vendor_fields = ['close'],
cache_algo = 'cache_algo_return') # how to return data)
df = self.tsfactory.harvest_time_series(tsr_indices)
df.columns = [x.split(".")[0] for x in df.columns]
return df
if __name__ == '__main__':
# create FX CTA strategy then chart the returns, leverage over time
if True:
strategy = StrategyFXCTA_Example()
strategy.construct_strategy()
strategy.plot_strategy_pnl()
strategy.plot_strategy_leverage()
strategy.plot_strategy_group_benchmark_pnl()
strategy.plot_strategy_group_leverage()
strategy.plot_strategy_group_benchmark_annualised_pnl()
# create FX CTA strategy then use TradeAnalysis (via pyfolio) to analyse returns
if True:
from pythalesians.backtest.stratanalysis.tradeanalysis import TradeAnalysis
strategy = StrategyFXCTA_Example()
strategy.construct_strategy()
tradeanalysis = TradeAnalysis()
tradeanalysis.run_strategy_returns_stats(strategy)
strategy.plot_strategy_pnl() # plot the final strategy
strategy.plot_strategy_leverage() # plot the leverage of the portfolio
strategy.plot_strategy_group_pnl_trades() # plot the individual trade P&Ls
strategy.plot_strategy_group_benchmark_pnl() # plot all the cumulative P&Ls of each component
strategy.plot_strategy_group_leverage() # plot all the individual leverages
strategy.plot_strategy_group_benchmark_annualised_pnl()
# create a FX CTA strategy, then examine how P&L changes with different vol targeting
# and later transaction costs
if True:
strategy = StrategyFXCTA_Example()
from pythalesians.backtest.stratanalysis.tradeanalysis import TradeAnalysis
ta = TradeAnalysis()
# which backtesting parameters to change
# names of the portfolio
# broad type of parameter name
parameter_list = [
{'portfolio_vol_adjust': True, 'signal_vol_adjust' : True},
{'portfolio_vol_adjust': False, 'signal_vol_adjust' : False}]
pretty_portfolio_names = \
['Vol target',
'No vol target']
parameter_type = 'vol target'
ta.run_arbitrary_sensitivity(strategy,
cache_algo='cache_algo_return') # how to return data)
df = self.tsfactory.harvest_time_series(tsr_indices)
df.columns = [x.split(".")[0] for x in df.columns]
return df
if __name__ == '__main__':
# create FX CTA strategy then chart the returns, leverage over time
if True:
strategy = StrategyFXCTA_Example()
strategy.construct_strategy()
strategy.plot_strategy_pnl()
strategy.plot_strategy_leverage()
strategy.plot_strategy_group_benchmark_pnl()
strategy.plot_strategy_group_leverage()
strategy.plot_strategy_group_benchmark_annualised_pnl()
# create FX CTA strategy then use TradeAnalysis (via pyfolio) to analyse returns
if True:
from pythalesians.backtest.stratanalysis.tradeanalysis import TradeAnalysis
strategy = StrategyFXCTA_Example()
strategy.construct_strategy()
tradeanalysis = TradeAnalysis()
tradeanalysis.run_strategy_returns_stats(strategy)