def calculate_backtest_performance(self):
# outputs a dataset with entries a) net_asset_value (a series) and b) holdings: a holdings dataframe
net_asset_value_series = pd.Series()
net_asset_value_series.loc[self.bt_dt_index[0]] = self.aum
holdings_df = self.holdings
for date in self.dt_df.index:
# get previous date
date_previous = self.dt_df.dt_previous.loc[date]
# calculate pnl per instrument
self.pnl_per_instrument.loc[date] = self._calculate_instruments_pnl(self.dt_df.loc[date])
net_asset_value_now = self._update_net_asset_value(
self.dt_df.loc[date], net_asset_value_series.loc[date_previous], holdings_df.loc[date_previous])
# update nav at date t
net_asset_value_series.loc[date] = net_asset_value_now
# don't update holdings at last date as we have no open t+1 price
# if date != self.trading_dt_index[-1]:
if date != self.dt_df.index[-1]:
holdings_df.loc[date] = self._update_holdings(
self.dt_df.loc[date], holdings_df.loc[date_previous], self.aum)
if date.is_year_end and date.time().hour is 23:
print("\nStrategy value for date " + str(date) + " for strategy calculated \n")
backtest_dataset = DataSet()
backtest_dataset.holdings = holdings_df
backtest_dataset.net_asset_value = net_asset_value_series
self._backtest = backtest_dataset
return self._backtest
def calculate_backtest_performance(self):
"""
:return: returns a DataSet object with 2 keys: net_asset_value (pd.Series), holdings (pd.DataFrame)
"""
net_asset_value_series = pd.Series()
net_asset_value_series.loc[self.bt_dt_index[0]] = self.aum
holdings_df = self.holdings
for date in self.dt_df.index:
# get previous date
date_previous = self.dt_df.dt_previous.loc[date]
# calculate pnl per instrument
self.pnl_per_instrument.loc[date] = self._calculate_instruments_pnl(self.dt_df.loc[date])
net_asset_value_now = self._update_net_asset_value(
self.dt_df.loc[date], net_asset_value_series.loc[date_previous], holdings_df.loc[date_previous])
# update nav at date t
net_asset_value_series.loc[date] = net_asset_value_now
# don't update holdings at last date as we have no open t+1 price
if date != self.dt_df.index[-1]:
holdings_df.loc[date] = self._update_holdings(
self.dt_df.loc[date], holdings_df.loc[date_previous], net_asset_value_now)
if date.is_year_end and date.time().hour is 23:
print("\nStrategy value for date " + str(date) + " for strategy calculated \n")
backtest_dataset = DataSet()
backtest_dataset.holdings = holdings_df.resample(self.frequency).last()
backtest_dataset.net_asset_value = net_asset_value_series.resample(self.frequency).last()
backtest_dataset.cumulative_pnl_per_instrument = self._get_pnl_attribution()
self._backtest = backtest_dataset
return self._backtest