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improvement : move to simulated annualized vs calculated

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Pialat 2019-09-13 10:06:33 +02:00
parent 847aab0359
commit 1538523adf
1 changed files with 6 additions and 4 deletions
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10
freqtrade/optimize/hyperopt_loss_calmar.py
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@ -37,6 +37,7 @@ class CalmarHyperOptLoss(IHyperOptLoss):
return MAX_LOSS
simulated_drawdowns = []
simulated_annualized_returns = []
backtest_duration_years = ((max_date-min_date).days/365)
trade_count_average_per_year = trade_count/backtest_duration_years
@ -44,20 +45,21 @@ class CalmarHyperOptLoss(IHyperOptLoss):
# add slipage to be closed to live
results['profit_percent'] -= SLIPPAGE_PERCENT
return_avg_per_year = (results.profit_percent.sum() / backtest_duration_years)
sample_size = round(trade_count_average_per_year * SIMULATION_YEAR_DURATION)
# simulate n years of run to define a median max drawdown
# simulate n years of run to define a median max drawdown and median annual return
for i in range(0, NB_SIMULATIONS):
randomized_result = results.profit_percent.sample(n=sample_size,
random_state=np.random.RandomState(),
replace=True)
simulated_drawdown = cls.abs_max_drawdown(randomized_result)
simulated_drawdowns.append(simulated_drawdown)
simulated_annualized_returns.append(randomized_result.sum()/SIMULATION_YEAR_DURATION)
abs_mediam_simulated_drawdowns = Series(simulated_drawdowns).median()
calmar_ratio = return_avg_per_year/abs_mediam_simulated_drawdowns
mediam_simulated_annualized_returns = Series(simulated_annualized_returns).median()
calmar_ratio = mediam_simulated_annualized_returns/abs_mediam_simulated_drawdowns
# Normalize loss value to be float between (0, 1) : 0.5 value mean no profit
calmar_loss = 1 - (norm.cdf(calmar_ratio, 0, 10))