def add_pnl_stats(self, period_stats):
"""
Save p&l stats.
Parameters
----------
period_stats
Returns
-------
"""
starting = period_stats['starting_cash']
current = period_stats['portfolio_value']
appreciation = (current / starting) - 1
perc = (appreciation * 100) if current != 0 else 0
log.debug('adding pnl stats: {:6f}%'.format(perc))
df = pd.DataFrame(
data=[dict(performance=perc)],
index=[period_stats['period_close']]
)
self.pnl_stats = pd.concat([self.pnl_stats, df])
save_algo_df(self.algo_namespace, 'pnl_stats', self.pnl_stats)
def add_exposure_stats(self, period_stats):
"""
Save exposure stats.
Parameters
----------
period_stats
Returns
-------
"""
data = dict(
long_exposure=period_stats['long_exposure'],
base_currency=period_stats['ending_cash']
)
log.debug('adding exposure stats: {}'.format(data))
df = pd.DataFrame(
data=[data],
index=[period_stats['period_close']],
)
self.exposure_stats = pd.concat([self.exposure_stats, df])
save_algo_df(
self.algo_namespace, 'exposure_stats', self.exposure_stats
)
def add_custom_signals_stats(self, period_stats):
log.debug('adding custom signals stats: {}'.format(self.recorded_vars))
df = pd.DataFrame(
data=[self.recorded_vars],
index=[period_stats['period_close']],
)
self.custom_signals_stats = pd.concat([self.custom_signals_stats, df])
save_algo_df(self.algo_namespace, 'custom_signals_stats',
self.custom_signals_stats)
