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Allow loading custom hyperopt loss functions

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Matthias
2019-07-15 21:35:42 +02:00
parent 2fedae6060
commit 2a20423be6
6 changed files with 135 additions and 24 deletions
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32
freqtrade/optimize/hyperopt.py
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@@ -7,7 +7,7 @@ This module contains the hyperopt logic
import logging
import os
import sys
from math import exp
from operator import itemgetter
from pathlib import Path
from pprint import pprint
@@ -22,6 +22,7 @@ from freqtrade.configuration import Arguments
from freqtrade.data.history import load_data, get_timeframe
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver
from freqtrade.optimize.hyperopt_loss import hyperopt_loss_legacy
logger = logging.getLogger(__name__)
@@ -69,6 +70,20 @@ class Hyperopt(Backtesting):
self.trials_file = TRIALSDATA_PICKLE
self.trials: List = []
# Assign loss function
if self.config['loss_function'] == 'legacy':
self.calculate_loss = hyperopt_loss_legacy
elif (self.config['loss_function'] == 'custom' and
hasattr(self.custom_hyperopt, 'hyperopt_loss_custom')):
self.calculate_loss = self.custom_hyperopt.hyperopt_loss_custom
# Implement fallback to avoid odd crashes when custom-hyperopt fails to load.
# TODO: Maybe this should just stop hyperopt completely?
if not hasattr(self.custom_hyperopt, 'hyperopt_loss_custom'):
logger.warning("Could not load hyperopt configuration. "
"Falling back to legacy configuration.")
self.calculate_loss = hyperopt_loss_legacy
# Populate functions here (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
self.advise_buy = self.custom_hyperopt.populate_buy_trend # type: ignore
@@ -160,16 +175,6 @@ class Hyperopt(Backtesting):
print('.', end='')
sys.stdout.flush()
def calculate_loss(self, total_profit: float, trade_count: int, trade_duration: float) -> float:
"""
Objective function, returns smaller number for more optimal results
"""
trade_loss = 1 - 0.25 * exp(-(trade_count - self.target_trades) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / self.expected_max_profit)
duration_loss = 0.4 * min(trade_duration / self.max_accepted_trade_duration, 1)
result = trade_loss + profit_loss + duration_loss
return result
def has_space(self, space: str) -> bool:
"""
Tell if a space value is contained in the configuration
@@ -231,9 +236,7 @@ class Hyperopt(Backtesting):
)
result_explanation = self.format_results(results)
total_profit = results.profit_percent.sum()
trade_count = len(results.index)
trade_duration = results.trade_duration.mean()
# If this evaluation contains too short amount of trades to be
# interesting -- consider it as 'bad' (assigned max. loss value)
@@ -246,7 +249,8 @@ class Hyperopt(Backtesting):
'result': result_explanation,
}
loss = self.calculate_loss(total_profit, trade_count, trade_duration)
loss = self.calculate_loss(results=results, trade_count=trade_count,
min_date=min_date.datetime, max_date=max_date.datetime)
return {
'loss': loss,