
"""
highest = None
highest_params = {}
param_combinations, mapping = parse_grid_params(param_grid)
num_runs = len(param_combinations)
print("{} unique combinations of parameters found. Starting...".format(num_runs))
run_number = 1
for comb in param_combinations:
strategy_kwargs = {}
for indexer, arg_name in mapping.iteritems():
strategy_kwargs[arg_name] = comb[indexer]
strategy = strategy_class(**strategy_kwargs)
start_date = datetime.date(2014, 7, 1)
sim = StockMarketStrategySim("^GSPC", start_date, 10000, strategy).set_silent()
sim.run()
value = sim.get_current_value()
print("Run {}/{} completed, value at end: {:5.2f}".format(run_number, num_runs, value))
if value > highest:
highest = value
highest_params = strategy_kwargs
run_number += 1
print("Done! Best value: {}, best params: {}".format(highest, highest_params))
def test_value_update_long_position(self):
"""Tests if the values are correctly updated when holding a long
position."""
start_value = 1000
sim = StockMarketStrategySim("test", datetime.date(2015,1,1),
start_value, BuyAndHoldStrategy())
sim.run()
# The difference in points in the test csv is the following, assuming
# buy on day 1 (at halfway point between open and close) and no sell:
buy_val = (1005 + 1010) / 2.0
proc_diff = (1010 - buy_val) / buy_val
# Calculate end value. Private method in sim used here for test purposes
# only:
start_value_stock = start_value - sim._get_transaction_cost(start_value)
end_value = start_value_stock * (1+proc_diff)
# Using inequality check because of rounding differences and/or
# inaccuracies due to using floats precision.
assert abs(end_value - sim.get_current_value()) < .01, \
"End value {0} expected, got {1}".format(end_value,
sim.get_current_value())
def main():
strategy = BuyAndHoldStrategy()
start_date = datetime.date(2014,7,1)
sim = StockMarketStrategySim("^GSPC", start_date, 10000, strategy)
sim.run()
def main():
strategy = RsiStrategy(rsi_buy_threshold=30, rsi_sell_threshold=80)
start_date = datetime.date(2014,7,1)
sim = StockMarketStrategySim("^GSPC", start_date, 10000, strategy)
sim.run()
