def construct_strategy(self, br = None):
"""
construct_strategy - Constructs the returns for all the strategies which have been specified.
- gets parameters form fill_backtest_request
- market data from fill_assets
"""
time_series_calcs = TimeSeriesCalcs()
# get the parameters for backtesting
if hasattr(self, 'br'):
br = self.br
elif br is None:
br = self.fill_backtest_request()
# get market data for backtest
asset_df, spot_df, spot_df2, basket_dict = self.fill_assets()
if hasattr(br, 'tech_params'):
tech_params = br.tech_params
else:
tech_params = TechParams()
cumresults = pandas.DataFrame(index = asset_df.index)
portleverage = pandas.DataFrame(index = asset_df.index)
from collections import OrderedDict
tsdresults = OrderedDict()
# each portfolio key calculate returns - can put parts of the portfolio in the key
for key in basket_dict.keys():
asset_cut_df = asset_df[[x +'.close' for x in basket_dict[key]]]
spot_cut_df = spot_df[[x +'.close' for x in basket_dict[key]]]
self.logger.info("Calculating " + key)
results, cash_backtest = self.construct_individual_strategy(br, spot_cut_df, spot_df2, asset_cut_df, tech_params, key)
cumresults[results.columns[0]] = results
portleverage[results.columns[0]] = cash_backtest.get_porfolio_leverage()
tsdresults[key] = cash_backtest.get_portfolio_pnl_tsd()
# for a key, designated as the final strategy save that as the "strategy"
if key == self.FINAL_STRATEGY:
self._strategy_pnl = results
self._strategy_pnl_tsd = cash_backtest.get_portfolio_pnl_tsd()
self._strategy_leverage = cash_backtest.get_porfolio_leverage()
self._strategy_signal = cash_backtest.get_porfolio_signal()
self._strategy_pnl_trades = cash_backtest.get_pnl_trades()
# get benchmark for comparison
benchmark = self.construct_strategy_benchmark()
cumresults_benchmark = self.compare_strategy_vs_benchmark(br, cumresults, benchmark)
self._strategy_group_benchmark_tsd = tsdresults
if hasattr(self, '_benchmark_tsd'):
tsdlist = tsdresults
tsdlist['Benchmark'] = (self._benchmark_tsd)
self._strategy_group_benchmark_tsd = tsdlist
# calculate annualised returns
years = time_series_calcs.average_by_annualised_year(time_series_calcs.calculate_returns(cumresults_benchmark))
self._strategy_group_pnl = cumresults
self._strategy_group_pnl_tsd = tsdresults
self._strategy_group_benchmark_pnl = cumresults_benchmark
self._strategy_group_leverage = portleverage
self._strategy_group_benchmark_annualised_pnl = years
def construct_strategy(self):
"""
construct_strategy - Constructs the returns for all the strategies which have been specified.
- gets parameters form fill_backtest_request
- market data from fill_assets
"""
time_series_calcs = TimeSeriesCalcs()
# get the parameters for backtesting
if hasattr(self, 'br'):
br = self.br
else:
br = self.fill_backtest_request()
# get market data for backtest
asset_df, spot_df, spot_df2, basket_dict = self.fill_assets()
if hasattr(br, 'tech_params'):
tech_params = br.tech_params
else:
tech_params = TechParams()
cumresults = pandas.DataFrame(index=asset_df.index)
portleverage = pandas.DataFrame(index=asset_df.index)
tsdresults = {}
# each portfolio key calculate returns - can put parts of the portfolio in the key
for key in basket_dict.keys():
asset_cut_df = asset_df[[x + '.close' for x in basket_dict[key]]]
spot_cut_df = spot_df[[x + '.close' for x in basket_dict[key]]]
self.logger.info("Calculating " + key)
results, cash_backtest = self.construct_individual_strategy(
br, spot_cut_df, spot_df2, asset_cut_df, tech_params, key)
cumresults[results.columns[0]] = results
portleverage[
results.columns[0]] = cash_backtest.get_porfolio_leverage()
tsdresults[key] = cash_backtest.get_portfolio_pnl_tsd()
# for a key, designated as the final strategy save that as the "strategy"
if key == self.FINAL_STRATEGY:
self._strategy_pnl = results
self._strategy_pnl_tsd = cash_backtest.get_portfolio_pnl_tsd()
self._strategy_leverage = cash_backtest.get_porfolio_leverage()
self._strategy_signal = cash_backtest.get_porfolio_signal()
# get benchmark for comparison
benchmark = self.construct_strategy_benchmark()
cumresults_benchmark = self.compare_strategy_vs_benchmark(
br, cumresults, benchmark)
self._strategy_group_benchmark_tsd = tsdresults
if hasattr(self, '_benchmark_tsd'):
tsdlist = tsdresults
tsdlist['Benchmark'] = (self._benchmark_tsd)
self._strategy_group_benchmark_tsd = tsdlist
# calculate annualised returns
years = time_series_calcs.average_by_annualised_year(
time_series_calcs.calculate_returns(cumresults_benchmark))
self._strategy_group_pnl = cumresults
self._strategy_group_pnl_tsd = tsdresults
self._strategy_group_benchmark_pnl = cumresults_benchmark
self._strategy_group_leverage = portleverage
self._strategy_group_benchmark_annualised_pnl = years
