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Hi there,
I am still trying out the backtesting.optimize function (which is GREAT!!!). One think I would like to do, for analysis, though, is to see the various metrics that are pumped out as part of the backtesting run.
For example, I understand the below example shows the 'heatmap' which shows each of the input variables, and the output for the desired maximize function...
BUT, is there a way to save other metrics, such as 'Sharpe Ratio', 'Volatility' etc, that were calculated from a 'straight' run?
Thanks, gurus!
#############
s_time = time.time()
bt = Backtest(data_test, RsiOscillator, cash=10_000, commission=.002)
stats_skopt_2 = bt.run()
# bt.plot()
print('time for the initial strategy test is', time.time() - s_time)
def maximize_func(stats):
# return stats['Equity Final [$]'] / stats['Max. Drawdown [%]'] * stats['Expectancy [%]']
if stats['Max. Drawdown [%]'] == 0:
return stats['Equity Final [$]'] * stats['Expectancy [%]']
else:
return stats['Equity Final [$]'] * stats['Expectancy [%]'] / stats['Max. Drawdown [%]']
s_time = time.time()
# Note : for method = 'skopt', we only need interval end-points
# We can input constraints per this line if needed : constraint=lambda p: p.rsi_lower < p.rsi_upper,
stats_skopt_2, heatmap, optimize_result = bt.optimize(
upper_bound = [60, 70],
lower_bound = [15, 30],
rsi_window = [11, 14],
maximize= maximize_func,
method='skopt',
max_tries=100,
random_state=0,
return_heatmap=True,
return_optimization=True)
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Replies: 1 comment 4 replies
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You could save and/or examine every instance of stats that you get into your maximize_func() ...
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sorry, @kernc , but where would I put the code to save down the stats within the bt.optimize?
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Did you find an answer to this? I would be interested to know how to save every instance of stats and trades as my optimisations can take hours to complete.
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I thought one can save all optimization stats somewhat like so:
all_results_stats = [] def maximize_func(stats): all_results_stats.append(stats) return stats['SQN'] # or similar bt = Backtest(...) bt.optimize(..., maximize=maximize_func) assert all_results_stats
but now I'm thinking this simple approach may not work with multiprocess optimization. 😅
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@daveysab / @kernc ,
@kernc , thanks for the advice (code)..... I tried your sample, and unfortunately, it did not seem to save down anything. Perhaps I did something wrong (though I just cut-and-paste your suggestion). And yes, the trouble is, I am trying to have multi-process optimization...
@daveysab , unfortunately, I did not manage to debug/get the answer to this as of yet. If I manage to debug it, I will post it here, and give you a shout.
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👍 1