freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe_daily.py
"""
SharpeHyperOptLossDaily
This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization.
"""
import math
from datetime import datetime
from pandas import DataFrame, date_range
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.
"""
resample_freq = '1D'
slippage_per_trade_ratio = 0.0005
days_in_year = 365
annual_risk_free_rate = 0.0
risk_free_rate = annual_risk_free_rate / days_in_year
# apply slippage per trade to profit_ratio
results.loc[:, 'profit_ratio_after_slippage'] = \
results['profit_ratio'] - slippage_per_trade_ratio
# create the index within the min_date and end max_date
t_index = date_range(start=min_date, end=max_date, freq=resample_freq,
normalize=True)
sum_daily = (
results.resample(resample_freq, on='close_date').agg(
{"profit_ratio_after_slippage": 'sum'}).reindex(t_index).fillna(0)
)
total_profit = sum_daily["profit_ratio_after_slippage"] - risk_free_rate
expected_returns_mean = total_profit.mean()
up_stdev = total_profit.std()
if up_stdev != 0:
sharp_ratio = expected_returns_mean / up_stdev * math.sqrt(days_in_year)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = -20.
# print(t_index, sum_daily, total_profit)
# print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
return -sharp_ratio