stable/freqtrade/optimize/hyperopt_loss_sharpe.py
2019-07-16 06:45:13 +02:00

43 lines
1.4 KiB
Python

"""
IHyperOptLoss interface
This module defines the interface for the loss-function for hyperopts
"""
from datetime import datetime
from pandas import DataFrame
import numpy as np
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
class SharpeHyperOptLoss(IHyperOptLoss):
"""
Defines the a 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
Using 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