lychiyu/freqtrade_origin
1
0
Fork 0
mirror of https://github.com/freqtrade/freqtrade.git synced 2024-11-10 10:21:59 +00:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

update sortino calc

Browse Source
This commit is contained in:
Stefano Ariestasia 2023-01-07 09:19:06 +09:00
parent 157bf962f7
commit d3b1aa7f01
1 changed files with 5 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py
View File

@ -10,7 +10,8 @@ import numpy as np
from pandas import DataFrame from pandas import DataFrame
from freqtrade.optimize.hyperopt import IHyperOptLoss from freqtrade.optimize.hyperopt import IHyperOptLoss
from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_sortino
class SortinoHyperOptLoss(IHyperOptLoss): class SortinoHyperOptLoss(IHyperOptLoss):
""" """
@ -22,28 +23,13 @@ class SortinoHyperOptLoss(IHyperOptLoss):
@staticmethod @staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int, def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime, min_date: datetime, max_date: datetime,
*args, **kwargs) -> float: config: Config, *args, **kwargs) -> float:
""" """
Objective function, returns smaller number for more optimal results. Objective function, returns smaller number for more optimal results.
Uses Sortino Ratio calculation. Uses Sortino Ratio calculation.
""" """
total_profit = results["profit_ratio"] starting_balance = config['dry_run_wallet']
days_period = (max_date - min_date).days sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance)
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_returns_mean = total_profit.sum() / days_period
results['downside_returns'] = 0
results.loc[total_profit < 0, 'downside_returns'] = results['profit_ratio']
down_stdev = np.std(results['downside_returns'])
if down_stdev != 0:
sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
else:
# Define high (negative) sortino ratio to be clear that this is NOT optimal.
sortino_ratio = -20.
# print(expected_returns_mean, down_stdev, sortino_ratio) # print(expected_returns_mean, down_stdev, sortino_ratio)
return -sortino_ratio return -sortino_ratio