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Simplify SortinoLoss

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This commit is contained in:
Matthias 2021-03-25 06:49:19 +01:00
parent 958ad7d446
commit 5aaa05f2f2
1 changed files with 8 additions and 16 deletions
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24
freqtrade/optimize/SortinoLossBalance.py
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@ -4,8 +4,8 @@ This module defines the alternative HyperOptLoss class which can be used for
Hyperoptimization. Hyperoptimization.
""" """
import logging import logging
import os
from datetime import datetime from datetime import datetime
from typing import Dict
import numpy as np import numpy as np
from pandas import DataFrame, Timedelta from pandas import DataFrame, Timedelta
@ -16,11 +16,7 @@ from freqtrade.optimize.hyperopt import IHyperOptLoss
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
interval = os.getenv("FQT_TIMEFRAME") or "5m"
slippage = 0.0005
target = 0 target = 0
annualize = np.sqrt(365 * (Timedelta("1D") / Timedelta(interval)))
logger.info(f"SortinoLossBalance target is set to: {target}") logger.info(f"SortinoLossBalance target is set to: {target}")
@ -31,28 +27,24 @@ class SortinoLossBalance(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: Dict, processed: Dict[str, DataFrame],
min_date: datetime, *args, **kwargs) -> float:
max_date: datetime,
*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.
""" """
hloc = kwargs["processed"]
timeframe = SortinoLossBalance.timeframe timeframe = SortinoLossBalance.timeframe
annualize = np.sqrt(365 * (Timedelta("1D") / Timedelta(timeframe)))
balance_total = calculate_outstanding_balance(results, timeframe, hloc) balance_total = calculate_outstanding_balance(results, timeframe, processed)
returns = balance_total.mean() returns = balance_total.mean()
# returns = balance_total.values.mean() # returns = balance_total.values.mean()
downside_returns = np.where(balance_total < 0, balance_total, 0) downside_returns = np.where(balance_total < 0, balance_total, 0)
downside_risk = np.sqrt((downside_returns ** 2).sum() / len(hloc)) downside_risk = np.sqrt((downside_returns ** 2).sum() / len(processed))
if downside_risk != 0.0: if downside_risk != 0.0:
sortino_ratio = (returns - target) / downside_risk * annualize sortino_ratio = (returns - target) / downside_risk * annualize