adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Reworked to fill leading and trailing days

Browse Source
This commit is contained in:
hroff-1902 2020-02-05 16:54:04 +03:00
parent 379bfc120a
commit 8e6ab0eaaf
1 changed files with 20 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

32
freqtrade/optimize/hyperopt_loss_sharpe_daily.py
View File

@ -6,7 +6,7 @@ Hyperoptimization.
"""
from datetime import datetime
from pandas import DataFrame
from pandas import DataFrame, date_range
import numpy as np
from freqtrade.optimize.hyperopt import IHyperOptLoss
@ -28,25 +28,33 @@ class SharpeHyperOptLossDaily(IHyperOptLoss):
Uses Sharpe Ratio calculation.
"""
# get profit_percent and apply slippage of 0.1% per trade
results.loc[:, 'profit_percent_after_slippage'] = results['profit_percent'] - 0.0005
resample_freq = '1D'
slippage_per_trade_ratio = 0.0005
days_in_year = 365
annual_risk_free_rate = 0.03
risk_free_rate = annual_risk_free_rate / days_in_year
# apply slippage per trade to profit_percent
results.loc[:, 'profit_percent_after_slippage'] = results['profit_percent'] - 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)
sum_daily = (
results.resample("D", on="close_time").agg(
{"profit_percent_after_slippage": sum}
)
* 100.0
results.resample(resample_freq, on='close_time').agg(
{"profit_percent_after_slippage": sum}).reindex(t_index).fillna(0)
)
total_profit = sum_daily["profit_percent_after_slippage"]
total_profit = sum_daily["profit_percent_after_slippage"] - risk_free_rate
expected_returns_mean = total_profit.mean()
up_stdev = np.std(total_profit)
up_stdev = total_profit.std()
if (np.std(total_profit) != 0.):
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
if (up_stdev != 0.):
sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(days_in_year)
else:
# Define high (negative) sharpe ratio to be clear that this is NOT optimal.
sharp_ratio = -20.
# print(expected_returns_mean, up_stdev, sharp_ratio)
#print(t_index, sum_daily, total_profit)
#print(risk_free_rate, expected_returns_mean, up_stdev, sharp_ratio)
return -sharp_ratio