freqtrade_origin/freqtrade/optimize/hyperopt_loss_sharpe.py

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"""
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SharpeHyperOptLoss
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This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
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from datetime import datetime
from pandas import DataFrame
import numpy as np
from freqtrade.optimize.hyperopt import IHyperOptLoss
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class SharpeHyperOptLoss(IHyperOptLoss):
"""
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Defines the loss function for hyperopt.
This implementation uses the Sharpe Ratio calculation.
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"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
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Objective function, returns smaller number for more optimal results.
Uses Sharpe Ratio calculation.
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"""
total_profit = results.profit_percent
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_yearly_return = total_profit.sum() / days_period
if (np.std(total_profit) != 0.):
sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = -20.
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# print(expected_yearly_return, np.std(total_profit), sharp_ratio)
return -sharp_ratio