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

update calmar loss

Browse Source
This commit is contained in:
Stefano Ariestasia 2023-01-07 09:30:16 +09:00
parent d3b1aa7f01
commit 6198b21001
1 changed files with 6 additions and 33 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

39
freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py
View File

@ -11,7 +11,7 @@ from typing import Any, Dict
from pandas import DataFrame from pandas import DataFrame
from freqtrade.constants import Config from freqtrade.constants import Config
from freqtrade.data.metrics import calculate_max_drawdown from freqtrade.data.metrics import calculate_calmar
from freqtrade.optimize.hyperopt import IHyperOptLoss from freqtrade.optimize.hyperopt import IHyperOptLoss
@ -23,42 +23,15 @@ class CalmarHyperOptLoss(IHyperOptLoss):
""" """
@staticmethod @staticmethod
def hyperopt_loss_function( def hyperopt_loss_function(results: DataFrame, trade_count: int,
results: DataFrame, min_date: datetime, max_date: datetime,
trade_count: int, config: Config, *args, **kwargs) -> float:
min_date: datetime,
max_date: datetime,
config: Config,
processed: Dict[str, DataFrame],
backtest_stats: Dict[str, Any],
*args,
**kwargs
) -> float:
""" """
Objective function, returns smaller number for more optimal results. Objective function, returns smaller number for more optimal results.
Uses Calmar Ratio calculation. Uses Calmar Ratio calculation.
""" """
total_profit = backtest_stats["profit_total"] starting_balance = config['dry_run_wallet']
days_period = (max_date - min_date).days calmar_ratio = calculate_calmar(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 * 100
# calculate max drawdown
try:
_, _, _, _, _, max_drawdown = calculate_max_drawdown(
results, value_col="profit_abs"
)
except ValueError:
max_drawdown = 0
if max_drawdown != 0:
calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
else:
# Define high (negative) calmar ratio to be clear that this is NOT optimal.
calmar_ratio = -20.0
# print(expected_returns_mean, max_drawdown, calmar_ratio) # print(expected_returns_mean, max_drawdown, calmar_ratio)
return -calmar_ratio return -calmar_ratio