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Update onlyprofit loss should use absolute profit

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closes #4934
This commit is contained in:
Matthias 2021-05-28 08:38:46 +02:00
parent b740ed8064
commit 8a56af9192
2 changed files with 5 additions and 17 deletions
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18
freqtrade/optimize/hyperopt_loss_onlyprofit.py
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@ -9,23 +9,11 @@ from pandas import DataFrame
from freqtrade.optimize.hyperopt import IHyperOptLoss
# This is assumed to be expected avg profit * expected trade count.
# For example, for 0.35% avg per trade (or 0.0035 as ratio) and 1100 trades,
# expected max profit = 3.85
#
# Note, this is ratio. 3.85 stated above means 385Σ%, 3.0 means 300Σ%.
#
# In this implementation it's only used in calculation of the resulting value
# of the objective function as a normalization coefficient and does not
# represent any limit for profits as in the Freqtrade legacy default loss function.
EXPECTED_MAX_PROFIT = 3.0
class OnlyProfitHyperOptLoss(IHyperOptLoss):
"""
Defines the loss function for hyperopt.
This implementation takes only profit into account.
This implementation takes only absolute profit into account, not looking at any other indicator.
"""
@staticmethod
@ -34,5 +22,5 @@ class OnlyProfitHyperOptLoss(IHyperOptLoss):
"""
Objective function, returns smaller number for better results.
"""
total_profit = results['profit_ratio'].sum()
return 1 - total_profit / EXPECTED_MAX_PROFIT
total_profit = results['profit_abs'].sum()
return -1 * total_profit

4
tests/optimize/test_hyperoptloss.py
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@ -149,9 +149,9 @@ def test_sortino_loss_daily_prefers_higher_profits(default_conf, hyperopt_result
def test_onlyprofit_loss_prefers_higher_profits(default_conf, hyperopt_results) -> None:
results_over = hyperopt_results.copy()
results_over['profit_ratio'] = hyperopt_results['profit_ratio'] * 2
results_over['profit_abs'] = hyperopt_results['profit_abs'] * 2
results_under = hyperopt_results.copy()
results_under['profit_ratio'] = hyperopt_results['profit_ratio'] / 2
results_under['profit_abs'] = hyperopt_results['profit_abs'] / 2
default_conf.update({'hyperopt_loss': 'OnlyProfitHyperOptLoss'})
hl = HyperOptLossResolver.load_hyperoptloss(default_conf)