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Update hyperopt-loss to use resolver

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This commit is contained in:
Matthias
2019-07-16 06:27:23 +02:00
parent 7d62bb8c53
commit d23179e25c
9 changed files with 177 additions and 120 deletions
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14
freqtrade/optimize/default_hyperopt.py
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@@ -10,20 +10,6 @@ from skopt.space import Categorical, Dimension, Integer, Real
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.optimize.hyperopt_interface import IHyperOpt
# set TARGET_TRADES to suit your number concurrent trades so its realistic
# to the number of days
TARGET_TRADES = 600
# 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,
# self.expected_max_profit = 3.85
# Check that the reported Σ% values do not exceed this!
# Note, this is ratio. 3.85 stated above means 385Σ%.
EXPECTED_MAX_PROFIT = 3.0
# max average trade duration in minutes
# if eval ends with higher value, we consider it a failed eval
MAX_ACCEPTED_TRADE_DURATION = 300
class DefaultHyperOpts(IHyperOpt):
"""

51
freqtrade/optimize/default_hyperopt_loss.py Normal file
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@@ -0,0 +1,51 @@
"""
IHyperOptLoss interface
This module defines the interface for the loss-function for hyperopts
"""
from math import exp
from pandas import DataFrame
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss
# Define some constants:
# set TARGET_TRADES to suit your number concurrent trades so its realistic
# to the number of days
TARGET_TRADES = 600
# 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,
# self.expected_max_profit = 3.85
# Check that the reported Σ% values do not exceed this!
# Note, this is ratio. 3.85 stated above means 385Σ%.
EXPECTED_MAX_PROFIT = 3.0
# max average trade duration in minutes
# if eval ends with higher value, we consider it a failed eval
MAX_ACCEPTED_TRADE_DURATION = 300
class DefaultHyperOptLoss(IHyperOptLoss):
"""
Defines the default loss function for hyperopt
"""
@staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
This is the legacy algorithm (used until now in freqtrade).
Weights are distributed as follows:
* 0.4 to trade duration
* 0.25: Avoiding trade loss
"""
total_profit = results.profit_percent.sum()
trade_duration = results.trade_duration.mean()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
result = trade_loss + profit_loss + duration_loss
return result

22
freqtrade/optimize/hyperopt.py
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@@ -18,12 +18,10 @@ from pandas import DataFrame
from skopt import Optimizer
from skopt.space import Dimension
from freqtrade import OperationalException
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, hyperopt_loss_sharpe
from freqtrade.resolvers.hyperopt_resolver import HyperOptResolver, HyperOptLossResolver
logger = logging.getLogger(__name__)
@@ -48,6 +46,9 @@ class Hyperopt(Backtesting):
super().__init__(config)
self.custom_hyperopt = HyperOptResolver(self.config).hyperopt
self.custom_hyperoptloss = HyperOptLossResolver(self.config).hyperoptloss
self.calculate_loss = self.custom_hyperoptloss.hyperopt_loss_function
# set TARGET_TRADES to suit your number concurrent trades so its realistic
# to the number of days
self.target_trades = 600
@@ -74,21 +75,6 @@ class Hyperopt(Backtesting):
self.trials_file = TRIALSDATA_PICKLE
self.trials: List = []
# Assign loss function
if self.config.get('loss_function', 'legacy') == 'legacy':
self.calculate_loss = hyperopt_loss_legacy # type: ignore
elif self.config.get('loss_function', 'sharpe') == 'sharpe':
self.calculate_loss = hyperopt_loss_sharpe # type: ignore
elif (self.config['loss_function'] == 'custom' and
hasattr(self.custom_hyperopt, 'hyperopt_loss_custom')):
self.calculate_loss = self.custom_hyperopt.hyperopt_loss_custom # type: ignore
# Implement fallback to avoid odd crashes when custom-hyperopt fails to load.
if not hasattr(self.custom_hyperopt, 'hyperopt_loss_custom'):
logger.warning("Could not load hyperopt configuration. "
"Falling back to legacy configuration.")
raise OperationalException("Could not load hyperopt loss function.")
# 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

64
freqtrade/optimize/hyperopt_loss.py
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@@ -1,64 +0,0 @@
from datetime import datetime
from math import exp
import numpy as np
from pandas import DataFrame
# Define some constants:
# set TARGET_TRADES to suit your number concurrent trades so its realistic
# to the number of days
TARGET_TRADES = 600
# 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,
# self.expected_max_profit = 3.85
# Check that the reported Σ% values do not exceed this!
# Note, this is ratio. 3.85 stated above means 385Σ%.
EXPECTED_MAX_PROFIT = 3.0
# max average trade duration in minutes
# if eval ends with higher value, we consider it a failed eval
MAX_ACCEPTED_TRADE_DURATION = 300
def hyperopt_loss_legacy(results: DataFrame, trade_count: int,
*args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
This is the legacy algorithm (used until now in freqtrade).
Weights are distributed as follows:
* 0.4 to trade duration
* 0.25: Avoiding trade loss
"""
total_profit = results.profit_percent.sum()
trade_duration = results.trade_duration.mean()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
result = trade_loss + profit_loss + duration_loss
return result
def hyperopt_loss_sharpe(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime, *args, **kwargs) -> float:
"""
Objective function, returns smaller number for more optimal results
Using sharpe ratio calculation
"""
total_profit = results.profit_percent
days_period = (max_date - min_date).days
# adding slippage of 0.1% per trade
total_profit = total_profit - 0.0005
expected_yearly_return = total_profit.sum() / days_period
if (np.std(total_profit) != 0.):
sharp_ratio = expected_yearly_return / np.std(total_profit) * np.sqrt(365)
else:
sharp_ratio = 1.
# print(expected_yearly_return, np.std(total_profit), sharp_ratio)
# Negate sharp-ratio so lower is better (??)
return -sharp_ratio

25
freqtrade/optimize/hyperopt_loss_interface.py Normal file
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@@ -0,0 +1,25 @@
"""
IHyperOptLoss interface
This module defines the interface for the loss-function for hyperopts
"""
from abc import ABC, abstractmethod
from datetime import datetime
from pandas import DataFrame
class IHyperOptLoss(ABC):
"""
Interface for freqtrade hyperopts Loss functions.
Defines the custom loss function (`hyperopt_loss_function()` which is evaluated every epoch.)
"""
ticker_interval: str
@staticmethod
@abstractmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int,
min_date: datetime, max_date: datetime, *args, **kwargs) -> float:
"""
Objective function, returns smaller number for better results
"""