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resolved the total profit issue

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I resolved the total profit issue and locally ran flak8 and isort
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Robert Roman 2021-09-23 21:31:33 -05:00 committed by GitHub
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commit 3b99c84b0a
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1 changed files with 22 additions and 10 deletions
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32
freqtrade/optimize/hyperopt_loss_calmar.py
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@ -5,11 +5,13 @@ This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization. Hyperoptimization.
""" """
from datetime import datetime from datetime import datetime
from math import sqrt as msqrt
from typing import Any, Dict
from pandas import DataFrame
import numpy as np
from freqtrade.data.btanalysis import calculate_max_drawdown from freqtrade.data.btanalysis import calculate_max_drawdown
from freqtrade.optimize.hyperopt import IHyperOptLoss from freqtrade.optimize.hyperopt import IHyperOptLoss
from pandas import DataFrame
class CalmarHyperOptLoss(IHyperOptLoss): class CalmarHyperOptLoss(IHyperOptLoss):
@ -20,31 +22,41 @@ class CalmarHyperOptLoss(IHyperOptLoss):
""" """
@staticmethod @staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int, def hyperopt_loss_function(
min_date: datetime, max_date: datetime, results: DataFrame,
*args, **kwargs) -> float: trade_count: int,
min_date: datetime,
max_date: datetime,
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 = results["profit_ratio"] total_profit = backtest_stats["profit_total"]
days_period = (max_date - min_date).days days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade # adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005 total_profit = total_profit - 0.0005
expected_returns_mean = total_profit.sum() / days_period expected_returns_mean = total_profit.sum() / days_period * 100
# calculate max drawdown # calculate max drawdown
try: try:
_,_,_,high_val,low_val = calculate_max_drawdown(results) _, _, _, high_val, low_val = calculate_max_drawdown(
results, value_col="profit_abs"
)
max_drawdown = (high_val - low_val) / high_val max_drawdown = (high_val - low_val) / high_val
except ValueError: except ValueError:
max_drawdown = 0 max_drawdown = 0
if max_drawdown != 0: if max_drawdown != 0:
calmar_ratio = expected_returns_mean / max_drawdown * np.sqrt(365) calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
else: else:
calmar_ratio = -20. # 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)
return -calmar_ratio return -calmar_ratio