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Merge branch 'feat/short' into pr/samgermain/5378

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This commit is contained in:
Matthias
2021-09-17 19:24:47 +02:00
parent b752516f65 d680fdf33a
commit 4d558879e9
99 changed files with 1075 additions and 2863 deletions
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7
freqtrade/optimize/backtesting.py
View File

@@ -11,7 +11,7 @@ from typing import Any, Dict, List, Optional, Tuple
from pandas import DataFrame
from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
from freqtrade.configuration import TimeRange, validate_config_consistency
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.data import history
from freqtrade.data.btanalysis import trade_list_to_dataframe
@@ -64,8 +64,7 @@ class Backtesting:
self.config = config
self.results: Optional[Dict[str, Any]] = None
# Reset keys for backtesting
remove_credentials(self.config)
config['dry_run'] = True
self.strategylist: List[IStrategy] = []
self.all_results: Dict[str, Dict] = {}
self._exchange_name = self.config['exchange']['name']
@@ -403,7 +402,7 @@ class Backtesting:
detail_data = detail_data.loc[
(detail_data['date'] >= sell_candle_time) &
(detail_data['date'] < sell_candle_end)
]
]
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
return self._get_sell_trade_entry_for_candle(trade, sell_row)

6
freqtrade/optimize/edge_cli.py
View File

@@ -7,7 +7,7 @@ import logging
from typing import Any, Dict
from freqtrade import constants
from freqtrade.configuration import TimeRange, remove_credentials, validate_config_consistency
from freqtrade.configuration import TimeRange, validate_config_consistency
from freqtrade.edge import Edge
from freqtrade.optimize.optimize_reports import generate_edge_table
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@@ -28,8 +28,8 @@ class EdgeCli:
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
# Reset keys for edge
remove_credentials(self.config)
# Ensure using dry-run
self.config['dry_run'] = True
self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
self.strategy = StrategyResolver.load_strategy(self.config)

83
freqtrade/optimize/hyperopt.py
View File

@@ -22,6 +22,7 @@ from pandas import DataFrame
from freqtrade.constants import DATETIME_PRINT_FORMAT, FTHYPT_FILEVERSION, LAST_BT_RESULT_FN
from freqtrade.data.converter import trim_dataframes
from freqtrade.data.history import get_timerange
from freqtrade.exceptions import OperationalException
from freqtrade.misc import deep_merge_dicts, file_dump_json, plural
from freqtrade.optimize.backtesting import Backtesting
# Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
@@ -30,7 +31,7 @@ from freqtrade.optimize.hyperopt_interface import IHyperOpt # noqa: F401
from freqtrade.optimize.hyperopt_loss_interface import IHyperOptLoss # noqa: F401
from freqtrade.optimize.hyperopt_tools import HyperoptTools, hyperopt_serializer
from freqtrade.optimize.optimize_reports import generate_strategy_stats
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver, HyperOptResolver
from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
# Suppress scikit-learn FutureWarnings from skopt
@@ -44,7 +45,7 @@ progressbar.streams.wrap_stdout()
logger = logging.getLogger(__name__)
INITIAL_POINTS = 30
INITIAL_POINTS = 5
# Keep no more than SKOPT_MODEL_QUEUE_SIZE models
# in the skopt model queue, to optimize memory consumption
@@ -78,10 +79,10 @@ class Hyperopt:
if not self.config.get('hyperopt'):
self.custom_hyperopt = HyperOptAuto(self.config)
self.auto_hyperopt = True
else:
self.custom_hyperopt = HyperOptResolver.load_hyperopt(self.config)
self.auto_hyperopt = False
raise OperationalException(
"Using separate Hyperopt files has been removed in 2021.9. Please convert "
"your existing Hyperopt file to the new Hyperoptable strategy interface")
self.backtesting._set_strategy(self.backtesting.strategylist[0])
self.custom_hyperopt.strategy = self.backtesting.strategy
@@ -103,31 +104,6 @@ class Hyperopt:
self.num_epochs_saved = 0
self.current_best_epoch: Optional[Dict[str, Any]] = None
if not self.auto_hyperopt:
# Populate "fallback" functions here
# (hasattr is slow so should not be run during "regular" operations)
if hasattr(self.custom_hyperopt, 'populate_indicators'):
logger.warning(
"DEPRECATED: Using `populate_indicators()` in the hyperopt file is deprecated. "
"Please move these methods to your strategy."
)
self.backtesting.strategy.populate_indicators = ( # type: ignore
self.custom_hyperopt.populate_indicators) # type: ignore
if hasattr(self.custom_hyperopt, 'populate_buy_trend'):
logger.warning(
"DEPRECATED: Using `populate_buy_trend()` in the hyperopt file is deprecated. "
"Please move these methods to your strategy."
)
self.backtesting.strategy.populate_buy_trend = ( # type: ignore
self.custom_hyperopt.populate_buy_trend) # type: ignore
if hasattr(self.custom_hyperopt, 'populate_sell_trend'):
logger.warning(
"DEPRECATED: Using `populate_sell_trend()` in the hyperopt file is deprecated. "
"Please move these methods to your strategy."
)
self.backtesting.strategy.populate_sell_trend = ( # type: ignore
self.custom_hyperopt.populate_sell_trend) # type: ignore
# Use max_open_trades for hyperopt as well, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
self.max_open_trades = self.config['max_open_trades']
@@ -256,7 +232,7 @@ class Hyperopt:
"""
Assign the dimensions in the hyperoptimization space.
"""
if self.auto_hyperopt and HyperoptTools.has_space(self.config, 'protection'):
if HyperoptTools.has_space(self.config, 'protection'):
# Protections can only be optimized when using the Parameter interface
logger.debug("Hyperopt has 'protection' space")
# Enable Protections if protection space is selected.
@@ -265,7 +241,7 @@ class Hyperopt:
if HyperoptTools.has_space(self.config, 'buy'):
logger.debug("Hyperopt has 'buy' space")
self.buy_space = self.custom_hyperopt.indicator_space()
self.buy_space = self.custom_hyperopt.buy_indicator_space()
if HyperoptTools.has_space(self.config, 'sell'):
logger.debug("Hyperopt has 'sell' space")
@@ -285,6 +261,15 @@ class Hyperopt:
self.dimensions = (self.buy_space + self.sell_space + self.protection_space
+ self.roi_space + self.stoploss_space + self.trailing_space)
def assign_params(self, params_dict: Dict, category: str) -> None:
"""
Assign hyperoptable parameters
"""
for attr_name, attr in self.backtesting.strategy.enumerate_parameters(category):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params_dict[attr_name]
def generate_optimizer(self, raw_params: List[Any], iteration=None) -> Dict:
"""
Used Optimize function.
@@ -296,18 +281,13 @@ class Hyperopt:
# Apply parameters
if HyperoptTools.has_space(self.config, 'buy'):
self.backtesting.strategy.advise_buy = ( # type: ignore
self.custom_hyperopt.buy_strategy_generator(params_dict))
self.assign_params(params_dict, 'buy')
if HyperoptTools.has_space(self.config, 'sell'):
self.backtesting.strategy.advise_sell = ( # type: ignore
self.custom_hyperopt.sell_strategy_generator(params_dict))
self.assign_params(params_dict, 'sell')
if HyperoptTools.has_space(self.config, 'protection'):
for attr_name, attr in self.backtesting.strategy.enumerate_parameters('protection'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params_dict[attr_name]
self.assign_params(params_dict, 'protection')
if HyperoptTools.has_space(self.config, 'roi'):
self.backtesting.strategy.minimal_roi = ( # type: ignore
@@ -385,10 +365,20 @@ class Hyperopt:
}
def get_optimizer(self, dimensions: List[Dimension], cpu_count) -> Optimizer:
estimator = self.custom_hyperopt.generate_estimator()
acq_optimizer = "sampling"
if isinstance(estimator, str):
if estimator not in ("GP", "RF", "ET", "GBRT"):
raise OperationalException(f"Estimator {estimator} not supported.")
else:
acq_optimizer = "auto"
logger.info(f"Using estimator {estimator}.")
return Optimizer(
dimensions,
base_estimator="ET",
acq_optimizer="auto",
base_estimator=estimator,
acq_optimizer=acq_optimizer,
n_initial_points=INITIAL_POINTS,
acq_optimizer_kwargs={'n_jobs': cpu_count},
random_state=self.random_state,
@@ -517,11 +507,10 @@ class Hyperopt:
f"saved to '{self.results_file}'.")
if self.current_best_epoch:
if self.auto_hyperopt:
HyperoptTools.try_export_params(
self.config,
self.backtesting.strategy.get_strategy_name(),
self.current_best_epoch)
HyperoptTools.try_export_params(
self.config,
self.backtesting.strategy.get_strategy_name(),
self.current_best_epoch)
HyperoptTools.show_epoch_details(self.current_best_epoch, self.total_epochs,
self.print_json)

50
freqtrade/optimize/hyperopt_auto.py
View File

@@ -4,15 +4,23 @@ This module implements a convenience auto-hyperopt class, which can be used toge
that implement IHyperStrategy interface.
"""
from contextlib import suppress
from typing import Any, Callable, Dict, List
from typing import Callable, Dict, List
from pandas import DataFrame
from freqtrade.exceptions import OperationalException
with suppress(ImportError):
from skopt.space import Dimension
from freqtrade.optimize.hyperopt_interface import IHyperOpt
from freqtrade.optimize.hyperopt_interface import EstimatorType, IHyperOpt
def _format_exception_message(space: str) -> str:
raise OperationalException(
f"The '{space}' space is included into the hyperoptimization "
f"but no parameter for this space was not found in your Strategy. "
f"Please make sure to have parameters for this space enabled for optimization "
f"or remove the '{space}' space from hyperoptimization.")
class HyperOptAuto(IHyperOpt):
@@ -22,26 +30,6 @@ class HyperOptAuto(IHyperOpt):
sell_indicator_space methods, but other hyperopt methods can be overridden as well.
"""
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def populate_buy_trend(dataframe: DataFrame, metadata: dict):
for attr_name, attr in self.strategy.enumerate_parameters('buy'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params[attr_name]
return self.strategy.populate_buy_trend(dataframe, metadata)
return populate_buy_trend
def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def populate_sell_trend(dataframe: DataFrame, metadata: dict):
for attr_name, attr in self.strategy.enumerate_parameters('sell'):
if attr.optimize:
# noinspection PyProtectedMember
attr.value = params[attr_name]
return self.strategy.populate_sell_trend(dataframe, metadata)
return populate_sell_trend
def _get_func(self, name) -> Callable:
"""
Return a function defined in Strategy.HyperOpt class, or one defined in super() class.
@@ -60,21 +48,22 @@ class HyperOptAuto(IHyperOpt):
if attr.optimize:
yield attr.get_space(attr_name)
def _get_indicator_space(self, category, fallback_method_name):
def _get_indicator_space(self, category):
# TODO: is this necessary, or can we call "generate_space" directly?
indicator_space = list(self._generate_indicator_space(category))
if len(indicator_space) > 0:
return indicator_space
else:
return self._get_func(fallback_method_name)()
_format_exception_message(category)
def indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('buy', 'indicator_space')
def buy_indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('buy')
def sell_indicator_space(self) -> List['Dimension']:
return self._get_indicator_space('sell', 'sell_indicator_space')
return self._get_indicator_space('sell')
def protection_space(self) -> List['Dimension']:
return self._get_indicator_space('protection', 'protection_space')
return self._get_indicator_space('protection')
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
return self._get_func('generate_roi_table')(params)
@@ -90,3 +79,6 @@ class HyperOptAuto(IHyperOpt):
def trailing_space(self) -> List['Dimension']:
return self._get_func('trailing_space')()
def generate_estimator(self) -> EstimatorType:
return self._get_func('generate_estimator')()

44
freqtrade/optimize/hyperopt_interface.py
View File

@@ -5,11 +5,11 @@ This module defines the interface to apply for hyperopt
import logging
import math
from abc import ABC
from typing import Any, Callable, Dict, List
from typing import Dict, List, Union
from sklearn.base import RegressorMixin
from skopt.space import Categorical, Dimension, Integer
from freqtrade.exceptions import OperationalException
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.misc import round_dict
from freqtrade.optimize.space import SKDecimal
@@ -18,12 +18,7 @@ from freqtrade.strategy import IStrategy
logger = logging.getLogger(__name__)
def _format_exception_message(method: str, space: str) -> str:
return (f"The '{space}' space is included into the hyperoptimization "
f"but {method}() method is not found in your "
f"custom Hyperopt class. You should either implement this "
f"method or remove the '{space}' space from hyperoptimization.")
EstimatorType = Union[RegressorMixin, str]
class IHyperOpt(ABC):
@@ -45,36 +40,13 @@ class IHyperOpt(ABC):
IHyperOpt.ticker_interval = str(config['timeframe']) # DEPRECATED
IHyperOpt.timeframe = str(config['timeframe'])
def buy_strategy_generator(self, params: Dict[str, Any]) -> Callable:
def generate_estimator(self) -> EstimatorType:
"""
Create a buy strategy generator.
Return base_estimator.
Can be any of "GP", "RF", "ET", "GBRT" or an instance of a class
inheriting from RegressorMixin (from sklearn).
"""
raise OperationalException(_format_exception_message('buy_strategy_generator', 'buy'))
def sell_strategy_generator(self, params: Dict[str, Any]) -> Callable:
"""
Create a sell strategy generator.
"""
raise OperationalException(_format_exception_message('sell_strategy_generator', 'sell'))
def protection_space(self) -> List[Dimension]:
"""
Create a protection space.
Only supported by the Parameter interface.
"""
raise OperationalException(_format_exception_message('indicator_space', 'protection'))
def indicator_space(self) -> List[Dimension]:
"""
Create an indicator space.
"""
raise OperationalException(_format_exception_message('indicator_space', 'buy'))
def sell_indicator_space(self) -> List[Dimension]:
"""
Create a sell indicator space.
"""
raise OperationalException(_format_exception_message('sell_indicator_space', 'sell'))
return 'ET'
def generate_roi_table(self, params: Dict) -> Dict[int, float]:
"""