advanced-algorithmic-trading

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It should be well remembered that large unexpected upward moves are just as dangerous as
large downward moves since they reflect unanticipated behaviour of the strategy. For instance,
a large upward performance jump could imply a new regulatory regime that has benefited the
strategy. This must now be taken into account in any subsequent strategy development in order
to maintain consistency of performance.
32.2 Python QSTrader Implementation
The implementation of the annualised rolling Sharpe ratio is now part of the QSTrader codebase
and is originally due to @nwillemse.
The addition of the annualised rolling Sharpe ratio necessitated an update to the minimum
required version of the Pandas library used by QSTrader, which is now 0.18.0. Hence if you wish
to use this feature you may need to update your Pandas version to 0.18.0 or greater.
The annualised rolling Sharpe feature is provided as a chart that sits underneath the equity
curve in the tearsheet visual output. It is optional and can be activated by setting rolling_sharpe=True
in the instantiation of the TearsheetStatistics class in the backtest. In the class itself it can
be seen that it is simply implemented as a member flag:
class TearsheetStatistics(AbstractStatistics):
def __init__(
self, config, portfolio_handler,
title=None, benchmark=None, periods=252,
rolling_sharpe=False
):
..
..
self.rolling_sharpe = rolling_sharpe
..
..
To calculate the annualised rolling Sharpe it is necessary to obtain the rolling object by
using the rolling method on the strategy returns series, with a lookback window equal to the
number of trading periods (self.periods).
The calculation simply multiplies the square root of trading periods by the ratio of the rolling
mean to the rolling standard deviation. Note that there is no risk-free rate included here. Zero
returns are considered the risk-free alternative. The same is carried out for the benchmark. All
of this occurs in the get_results method of the TearsheetStatistics class:
def get_results(self):
# Equity
equity_s = pd.Series(self.equity).sort_index()
# Returns
returns_s = equity_s.pct_change().fillna(0.0)
# Rolling Annualised Sharpe
rolling = returns_s.rolling(window=self.periods)