62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
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"""
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SharpeHyperOptLossDaily
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This module defines the alternative HyperOptLoss class which can be used for
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Hyperoptimization.
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"""
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import math
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from datetime import datetime
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from pandas import DataFrame, date_range
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from freqtrade.optimize.hyperopt import IHyperOptLoss
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class SharpeHyperOptLossDaily(IHyperOptLoss):
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"""
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Defines the loss function for hyperopt.
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This implementation uses the Sharpe Ratio calculation.
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"""
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@staticmethod
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def hyperopt_loss_function(results: DataFrame, trade_count: int,
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min_date: datetime, max_date: datetime,
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*args, **kwargs) -> float:
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"""
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Objective function, returns smaller number for more optimal results.
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Uses Sharpe Ratio calculation.
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"""
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resample_freq = '1D'
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slippage_per_trade_ratio = 0.0005
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days_in_year = 365
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annual_risk_free_rate = 0.0
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risk_free_rate = annual_risk_free_rate / days_in_year
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# apply slippage per trade to profit_percent
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results.loc[:, 'profit_percent_after_slippage'] = \
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results['profit_percent'] - slippage_per_trade_ratio
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# create the index within the min_date and end max_date
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t_index = date_range(start=min_date, end=max_date, freq=resample_freq)
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sum_daily = (
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results.resample(resample_freq, on='close_time').agg(
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{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
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)
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total_profit = sum_daily["profit_percent_after_slippage"] - risk_free_rate
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expected_returns_mean = total_profit.mean()
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up_stdev = total_profit.std()
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if (up_stdev != 0.):
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sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
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else:
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# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
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sharp_ratio = -20.
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# print(t_index, sum_daily, total_profit)
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# print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
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return -sharp_ratio
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