stable/freqtrade/optimize/hyperopt_loss_sharpe.py
Cedric Schmeits 8ad5afd3a1
As -sharp_ratio is returned the value should be nagative.
This leads in a high positive result of the loss function, as it is a minimal optimizer
2019-08-08 22:10:51 +02:00

46 lines
1.4 KiB
Python

"""
SharpeHyperOptLoss
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
from datetime import datetime
from pandas import DataFrame
import numpy as np
from freqtrade.optimize.hyperopt import IHyperOptLoss
class SharpeHyperOptLoss(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation uses the Sharpe Ratio calculation.
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results.
Uses Sharpe Ratio calculation.
"""
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.
# print(expected_yearly_return, np.std(total_profit), sharp_ratio)
return -sharp_ratio