def load_exodata(self):
if 'exo_storage' in self.context['strategy']:
storage = self.context['strategy']['exo_storage']
self.data, self.exo_dict = storage.load_series(self.exo_name)
if self.data is None:
raise KeyError(
"EXO ('{0}') not found in EXOStorage engine".format(
self.exo_name))
del self.context['strategy']['exo_storage']
else:
if os.path.exists(self.exo_name):
self.data, self.exo_dict = matlab.loaddata(self.exo_name)
else:
TMQRPATH = os.getenv("TMQRPATH", '')
self.data, self.exo_dict = matlab.loaddata(
os.path.join(TMQRPATH, 'mat',
self.exo_name.replace('.mat', '') + '.mat'))
exit_rule = (CrossDown(px, trailing_stop)) # Cross down for longs
elif direction == -1:
exit_rule = (CrossUp(px, trailing_stop)
) # Cross up for shorts, Cross down for longs
# Swarm_member_name must be *unique* for every swarm member
# We use params values for uniqueness
swarm_member_name = self.get_member_name(params)
#
# Calculation info
#
calc_info = None
if save_info:
calc_info = {'trailing_stop': trailing_stop, 'sp_df': sp_df}
return swarm_member_name, entry_rule, exit_rule, calc_info
if __name__ == "__main__":
#
# Run this code only from direct shell execution
#
#strategy = StrategyMACrossTrail()
#equity, stats = strategy.calculate()
# Do some work
data, info = matlab.loaddata('../mat/strategy_270225.mat')
data.plot()
def load_exodata(self):
if os.path.exists(self.exo_name):
self.data, self.exo_dict = matlab.loaddata(self.exo_name)
else:
self.data, self.exo_dict = matlab.loaddata('../mat/' +
self.exo_name + '.mat')
def from_matfile(cls, filename):
data, exo_info = matlab.loaddata(filename)
return cls(data, exo_info)
def test_laststate_update_handle_if_swarm_composition_is_empty_after_rebalance_with_costs(self, mock_get_costs):
STRATEGY_CONTEXT = {
'strategy': {
'class': StrategyMACrossTrail,
'exo_name': './mat/strategy_270225.mat',
'direction': -1,
'opt_params': [
# OptParam(name, default_value, min_value, max_value, step)
OptParamArray('Direction', [-1]),
OptParam('SlowMAPeriod', 20, 10, 40, 5),
OptParam('FastMAPeriod', 2, 5, 20, 5),
OptParam('MedianPeriod', 5, 2, 10, 1)
],
},
'swarm': {
'members_count': 3,
'ranking_class': RankerHighestReturns(return_period=1),
'rebalance_time_function': SwarmRebalance.every_friday
},
}
#
# Mocking the CostsManagerEXOFixed.get_costs
#
from backtester.matlab import loaddata
exo_df, info = loaddata('./mat/strategy_270225.mat')
mock_get_costs.return_value = pd.DataFrame({'rollover_costs': np.zeros(len(exo_df.index)),
'transaction_costs': np.ones(len(exo_df.index)) * 10},
index=exo_df.index)
swm_full = Swarm(STRATEGY_CONTEXT)
swm_full.strategy.data.at[pd.Timestamp('2016-03-04'), 'exo'] = swm_full.strategy.data.ix['2016-03-03']['exo']
swm_full.strategy.data.loc[:, 'delta'] = 1.0
swm_full.run_swarm()
swm_full.pick()
swm_start = Swarm(STRATEGY_CONTEXT)
swm_start.strategy.data.at[pd.Timestamp('2016-03-04'), 'exo'] = swm_full.strategy.data.ix['2016-03-03']['exo']
swm_start.strategy.data = swm_start.strategy.data.ix[:'2016-03-04']
swm_start.strategy.data.loc[:, 'delta'] = 1.0
swm_start.run_swarm()
swm_start.pick()
ctx = deepcopy(STRATEGY_CONTEXT)
ctx['strategy']['opt_preset'] = Swarm._parse_params(swm_start.last_members_list)
swm_next = Swarm(ctx)
swm_next.strategy.data.at[pd.Timestamp('2016-03-04'), 'exo'] = swm_full.strategy.data.ix['2016-03-03']['exo']
swm_next.strategy.data = swm_next.strategy.data.ix[:'2016-03-11']
swm_next.strategy.data.loc[:, 'delta'] = 1.0
swm_next.run_swarm()
# Make sure that old EXO price used
self.assertEqual(-2, swm_start.last_exposure)
self.assertEqual(swm_full.picked_equity.ix['2016-03-03'], swm_start.picked_equity.ix['2016-03-04'])
# After this run
with patch('warnings.warn') as mock_warn:
swm_start._laststate_update(swm_next.strategy.data, swm_next.raw_exposure.sum(axis=1), swm_next.strategy.costs)
self.assertTrue(mock_warn.called)
dt = '2016-03-04'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
dt = '2016-03-07'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
dt = '2016-03-08'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
dt = '2016-03-09'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
dt = '2016-03-10'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
dt = '2016-03-11'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
def test_laststate_update_real_with_costs(self, mock_get_costs):
from backtester.matlab import loaddata
STRATEGY_CONTEXT = {
'strategy': {
'class': StrategyMACrossTrail,
'exo_name': './mat/strategy_270225.mat',
'direction': -1,
'opt_params': [
# OptParam(name, default_value, min_value, max_value, step)
OptParamArray('Direction', [-1]),
OptParam('SlowMAPeriod', 20, 10, 40, 5),
OptParam('FastMAPeriod', 2, 5, 20, 5),
OptParam('MedianPeriod', 5, 2, 10, 1)
],
},
'swarm': {
'members_count': 3,
'ranking_class': RankerHighestReturns(return_period=1),
'rebalance_time_function': SwarmRebalance.every_friday
},
'costs': {
'manager': CostsManagerEXOFixed,
'context': {
'costs_options': 3.0,
'costs_futures': 3.0,
}
}
}
#
# Mocking the CostsManagerEXOFixed.get_costs
#
exo_df, info = loaddata('./mat/strategy_270225.mat')
mock_get_costs.return_value = pd.DataFrame({'rollover_costs': np.zeros(len(exo_df.index)),
'transaction_costs': np.ones(len(exo_df.index)) * 10}, index=exo_df.index)
swm_full = Swarm(STRATEGY_CONTEXT)
swm_full.strategy.data.loc[:, 'delta'] = 1.0
swm_full.run_swarm()
swm_full.pick()
swm_start = Swarm(STRATEGY_CONTEXT)
swm_start.strategy.data = swm_start.strategy.data.ix[:'2016-03-18']
swm_start.strategy.data.loc[:, 'delta'] = 1.0
swm_start.run_swarm()
swm_start.pick()
ctx = deepcopy(STRATEGY_CONTEXT)
ctx['strategy']['opt_preset'] = Swarm._parse_params(swm_start.last_members_list)
swm_next = Swarm(ctx)
swm_next.strategy.data = swm_next.strategy.data.ix[:'2016-03-25']
swm_next.strategy.data.loc[:, 'delta'] = 1.0
swm_next.run_swarm()
dt = '2016-03-18'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(np.isnan(swm_full.series['delta'].ix[dt]), np.isnan(swm_start.series['delta'].ix[dt]))
self.assertEqual(swm_start.last_exposure, swm_full.picked_exposure.sum(axis=1).ix['2016-03-18'])
# Updating swm_start (assuming that it was loaded from DB)
swm_start._laststate_update(swm_next.strategy.data, swm_next.raw_exposure.sum(axis=1), swm_next.strategy.costs)
dt = '2016-03-21'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
dt = '2016-03-22'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
dt = '2016-03-23'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
dt = '2016-03-24'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])
dt = '2016-03-25'
self.assertEqual(swm_full.series['equity'].ix[dt], swm_start.series['equity'].ix[dt])
self.assertEqual(swm_full.series['exposure'].ix[dt], swm_start.series['exposure'].ix[dt])
self.assertEqual(swm_full.series['costs'].ix[dt], swm_start.series['costs'].ix[dt])
self.assertEqual(swm_full.series['delta'].ix[dt], swm_start.series['delta'].ix[dt])