def _calculate_portfolio_returns_tms(self, tickers, open_to_open_returns_df: QFDataFrame,
exposure_values_df: QFDataFrame) \
-> SimpleReturnsSeries:
"""
SimpleReturnsSeries of the portfolio - for each date equal to the portfolio performance over the last
open-to-open period, ex. value indexed as 2010-02-15 would refer to the portfolio value change between
open at 14th and open at 15th, and would be based on the signal from 2010-02-13;
the first index of the series is the Day 3 of the backtest, as the first signal calculation occurs
after Day 1 (see ORDER OF ACTIONS below)
the last index of the series is test_end_date and the portfolio exposure is being set to zero
on the opening of the test_end_date
ORDER OF ACTIONS:
-- Day 1 --
signal is generated, based on the historic data INCLUDING prices from Day 1
suggested exposure for Day 2 is calculated
-- Day 2 --
a trade is entered, held or exited (or nothing happens) regarding the suggested exposure
this action is performed on the opening of the day
-- Day 3 --
at the opening the open-to-open return is calculated
now it is possible to estimate current portfolio value
the simple return of the portfolio (Day 3 to Day 2) is saved and indexed with Day 3 date
"""
open_to_open_returns_df = open_to_open_returns_df.dropna(how="all")
shifted_signals_df = exposure_values_df.shift(2, axis=0)
shifted_signals_df = shifted_signals_df.iloc[2:]
daily_returns_of_strategies_df = shifted_signals_df * open_to_open_returns_df
daily_returns_of_strategies_df = daily_returns_of_strategies_df.dropna(
axis=0, how='all')
daily_returns_of_strategies_df = cast_dataframe(
daily_returns_of_strategies_df,
SimpleReturnsDataFrame) # type: SimpleReturnsDataFrame
weights = Portfolio.one_over_n_weights(tickers)
# for strategies based on more than one ticker (ex. VolLongShort) use the line below:
# weights = QFSeries(np.ones(daily_returns_of_strategies_df.num_of_columns))
portfolio_rets_tms, _ = Portfolio.constant_weights(
daily_returns_of_strategies_df, weights)
return portfolio_rets_tms
def test_drifting_weights_alloc_not_fully_invested(self):
expected_df = self.alloc_for_not_fully_invested_drift_weights
_, actual_df = Portfolio.drifting_weights(self.assets_df,
self.weights_not_full_invest)
assert_dataframes_equal(expected_df,
actual_df,
absolute_tolerance=1e-06)
def test_drifting_weights_not_fully_invested(self):
expected_series = self.rets_for_not_fully_invested_drift_weights
actual_series, _ = Portfolio.drifting_weights(
self.assets_df, self.weights_not_full_invest)
assert_series_equal(expected_series,
actual_series,
absolute_tolerance=1e-06)
def _calculate_box(
asset_returns_df: SimpleReturnsDataFrame) -> SimpleReturnsSeries:
portfolio = EqualRiskContributionPortfolio(asset_returns_df.cov())
weights = portfolio.get_weights()
portfolio_rets, _ = Portfolio.constant_weights(asset_returns_df,
weights)
return portfolio_rets