57 lines
1.4 KiB
Python
57 lines
1.4 KiB
Python
"""
|
|
SharpeHyperOptLossDaily
|
|
|
|
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 SharpeHyperOptLossDaily(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.
|
|
"""
|
|
# get profit_percent and apply slippage of 0.1% per trade
|
|
results.loc[:, 'profit_percent'] = results['profit_percent'] - 0.0005
|
|
|
|
sum_daily = (
|
|
results.resample("D", on="close_time").agg(
|
|
{"profit_percent": sum}
|
|
)
|
|
* 100.0
|
|
)
|
|
|
|
if (np.std(sum_daily.profit_percent) != 0.):
|
|
sharp_ratio = (
|
|
sum_daily["profit_percent"].mean()
|
|
/ sum_daily["profit_percent"].std()
|
|
* np.sqrt(365)
|
|
)
|
|
else:
|
|
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
|
|
sharp_ratio = -20.0
|
|
|
|
return -sharp_ratio
|