1348 lines
63 KiB
Python
1348 lines
63 KiB
Python
"""
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IStrategy interface
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This module defines the interface to apply for strategies
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"""
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import logging
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from abc import ABC, abstractmethod
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from datetime import datetime, timedelta, timezone
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from typing import Dict, List, Optional, Tuple, Union
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import arrow
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from pandas import DataFrame
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from freqtrade.constants import Config, ListPairsWithTimeframes
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, RunMode, SignalDirection,
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SignalTagType, SignalType, TradingMode)
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from freqtrade.exceptions import OperationalException, StrategyError
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from freqtrade.exchange import timeframe_to_minutes, timeframe_to_next_date, timeframe_to_seconds
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from freqtrade.misc import remove_entry_exit_signals
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from freqtrade.persistence import Order, PairLocks, Trade
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from freqtrade.strategy.hyper import HyperStrategyMixin
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from freqtrade.strategy.informative_decorator import (InformativeData, PopulateIndicators,
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_create_and_merge_informative_pair,
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_format_pair_name)
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from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
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from freqtrade.wallets import Wallets
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logger = logging.getLogger(__name__)
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CUSTOM_EXIT_MAX_LENGTH = 64
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class IStrategy(ABC, HyperStrategyMixin):
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"""
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Interface for freqtrade strategies
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Defines the mandatory structure must follow any custom strategies
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Attributes you can use:
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minimal_roi -> Dict: Minimal ROI designed for the strategy
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stoploss -> float: optimal stoploss designed for the strategy
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timeframe -> str: value of the timeframe to use with the strategy
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"""
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# Strategy interface version
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# Default to version 2
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# Version 1 is the initial interface without metadata dict - deprecated and no longer supported.
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# Version 2 populate_* include metadata dict
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# Version 3 - First version with short and leverage support
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INTERFACE_VERSION: int = 3
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_ft_params_from_file: Dict
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# associated minimal roi
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minimal_roi: Dict = {"0": 10.0}
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# associated stoploss
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stoploss: float
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# trailing stoploss
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trailing_stop: bool = False
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trailing_stop_positive: Optional[float] = None
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trailing_stop_positive_offset: float = 0.0
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trailing_only_offset_is_reached = False
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use_custom_stoploss: bool = False
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# Can this strategy go short?
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can_short: bool = False
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# associated timeframe
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timeframe: str
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# Optional order types
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order_types: Dict = {
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'entry': 'limit',
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'exit': 'limit',
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'stoploss': 'limit',
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'stoploss_on_exchange': False,
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'stoploss_on_exchange_interval': 60,
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}
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# Optional time in force
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order_time_in_force: Dict = {
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'entry': 'GTC',
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'exit': 'GTC',
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}
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# run "populate_indicators" only for new candle
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process_only_new_candles: bool = True
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use_exit_signal: bool
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exit_profit_only: bool
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exit_profit_offset: float
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ignore_roi_if_entry_signal: bool
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# Position adjustment is disabled by default
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position_adjustment_enable: bool = False
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max_entry_position_adjustment: int = -1
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# Number of seconds after which the candle will no longer result in a buy on expired candles
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ignore_buying_expired_candle_after: int = 0
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# Disable checking the dataframe (converts the error into a warning message)
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disable_dataframe_checks: bool = False
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# Count of candles the strategy requires before producing valid signals
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startup_candle_count: int = 0
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# Protections
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protections: List = []
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# Class level variables (intentional) containing
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# the dataprovider (dp) (access to other candles, historic data, ...)
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# and wallets - access to the current balance.
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dp: DataProvider
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wallets: Optional[Wallets] = None
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# Filled from configuration
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stake_currency: str
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# container variable for strategy source code
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__source__: str = ''
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# Definition of plot_config. See plotting documentation for more details.
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plot_config: Dict = {}
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def __init__(self, config: Config) -> None:
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self.config = config
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# Dict to determine if analysis is necessary
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self._last_candle_seen_per_pair: Dict[str, datetime] = {}
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super().__init__(config)
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# Gather informative pairs from @informative-decorated methods.
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self._ft_informative: List[Tuple[InformativeData, PopulateIndicators]] = []
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for attr_name in dir(self.__class__):
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cls_method = getattr(self.__class__, attr_name)
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if not callable(cls_method):
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continue
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informative_data_list = getattr(cls_method, '_ft_informative', None)
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if not isinstance(informative_data_list, list):
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# Type check is required because mocker would return a mock object that evaluates to
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# True, confusing this code.
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continue
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strategy_timeframe_minutes = timeframe_to_minutes(self.timeframe)
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for informative_data in informative_data_list:
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if timeframe_to_minutes(informative_data.timeframe) < strategy_timeframe_minutes:
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raise OperationalException('Informative timeframe must be equal or higher than '
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'strategy timeframe!')
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if not informative_data.candle_type:
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informative_data.candle_type = config['candle_type_def']
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self._ft_informative.append((informative_data, cls_method))
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def load_freqAI_model(self) -> None:
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if self.config.get('freqai', {}).get('enabled', False):
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# Import here to avoid importing this if freqAI is disabled
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from freqtrade.freqai.utils import download_all_data_for_training
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from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
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self.freqai = FreqaiModelResolver.load_freqaimodel(self.config)
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self.freqai_info = self.config["freqai"]
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# download the desired data in dry/live
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if self.config.get('runmode') in (RunMode.DRY_RUN, RunMode.LIVE):
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logger.info(
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"Downloading all training data for all pairs in whitelist and "
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"corr_pairlist, this may take a while if the data is not "
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"already on disk."
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)
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download_all_data_for_training(self.dp, self.config)
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else:
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# Gracious failures if freqAI is disabled but "start" is called.
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class DummyClass():
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def start(self, *args, **kwargs):
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raise OperationalException(
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'freqAI is not enabled. '
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'Please enable it in your config to use this strategy.')
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def shutdown(self, *args, **kwargs):
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pass
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self.freqai = DummyClass() # type: ignore
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def ft_bot_start(self, **kwargs) -> None:
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"""
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Strategy init - runs after dataprovider has been added.
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Must call bot_start()
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"""
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self.load_freqAI_model()
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strategy_safe_wrapper(self.bot_start)()
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self.ft_load_hyper_params(self.config.get('runmode') == RunMode.HYPEROPT)
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def ft_bot_cleanup(self) -> None:
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"""
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Clean up FreqAI and child threads
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"""
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self.freqai.shutdown()
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@abstractmethod
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Populate indicators that will be used in the Buy, Sell, Short, Exit_short strategy
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:param dataframe: DataFrame with data from the exchange
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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DEPRECATED - please migrate to populate_entry_trend
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
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"""
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the entry signal for the given dataframe
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with entry columns populated
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"""
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return self.populate_buy_trend(dataframe, metadata)
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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DEPRECATED - please migrate to populate_exit_trend
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with sell column
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"""
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the exit signal for the given dataframe
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:param dataframe: DataFrame
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:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with exit columns populated
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"""
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return self.populate_sell_trend(dataframe, metadata)
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def bot_start(self, **kwargs) -> None:
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"""
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Called only once after bot instantiation.
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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"""
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pass
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def bot_loop_start(self, **kwargs) -> None:
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"""
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Called at the start of the bot iteration (one loop).
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Might be used to perform pair-independent tasks
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(e.g. gather some remote resource for comparison)
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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"""
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pass
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def check_buy_timeout(self, pair: str, trade: Trade, order: Order,
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current_time: datetime, **kwargs) -> bool:
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"""
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DEPRECATED: Please use `check_entry_timeout` instead.
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"""
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return False
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def check_entry_timeout(self, pair: str, trade: Trade, order: Order,
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current_time: datetime, **kwargs) -> bool:
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"""
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Check entry timeout function callback.
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This method can be used to override the entry-timeout.
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It is called whenever a limit entry order has been created,
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and is not yet fully filled.
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Configuration options in `unfilledtimeout` will be verified before this,
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so ensure to set these timeouts high enough.
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When not implemented by a strategy, this simply returns False.
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:param pair: Pair the trade is for
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:param trade: Trade object.
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:param order: Order object.
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:param current_time: datetime object, containing the current datetime
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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:return bool: When True is returned, then the entry order is cancelled.
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"""
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return self.check_buy_timeout(
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pair=pair, trade=trade, order=order, current_time=current_time)
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def check_sell_timeout(self, pair: str, trade: Trade, order: Order,
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current_time: datetime, **kwargs) -> bool:
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"""
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DEPRECATED: Please use `check_exit_timeout` instead.
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"""
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return False
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def check_exit_timeout(self, pair: str, trade: Trade, order: Order,
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current_time: datetime, **kwargs) -> bool:
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"""
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Check exit timeout function callback.
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This method can be used to override the exit-timeout.
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It is called whenever a limit exit order has been created,
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and is not yet fully filled.
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Configuration options in `unfilledtimeout` will be verified before this,
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so ensure to set these timeouts high enough.
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When not implemented by a strategy, this simply returns False.
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:param pair: Pair the trade is for
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:param trade: Trade object.
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:param order: Order object
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:param current_time: datetime object, containing the current datetime
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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:return bool: When True is returned, then the exit-order is cancelled.
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"""
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return self.check_sell_timeout(
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pair=pair, trade=trade, order=order, current_time=current_time)
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, entry_tag: Optional[str],
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side: str, **kwargs) -> bool:
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"""
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Called right before placing a entry order.
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Timing for this function is critical, so avoid doing heavy computations or
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network requests in this method.
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For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
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When not implemented by a strategy, returns True (always confirming).
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:param pair: Pair that's about to be bought/shorted.
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:param order_type: Order type (as configured in order_types). usually limit or market.
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:param amount: Amount in target (base) currency that's going to be traded.
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:param rate: Rate that's going to be used when using limit orders
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or current rate for market orders.
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:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
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:param current_time: datetime object, containing the current datetime
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:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
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:param side: 'long' or 'short' - indicating the direction of the proposed trade
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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:return bool: When True is returned, then the buy-order is placed on the exchange.
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False aborts the process
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"""
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return True
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def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
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rate: float, time_in_force: str, exit_reason: str,
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current_time: datetime, **kwargs) -> bool:
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"""
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Called right before placing a regular exit order.
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Timing for this function is critical, so avoid doing heavy computations or
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network requests in this method.
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For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
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When not implemented by a strategy, returns True (always confirming).
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:param pair: Pair for trade that's about to be exited.
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:param trade: trade object.
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:param order_type: Order type (as configured in order_types). usually limit or market.
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:param amount: Amount in base currency.
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:param rate: Rate that's going to be used when using limit orders
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or current rate for market orders.
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:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
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:param exit_reason: Exit reason.
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Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
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'exit_signal', 'force_exit', 'emergency_exit']
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:param current_time: datetime object, containing the current datetime
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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:return bool: When True, then the exit-order is placed on the exchange.
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False aborts the process
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"""
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return True
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def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
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current_profit: float, **kwargs) -> float:
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"""
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Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
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e.g. returning -0.05 would create a stoploss 5% below current_rate.
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The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
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For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
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When not implemented by a strategy, returns the initial stoploss value
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Only called when use_custom_stoploss is set to True.
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:param pair: Pair that's currently analyzed
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:param trade: trade object.
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:param current_time: datetime object, containing the current datetime
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:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
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:param current_profit: Current profit (as ratio), calculated based on current_rate.
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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:return float: New stoploss value, relative to the current_rate
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"""
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return self.stoploss
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def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
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entry_tag: Optional[str], side: str, **kwargs) -> float:
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"""
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Custom entry price logic, returning the new entry price.
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For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
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When not implemented by a strategy, returns None, orderbook is used to set entry price
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:param pair: Pair that's currently analyzed
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:param current_time: datetime object, containing the current datetime
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:param proposed_rate: Rate, calculated based on pricing settings in exit_pricing.
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:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
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:param side: 'long' or 'short' - indicating the direction of the proposed trade
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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:return float: New entry price value if provided
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"""
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return proposed_rate
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def custom_exit_price(self, pair: str, trade: Trade,
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current_time: datetime, proposed_rate: float,
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current_profit: float, exit_tag: Optional[str], **kwargs) -> float:
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"""
|
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Custom exit price logic, returning the new exit price.
|
|
|
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For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
|
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When not implemented by a strategy, returns None, orderbook is used to set exit price
|
|
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:param pair: Pair that's currently analyzed
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:param trade: trade object.
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:param current_time: datetime object, containing the current datetime
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:param proposed_rate: Rate, calculated based on pricing settings in exit_pricing.
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:param current_profit: Current profit (as ratio), calculated based on current_rate.
|
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:param exit_tag: Exit reason.
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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:return float: New exit price value if provided
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"""
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return proposed_rate
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def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
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current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
|
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"""
|
|
DEPRECATED - please use custom_exit instead.
|
|
Custom exit signal logic indicating that specified position should be sold. Returning a
|
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string or True from this method is equal to setting exit signal on a candle at specified
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time. This method is not called when exit signal is set.
|
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This method should be overridden to create exit signals that depend on trade parameters. For
|
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example you could implement an exit relative to the candle when the trade was opened,
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or a custom 1:2 risk-reward ROI.
|
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Custom exit reason max length is 64. Exceeding characters will be removed.
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:param pair: Pair that's currently analyzed
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:param trade: trade object.
|
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:param current_time: datetime object, containing the current datetime
|
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:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
|
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:param current_profit: Current profit (as ratio), calculated based on current_rate.
|
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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:return: To execute exit, return a string with custom exit reason or True. Otherwise return
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None or False.
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"""
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return None
|
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def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
|
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current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
|
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"""
|
|
Custom exit signal logic indicating that specified position should be sold. Returning a
|
|
string or True from this method is equal to setting exit signal on a candle at specified
|
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time. This method is not called when exit signal is set.
|
|
|
|
This method should be overridden to create exit signals that depend on trade parameters. For
|
|
example you could implement an exit relative to the candle when the trade was opened,
|
|
or a custom 1:2 risk-reward ROI.
|
|
|
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Custom exit reason max length is 64. Exceeding characters will be removed.
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|
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:param pair: Pair that's currently analyzed
|
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:param trade: trade object.
|
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:param current_time: datetime object, containing the current datetime
|
|
:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
|
|
:param current_profit: Current profit (as ratio), calculated based on current_rate.
|
|
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
|
|
:return: To execute exit, return a string with custom exit reason or True. Otherwise return
|
|
None or False.
|
|
"""
|
|
return self.custom_sell(pair, trade, current_time, current_rate, current_profit, **kwargs)
|
|
|
|
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
|
|
proposed_stake: float, min_stake: Optional[float], max_stake: float,
|
|
leverage: float, entry_tag: Optional[str], side: str,
|
|
**kwargs) -> float:
|
|
"""
|
|
Customize stake size for each new trade.
|
|
|
|
:param pair: Pair that's currently analyzed
|
|
:param current_time: datetime object, containing the current datetime
|
|
:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
|
|
:param proposed_stake: A stake amount proposed by the bot.
|
|
:param min_stake: Minimal stake size allowed by exchange.
|
|
:param max_stake: Balance available for trading.
|
|
:param leverage: Leverage selected for this trade.
|
|
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
|
|
:param side: 'long' or 'short' - indicating the direction of the proposed trade
|
|
:return: A stake size, which is between min_stake and max_stake.
|
|
"""
|
|
return proposed_stake
|
|
|
|
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
|
current_rate: float, current_profit: float,
|
|
min_stake: Optional[float], max_stake: float,
|
|
current_entry_rate: float, current_exit_rate: float,
|
|
current_entry_profit: float, current_exit_profit: float,
|
|
**kwargs) -> Optional[float]:
|
|
"""
|
|
Custom trade adjustment logic, returning the stake amount that a trade should be
|
|
increased or decreased.
|
|
This means extra buy or sell orders with additional fees.
|
|
Only called when `position_adjustment_enable` is set to True.
|
|
|
|
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
|
|
|
|
When not implemented by a strategy, returns None
|
|
|
|
:param trade: trade object.
|
|
:param current_time: datetime object, containing the current datetime
|
|
:param current_rate: Current buy rate.
|
|
:param current_profit: Current profit (as ratio), calculated based on current_rate.
|
|
:param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
|
|
:param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
|
|
:param current_entry_rate: Current rate using entry pricing.
|
|
:param current_exit_rate: Current rate using exit pricing.
|
|
:param current_entry_profit: Current profit using entry pricing.
|
|
:param current_exit_profit: Current profit using exit pricing.
|
|
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
|
|
:return float: Stake amount to adjust your trade,
|
|
Positive values to increase position, Negative values to decrease position.
|
|
Return None for no action.
|
|
"""
|
|
return None
|
|
|
|
def adjust_entry_price(self, trade: Trade, order: Optional[Order], pair: str,
|
|
current_time: datetime, proposed_rate: float, current_order_rate: float,
|
|
entry_tag: Optional[str], side: str, **kwargs) -> float:
|
|
"""
|
|
Entry price re-adjustment logic, returning the user desired limit price.
|
|
This only executes when a order was already placed, still open (unfilled fully or partially)
|
|
and not timed out on subsequent candles after entry trigger.
|
|
|
|
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-callbacks/
|
|
|
|
When not implemented by a strategy, returns current_order_rate as default.
|
|
If current_order_rate is returned then the existing order is maintained.
|
|
If None is returned then order gets canceled but not replaced by a new one.
|
|
|
|
:param pair: Pair that's currently analyzed
|
|
:param trade: Trade object.
|
|
:param order: Order object
|
|
:param current_time: datetime object, containing the current datetime
|
|
:param proposed_rate: Rate, calculated based on pricing settings in entry_pricing.
|
|
:param current_order_rate: Rate of the existing order in place.
|
|
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
|
|
:param side: 'long' or 'short' - indicating the direction of the proposed trade
|
|
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
|
|
:return float: New entry price value if provided
|
|
|
|
"""
|
|
return current_order_rate
|
|
|
|
def leverage(self, pair: str, current_time: datetime, current_rate: float,
|
|
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str],
|
|
side: str, **kwargs) -> float:
|
|
"""
|
|
Customize leverage for each new trade. This method is only called in futures mode.
|
|
|
|
:param pair: Pair that's currently analyzed
|
|
:param current_time: datetime object, containing the current datetime
|
|
:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
|
|
:param proposed_leverage: A leverage proposed by the bot.
|
|
:param max_leverage: Max leverage allowed on this pair
|
|
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
|
|
:param side: 'long' or 'short' - indicating the direction of the proposed trade
|
|
:return: A leverage amount, which is between 1.0 and max_leverage.
|
|
"""
|
|
return 1.0
|
|
|
|
def informative_pairs(self) -> ListPairsWithTimeframes:
|
|
"""
|
|
Define additional, informative pair/interval combinations to be cached from the exchange.
|
|
These pair/interval combinations are non-tradable, unless they are part
|
|
of the whitelist as well.
|
|
For more information, please consult the documentation
|
|
:return: List of tuples in the format (pair, interval)
|
|
Sample: return [("ETH/USDT", "5m"),
|
|
("BTC/USDT", "15m"),
|
|
]
|
|
"""
|
|
return []
|
|
|
|
def version(self) -> Optional[str]:
|
|
"""
|
|
Returns version of the strategy.
|
|
"""
|
|
return None
|
|
|
|
def populate_any_indicators(self, pair: str, df: DataFrame, tf: str,
|
|
informative: DataFrame = None,
|
|
set_generalized_indicators: bool = False) -> DataFrame:
|
|
"""
|
|
DEPRECATED - USE FEATURE ENGINEERING FUNCTIONS INSTEAD
|
|
Function designed to automatically generate, name and merge features
|
|
from user indicated timeframes in the configuration file. User can add
|
|
additional features here, but must follow the naming convention.
|
|
This method is *only* used in FreqaiDataKitchen class and therefore
|
|
it is only called if FreqAI is active.
|
|
:param pair: pair to be used as informative
|
|
:param df: strategy dataframe which will receive merges from informatives
|
|
:param tf: timeframe of the dataframe which will modify the feature names
|
|
:param informative: the dataframe associated with the informative pair
|
|
"""
|
|
return df
|
|
|
|
def feature_engineering_expand_all(self, dataframe: DataFrame,
|
|
period: int, **kwargs):
|
|
"""
|
|
*Only functional with FreqAI enabled strategies*
|
|
This function will automatically expand the defined features on the config defined
|
|
`indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
|
|
`include_corr_pairs`. In other words, a single feature defined in this function
|
|
will automatically expand to a total of
|
|
`indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
|
|
`include_corr_pairs` numbers of features added to the model.
|
|
|
|
All features must be prepended with `%` to be recognized by FreqAI internals.
|
|
|
|
More details on how these config defined parameters accelerate feature engineering
|
|
in the documentation at:
|
|
|
|
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
|
|
|
|
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
|
|
|
|
:param df: strategy dataframe which will receive the features
|
|
:param period: period of the indicator - usage example:
|
|
dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
|
|
"""
|
|
return dataframe
|
|
|
|
def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
|
|
"""
|
|
*Only functional with FreqAI enabled strategies*
|
|
This function will automatically expand the defined features on the config defined
|
|
`include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
|
|
In other words, a single feature defined in this function
|
|
will automatically expand to a total of
|
|
`include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
|
|
numbers of features added to the model.
|
|
|
|
Features defined here will *not* be automatically duplicated on user defined
|
|
`indicator_periods_candles`
|
|
|
|
All features must be prepended with `%` to be recognized by FreqAI internals.
|
|
|
|
More details on how these config defined parameters accelerate feature engineering
|
|
in the documentation at:
|
|
|
|
https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
|
|
|
|
https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
|
|
|
|
:param df: strategy dataframe which will receive the features
|
|
dataframe["%-pct-change"] = dataframe["close"].pct_change()
|
|
dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
|
|
"""
|
|
return dataframe
|
|
|
|
def feature_engineering_standard(self, dataframe: DataFrame, **kwargs):
|
|
"""
|
|
*Only functional with FreqAI enabled strategies*
|
|
This optional function will be called once with the dataframe of the base timeframe.
|
|
This is the final function to be called, which means that the dataframe entering this
|
|
function will contain all the features and columns created by all other
|
|
freqai_feature_engineering_* functions.
|
|
|
|
This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
|
|
This function is a good place for any feature that should not be auto-expanded upon
|
|
(e.g. day of the week).
|
|
|
|
All features must be prepended with `%` to be recognized by FreqAI internals.
|
|
|
|
More details about feature engineering available:
|
|
|
|
https://www.freqtrade.io/en/latest/freqai-feature-engineering
|
|
|
|
:param df: strategy dataframe which will receive the features
|
|
usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
|
|
"""
|
|
return dataframe
|
|
|
|
def set_freqai_targets(self, dataframe, **kwargs):
|
|
"""
|
|
*Only functional with FreqAI enabled strategies*
|
|
Required function to set the targets for the model.
|
|
All targets must be prepended with `&` to be recognized by the FreqAI internals.
|
|
|
|
More details about feature engineering available:
|
|
|
|
https://www.freqtrade.io/en/latest/freqai-feature-engineering
|
|
|
|
:param df: strategy dataframe which will receive the targets
|
|
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
|
|
"""
|
|
return dataframe
|
|
|
|
###
|
|
# END - Intended to be overridden by strategy
|
|
###
|
|
|
|
def __informative_pairs_freqai(self) -> ListPairsWithTimeframes:
|
|
"""
|
|
Create informative-pairs needed for FreqAI
|
|
"""
|
|
if self.config.get('freqai', {}).get('enabled', False):
|
|
whitelist_pairs = self.dp.current_whitelist()
|
|
candle_type = self.config.get('candle_type_def', CandleType.SPOT)
|
|
corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
|
|
informative_pairs = []
|
|
for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
|
|
for pair in set(whitelist_pairs + corr_pairs):
|
|
informative_pairs.append((pair, tf, candle_type))
|
|
return informative_pairs
|
|
|
|
return []
|
|
|
|
def gather_informative_pairs(self) -> ListPairsWithTimeframes:
|
|
"""
|
|
Internal method which gathers all informative pairs (user or automatically defined).
|
|
"""
|
|
informative_pairs = self.informative_pairs()
|
|
# Compatibility code for 2 tuple informative pairs
|
|
informative_pairs = [
|
|
(p[0], p[1], CandleType.from_string(p[2]) if len(
|
|
p) > 2 and p[2] != '' else self.config.get('candle_type_def', CandleType.SPOT))
|
|
for p in informative_pairs]
|
|
for inf_data, _ in self._ft_informative:
|
|
# Get default candle type if not provided explicitly.
|
|
candle_type = (inf_data.candle_type if inf_data.candle_type
|
|
else self.config.get('candle_type_def', CandleType.SPOT))
|
|
if inf_data.asset:
|
|
pair_tf = (
|
|
_format_pair_name(self.config, inf_data.asset),
|
|
inf_data.timeframe,
|
|
candle_type,
|
|
)
|
|
informative_pairs.append(pair_tf)
|
|
else:
|
|
for pair in self.dp.current_whitelist():
|
|
informative_pairs.append((pair, inf_data.timeframe, candle_type))
|
|
informative_pairs.extend(self.__informative_pairs_freqai())
|
|
return list(set(informative_pairs))
|
|
|
|
def get_strategy_name(self) -> str:
|
|
"""
|
|
Returns strategy class name
|
|
"""
|
|
return self.__class__.__name__
|
|
|
|
def lock_pair(self, pair: str, until: datetime, reason: str = None, side: str = '*') -> None:
|
|
"""
|
|
Locks pair until a given timestamp happens.
|
|
Locked pairs are not analyzed, and are prevented from opening new trades.
|
|
Locks can only count up (allowing users to lock pairs for a longer period of time).
|
|
To remove a lock from a pair, use `unlock_pair()`
|
|
:param pair: Pair to lock
|
|
:param until: datetime in UTC until the pair should be blocked from opening new trades.
|
|
Needs to be timezone aware `datetime.now(timezone.utc)`
|
|
:param reason: Optional string explaining why the pair was locked.
|
|
:param side: Side to check, can be long, short or '*'
|
|
"""
|
|
PairLocks.lock_pair(pair, until, reason, side=side)
|
|
|
|
def unlock_pair(self, pair: str) -> None:
|
|
"""
|
|
Unlocks a pair previously locked using lock_pair.
|
|
Not used by freqtrade itself, but intended to be used if users lock pairs
|
|
manually from within the strategy, to allow an easy way to unlock pairs.
|
|
:param pair: Unlock pair to allow trading again
|
|
"""
|
|
PairLocks.unlock_pair(pair, datetime.now(timezone.utc))
|
|
|
|
def unlock_reason(self, reason: str) -> None:
|
|
"""
|
|
Unlocks all pairs previously locked using lock_pair with specified reason.
|
|
Not used by freqtrade itself, but intended to be used if users lock pairs
|
|
manually from within the strategy, to allow an easy way to unlock pairs.
|
|
:param reason: Unlock pairs to allow trading again
|
|
"""
|
|
PairLocks.unlock_reason(reason, datetime.now(timezone.utc))
|
|
|
|
def is_pair_locked(self, pair: str, *, candle_date: datetime = None, side: str = '*') -> bool:
|
|
"""
|
|
Checks if a pair is currently locked
|
|
The 2nd, optional parameter ensures that locks are applied until the new candle arrives,
|
|
and not stop at 14:00:00 - while the next candle arrives at 14:00:02 leaving a gap
|
|
of 2 seconds for an entry order to happen on an old signal.
|
|
:param pair: "Pair to check"
|
|
:param candle_date: Date of the last candle. Optional, defaults to current date
|
|
:param side: Side to check, can be long, short or '*'
|
|
:returns: locking state of the pair in question.
|
|
"""
|
|
|
|
if not candle_date:
|
|
# Simple call ...
|
|
return PairLocks.is_pair_locked(pair, side=side)
|
|
else:
|
|
lock_time = timeframe_to_next_date(self.timeframe, candle_date)
|
|
return PairLocks.is_pair_locked(pair, lock_time, side=side)
|
|
|
|
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Parses the given candle (OHLCV) data and returns a populated DataFrame
|
|
add several TA indicators and entry order signal to it
|
|
:param dataframe: Dataframe containing data from exchange
|
|
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
|
|
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
|
|
"""
|
|
logger.debug("TA Analysis Launched")
|
|
dataframe = self.advise_indicators(dataframe, metadata)
|
|
dataframe = self.advise_entry(dataframe, metadata)
|
|
dataframe = self.advise_exit(dataframe, metadata)
|
|
return dataframe
|
|
|
|
def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Parses the given candle (OHLCV) data and returns a populated DataFrame
|
|
add several TA indicators and buy signal to it
|
|
WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
|
|
:param dataframe: Dataframe containing data from exchange
|
|
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
|
|
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
|
|
"""
|
|
pair = str(metadata.get('pair'))
|
|
|
|
new_candle = self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']
|
|
# Test if seen this pair and last candle before.
|
|
# always run if process_only_new_candles is set to false
|
|
if not self.process_only_new_candles or new_candle:
|
|
|
|
# Defs that only make change on new candle data.
|
|
dataframe = self.analyze_ticker(dataframe, metadata)
|
|
|
|
self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
|
|
|
|
candle_type = self.config.get('candle_type_def', CandleType.SPOT)
|
|
self.dp._set_cached_df(pair, self.timeframe, dataframe, candle_type=candle_type)
|
|
self.dp._emit_df((pair, self.timeframe, candle_type), dataframe, new_candle)
|
|
|
|
else:
|
|
logger.debug("Skipping TA Analysis for already analyzed candle")
|
|
dataframe = remove_entry_exit_signals(dataframe)
|
|
|
|
logger.debug("Loop Analysis Launched")
|
|
|
|
return dataframe
|
|
|
|
def analyze_pair(self, pair: str) -> None:
|
|
"""
|
|
Fetch data for this pair from dataprovider and analyze.
|
|
Stores the dataframe into the dataprovider.
|
|
The analyzed dataframe is then accessible via `dp.get_analyzed_dataframe()`.
|
|
:param pair: Pair to analyze.
|
|
"""
|
|
dataframe = self.dp.ohlcv(
|
|
pair, self.timeframe, candle_type=self.config.get('candle_type_def', CandleType.SPOT)
|
|
)
|
|
if not isinstance(dataframe, DataFrame) or dataframe.empty:
|
|
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
|
|
return
|
|
|
|
try:
|
|
df_len, df_close, df_date = self.preserve_df(dataframe)
|
|
|
|
dataframe = strategy_safe_wrapper(
|
|
self._analyze_ticker_internal, message=""
|
|
)(dataframe, {'pair': pair})
|
|
|
|
self.assert_df(dataframe, df_len, df_close, df_date)
|
|
except StrategyError as error:
|
|
logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
|
|
return
|
|
|
|
if dataframe.empty:
|
|
logger.warning('Empty dataframe for pair %s', pair)
|
|
return
|
|
|
|
def analyze(self, pairs: List[str]) -> None:
|
|
"""
|
|
Analyze all pairs using analyze_pair().
|
|
:param pairs: List of pairs to analyze
|
|
"""
|
|
for pair in pairs:
|
|
self.analyze_pair(pair)
|
|
|
|
@staticmethod
|
|
def preserve_df(dataframe: DataFrame) -> Tuple[int, float, datetime]:
|
|
""" keep some data for dataframes """
|
|
return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
|
|
|
|
def assert_df(self, dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
|
|
"""
|
|
Ensure dataframe (length, last candle) was not modified, and has all elements we need.
|
|
"""
|
|
message_template = "Dataframe returned from strategy has mismatching {}."
|
|
message = ""
|
|
if dataframe is None:
|
|
message = "No dataframe returned (return statement missing?)."
|
|
elif 'enter_long' not in dataframe:
|
|
message = "enter_long/buy column not set."
|
|
elif df_len != len(dataframe):
|
|
message = message_template.format("length")
|
|
elif df_close != dataframe["close"].iloc[-1]:
|
|
message = message_template.format("last close price")
|
|
elif df_date != dataframe["date"].iloc[-1]:
|
|
message = message_template.format("last date")
|
|
if message:
|
|
if self.disable_dataframe_checks:
|
|
logger.warning(message)
|
|
else:
|
|
raise StrategyError(message)
|
|
|
|
def get_latest_candle(
|
|
self,
|
|
pair: str,
|
|
timeframe: str,
|
|
dataframe: DataFrame,
|
|
) -> Tuple[Optional[DataFrame], Optional[arrow.Arrow]]:
|
|
"""
|
|
Calculates current signal based based on the entry order or exit order
|
|
columns of the dataframe.
|
|
Used by Bot to get the signal to enter, or exit
|
|
:param pair: pair in format ANT/BTC
|
|
:param timeframe: timeframe to use
|
|
:param dataframe: Analyzed dataframe to get signal from.
|
|
:return: (None, None) or (Dataframe, latest_date) - corresponding to the last candle
|
|
"""
|
|
if not isinstance(dataframe, DataFrame) or dataframe.empty:
|
|
logger.warning(f'Empty candle (OHLCV) data for pair {pair}')
|
|
return None, None
|
|
|
|
latest_date = dataframe['date'].max()
|
|
latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
|
|
# Explicitly convert to arrow object to ensure the below comparison does not fail
|
|
latest_date = arrow.get(latest_date)
|
|
|
|
# Check if dataframe is out of date
|
|
timeframe_minutes = timeframe_to_minutes(timeframe)
|
|
offset = self.config.get('exchange', {}).get('outdated_offset', 5)
|
|
if latest_date < (arrow.utcnow().shift(minutes=-(timeframe_minutes * 2 + offset))):
|
|
logger.warning(
|
|
'Outdated history for pair %s. Last tick is %s minutes old',
|
|
pair, int((arrow.utcnow() - latest_date).total_seconds() // 60)
|
|
)
|
|
return None, None
|
|
return latest, latest_date
|
|
|
|
def get_exit_signal(
|
|
self,
|
|
pair: str,
|
|
timeframe: str,
|
|
dataframe: DataFrame,
|
|
is_short: bool = None
|
|
) -> Tuple[bool, bool, Optional[str]]:
|
|
"""
|
|
Calculates current exit signal based based on the dataframe
|
|
columns of the dataframe.
|
|
Used by Bot to get the signal to exit.
|
|
depending on is_short, looks at "short" or "long" columns.
|
|
:param pair: pair in format ANT/BTC
|
|
:param timeframe: timeframe to use
|
|
:param dataframe: Analyzed dataframe to get signal from.
|
|
:param is_short: Indicating existing trade direction.
|
|
:return: (enter, exit) A bool-tuple with enter / exit values.
|
|
"""
|
|
latest, latest_date = self.get_latest_candle(pair, timeframe, dataframe)
|
|
if latest is None:
|
|
return False, False, None
|
|
|
|
if is_short:
|
|
enter = latest.get(SignalType.ENTER_SHORT.value, 0) == 1
|
|
exit_ = latest.get(SignalType.EXIT_SHORT.value, 0) == 1
|
|
|
|
else:
|
|
enter = latest[SignalType.ENTER_LONG.value] == 1
|
|
exit_ = latest.get(SignalType.EXIT_LONG.value, 0) == 1
|
|
exit_tag = latest.get(SignalTagType.EXIT_TAG.value, None)
|
|
# Tags can be None, which does not resolve to False.
|
|
exit_tag = exit_tag if isinstance(exit_tag, str) else None
|
|
|
|
logger.debug(f"exit-trigger: {latest['date']} (pair={pair}) "
|
|
f"enter={enter} exit={exit_}")
|
|
|
|
return enter, exit_, exit_tag
|
|
|
|
def get_entry_signal(
|
|
self,
|
|
pair: str,
|
|
timeframe: str,
|
|
dataframe: DataFrame,
|
|
) -> Tuple[Optional[SignalDirection], Optional[str]]:
|
|
"""
|
|
Calculates current entry signal based based on the dataframe signals
|
|
columns of the dataframe.
|
|
Used by Bot to get the signal to enter trades.
|
|
:param pair: pair in format ANT/BTC
|
|
:param timeframe: timeframe to use
|
|
:param dataframe: Analyzed dataframe to get signal from.
|
|
:return: (SignalDirection, entry_tag)
|
|
"""
|
|
latest, latest_date = self.get_latest_candle(pair, timeframe, dataframe)
|
|
if latest is None or latest_date is None:
|
|
return None, None
|
|
|
|
enter_long = latest[SignalType.ENTER_LONG.value] == 1
|
|
exit_long = latest.get(SignalType.EXIT_LONG.value, 0) == 1
|
|
enter_short = latest.get(SignalType.ENTER_SHORT.value, 0) == 1
|
|
exit_short = latest.get(SignalType.EXIT_SHORT.value, 0) == 1
|
|
|
|
enter_signal: Optional[SignalDirection] = None
|
|
enter_tag_value: Optional[str] = None
|
|
if enter_long == 1 and not any([exit_long, enter_short]):
|
|
enter_signal = SignalDirection.LONG
|
|
enter_tag_value = latest.get(SignalTagType.ENTER_TAG.value, None)
|
|
if (self.config.get('trading_mode', TradingMode.SPOT) != TradingMode.SPOT
|
|
and self.can_short
|
|
and enter_short == 1 and not any([exit_short, enter_long])):
|
|
enter_signal = SignalDirection.SHORT
|
|
enter_tag_value = latest.get(SignalTagType.ENTER_TAG.value, None)
|
|
|
|
enter_tag_value = enter_tag_value if isinstance(enter_tag_value, str) else None
|
|
|
|
timeframe_seconds = timeframe_to_seconds(timeframe)
|
|
|
|
if self.ignore_expired_candle(
|
|
latest_date=latest_date.datetime,
|
|
current_time=datetime.now(timezone.utc),
|
|
timeframe_seconds=timeframe_seconds,
|
|
enter=bool(enter_signal)
|
|
):
|
|
return None, enter_tag_value
|
|
|
|
logger.debug(f"entry trigger: {latest['date']} (pair={pair}) "
|
|
f"enter={enter_long} enter_tag_value={enter_tag_value}")
|
|
return enter_signal, enter_tag_value
|
|
|
|
def ignore_expired_candle(
|
|
self,
|
|
latest_date: datetime,
|
|
current_time: datetime,
|
|
timeframe_seconds: int,
|
|
enter: bool
|
|
):
|
|
if self.ignore_buying_expired_candle_after and enter:
|
|
time_delta = current_time - (latest_date + timedelta(seconds=timeframe_seconds))
|
|
return time_delta.total_seconds() > self.ignore_buying_expired_candle_after
|
|
else:
|
|
return False
|
|
|
|
def should_exit(self, trade: Trade, rate: float, current_time: datetime, *,
|
|
enter: bool, exit_: bool,
|
|
low: float = None, high: float = None,
|
|
force_stoploss: float = 0) -> List[ExitCheckTuple]:
|
|
"""
|
|
This function evaluates if one of the conditions required to trigger an exit order
|
|
has been reached, which can either be a stop-loss, ROI or exit-signal.
|
|
:param low: Only used during backtesting to simulate (long)stoploss/(short)ROI
|
|
:param high: Only used during backtesting, to simulate (short)stoploss/(long)ROI
|
|
:param force_stoploss: Externally provided stoploss
|
|
:return: List of exit reasons - or empty list.
|
|
"""
|
|
exits: List[ExitCheckTuple] = []
|
|
current_rate = rate
|
|
current_profit = trade.calc_profit_ratio(current_rate)
|
|
|
|
trade.adjust_min_max_rates(high or current_rate, low or current_rate)
|
|
|
|
stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
|
|
current_time=current_time,
|
|
current_profit=current_profit,
|
|
force_stoploss=force_stoploss, low=low, high=high)
|
|
|
|
# Set current rate to high for backtesting exits
|
|
current_rate = (low if trade.is_short else high) or rate
|
|
current_profit = trade.calc_profit_ratio(current_rate)
|
|
|
|
# if enter signal and ignore_roi is set, we don't need to evaluate min_roi.
|
|
roi_reached = (not (enter and self.ignore_roi_if_entry_signal)
|
|
and self.min_roi_reached(trade=trade, current_profit=current_profit,
|
|
current_time=current_time))
|
|
|
|
exit_signal = ExitType.NONE
|
|
custom_reason = ''
|
|
# use provided rate in backtesting, not high/low.
|
|
current_rate = rate
|
|
current_profit = trade.calc_profit_ratio(current_rate)
|
|
|
|
if self.use_exit_signal:
|
|
if exit_ and not enter:
|
|
exit_signal = ExitType.EXIT_SIGNAL
|
|
else:
|
|
reason_cust = strategy_safe_wrapper(self.custom_exit, default_retval=False)(
|
|
pair=trade.pair, trade=trade, current_time=current_time,
|
|
current_rate=current_rate, current_profit=current_profit)
|
|
if reason_cust:
|
|
exit_signal = ExitType.CUSTOM_EXIT
|
|
if isinstance(reason_cust, str):
|
|
custom_reason = reason_cust
|
|
if len(reason_cust) > CUSTOM_EXIT_MAX_LENGTH:
|
|
logger.warning(f'Custom exit reason returned from '
|
|
f'custom_exit is too long and was trimmed'
|
|
f'to {CUSTOM_EXIT_MAX_LENGTH} characters.')
|
|
custom_reason = reason_cust[:CUSTOM_EXIT_MAX_LENGTH]
|
|
else:
|
|
custom_reason = ''
|
|
if (
|
|
exit_signal == ExitType.CUSTOM_EXIT
|
|
or (exit_signal == ExitType.EXIT_SIGNAL
|
|
and (not self.exit_profit_only or current_profit > self.exit_profit_offset))
|
|
):
|
|
logger.debug(f"{trade.pair} - Sell signal received. "
|
|
f"exit_type=ExitType.{exit_signal.name}" +
|
|
(f", custom_reason={custom_reason}" if custom_reason else ""))
|
|
exits.append(ExitCheckTuple(exit_type=exit_signal, exit_reason=custom_reason))
|
|
|
|
# Sequence:
|
|
# Exit-signal
|
|
# Stoploss
|
|
# ROI
|
|
# Trailing stoploss
|
|
|
|
if stoplossflag.exit_type in (ExitType.STOP_LOSS, ExitType.LIQUIDATION):
|
|
|
|
logger.debug(f"{trade.pair} - Stoploss hit. exit_type={stoplossflag.exit_type}")
|
|
exits.append(stoplossflag)
|
|
|
|
if roi_reached:
|
|
logger.debug(f"{trade.pair} - Required profit reached. exit_type=ExitType.ROI")
|
|
exits.append(ExitCheckTuple(exit_type=ExitType.ROI))
|
|
|
|
if stoplossflag.exit_type == ExitType.TRAILING_STOP_LOSS:
|
|
|
|
logger.debug(f"{trade.pair} - Trailing stoploss hit.")
|
|
exits.append(stoplossflag)
|
|
|
|
return exits
|
|
|
|
def stop_loss_reached(self, current_rate: float, trade: Trade,
|
|
current_time: datetime, current_profit: float,
|
|
force_stoploss: float, low: float = None,
|
|
high: float = None) -> ExitCheckTuple:
|
|
"""
|
|
Based on current profit of the trade and configured (trailing) stoploss,
|
|
decides to exit or not
|
|
:param current_profit: current profit as ratio
|
|
:param low: Low value of this candle, only set in backtesting
|
|
:param high: High value of this candle, only set in backtesting
|
|
"""
|
|
stop_loss_value = force_stoploss if force_stoploss else self.stoploss
|
|
|
|
# Initiate stoploss with open_rate. Does nothing if stoploss is already set.
|
|
trade.adjust_stop_loss(trade.open_rate, stop_loss_value, initial=True)
|
|
|
|
dir_correct = (trade.stop_loss < (low or current_rate)
|
|
if not trade.is_short else
|
|
trade.stop_loss > (high or current_rate)
|
|
)
|
|
|
|
# Make sure current_profit is calculated using high for backtesting.
|
|
bound = (low if trade.is_short else high)
|
|
bound_profit = current_profit if not bound else trade.calc_profit_ratio(bound)
|
|
if self.use_custom_stoploss and dir_correct:
|
|
stop_loss_value = strategy_safe_wrapper(self.custom_stoploss, default_retval=None
|
|
)(pair=trade.pair, trade=trade,
|
|
current_time=current_time,
|
|
current_rate=(bound or current_rate),
|
|
current_profit=bound_profit)
|
|
# Sanity check - error cases will return None
|
|
if stop_loss_value:
|
|
# logger.info(f"{trade.pair} {stop_loss_value=} {bound_profit=}")
|
|
trade.adjust_stop_loss(bound or current_rate, stop_loss_value)
|
|
else:
|
|
logger.warning("CustomStoploss function did not return valid stoploss")
|
|
|
|
if self.trailing_stop and dir_correct:
|
|
# trailing stoploss handling
|
|
sl_offset = self.trailing_stop_positive_offset
|
|
# Make sure current_profit is calculated using high for backtesting.
|
|
|
|
# Don't update stoploss if trailing_only_offset_is_reached is true.
|
|
if not (self.trailing_only_offset_is_reached and bound_profit < sl_offset):
|
|
# Specific handling for trailing_stop_positive
|
|
if self.trailing_stop_positive is not None and bound_profit > sl_offset:
|
|
stop_loss_value = self.trailing_stop_positive
|
|
logger.debug(f"{trade.pair} - Using positive stoploss: {stop_loss_value} "
|
|
f"offset: {sl_offset:.4g} profit: {bound_profit:.2%}")
|
|
|
|
trade.adjust_stop_loss(bound or current_rate, stop_loss_value)
|
|
|
|
sl_higher_long = (trade.stop_loss >= (low or current_rate) and not trade.is_short)
|
|
sl_lower_short = (trade.stop_loss <= (high or current_rate) and trade.is_short)
|
|
liq_higher_long = (trade.liquidation_price
|
|
and trade.liquidation_price >= (low or current_rate)
|
|
and not trade.is_short)
|
|
liq_lower_short = (trade.liquidation_price
|
|
and trade.liquidation_price <= (high or current_rate)
|
|
and trade.is_short)
|
|
|
|
if (liq_higher_long or liq_lower_short):
|
|
logger.debug(f"{trade.pair} - Liquidation price hit. exit_type=ExitType.LIQUIDATION")
|
|
return ExitCheckTuple(exit_type=ExitType.LIQUIDATION)
|
|
|
|
# evaluate if the stoploss was hit if stoploss is not on exchange
|
|
# in Dry-Run, this handles stoploss logic as well, as the logic will not be different to
|
|
# regular stoploss handling.
|
|
if ((sl_higher_long or sl_lower_short) and
|
|
(not self.order_types.get('stoploss_on_exchange') or self.config['dry_run'])):
|
|
|
|
exit_type = ExitType.STOP_LOSS
|
|
|
|
# If initial stoploss is not the same as current one then it is trailing.
|
|
if trade.initial_stop_loss != trade.stop_loss:
|
|
exit_type = ExitType.TRAILING_STOP_LOSS
|
|
logger.debug(
|
|
f"{trade.pair} - HIT STOP: current price at "
|
|
f"{((high if trade.is_short else low) or current_rate):.6f}, "
|
|
f"stoploss is {trade.stop_loss:.6f}, "
|
|
f"initial stoploss was at {trade.initial_stop_loss:.6f}, "
|
|
f"trade opened at {trade.open_rate:.6f}")
|
|
|
|
return ExitCheckTuple(exit_type=exit_type)
|
|
|
|
return ExitCheckTuple(exit_type=ExitType.NONE)
|
|
|
|
def min_roi_reached_entry(self, trade_dur: int) -> Tuple[Optional[int], Optional[float]]:
|
|
"""
|
|
Based on trade duration defines the ROI entry that may have been reached.
|
|
:param trade_dur: trade duration in minutes
|
|
:return: minimal ROI entry value or None if none proper ROI entry was found.
|
|
"""
|
|
# Get highest entry in ROI dict where key <= trade-duration
|
|
roi_list = list(filter(lambda x: x <= trade_dur, self.minimal_roi.keys()))
|
|
if not roi_list:
|
|
return None, None
|
|
roi_entry = max(roi_list)
|
|
return roi_entry, self.minimal_roi[roi_entry]
|
|
|
|
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
|
|
"""
|
|
Based on trade duration, current profit of the trade and ROI configuration,
|
|
decides whether bot should exit.
|
|
:param current_profit: current profit as ratio
|
|
:return: True if bot should exit at current rate
|
|
"""
|
|
# Check if time matches and current rate is above threshold
|
|
trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
|
|
_, roi = self.min_roi_reached_entry(trade_dur)
|
|
if roi is None:
|
|
return False
|
|
else:
|
|
return current_profit > roi
|
|
|
|
def ft_check_timed_out(self, trade: Trade, order: Order,
|
|
current_time: datetime) -> bool:
|
|
"""
|
|
FT Internal method.
|
|
Check if timeout is active, and if the order is still open and timed out
|
|
"""
|
|
side = 'entry' if order.ft_order_side == trade.entry_side else 'exit'
|
|
|
|
timeout = self.config.get('unfilledtimeout', {}).get(side)
|
|
if timeout is not None:
|
|
timeout_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
|
|
timeout_kwargs = {timeout_unit: -timeout}
|
|
timeout_threshold = current_time + timedelta(**timeout_kwargs)
|
|
timedout = (order.status == 'open' and order.order_date_utc < timeout_threshold)
|
|
if timedout:
|
|
return True
|
|
time_method = (self.check_exit_timeout if order.side == trade.exit_side
|
|
else self.check_entry_timeout)
|
|
|
|
return strategy_safe_wrapper(time_method,
|
|
default_retval=False)(
|
|
pair=trade.pair, trade=trade, order=order,
|
|
current_time=current_time)
|
|
|
|
def advise_all_indicators(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
|
|
"""
|
|
Populates indicators for given candle (OHLCV) data (for multiple pairs)
|
|
Does not run advise_entry or advise_exit!
|
|
Used by optimize operations only, not during dry / live runs.
|
|
Using .copy() to get a fresh copy of the dataframe for every strategy run.
|
|
Also copy on output to avoid PerformanceWarnings pandas 1.3.0 started to show.
|
|
Has positive effects on memory usage for whatever reason - also when
|
|
using only one strategy.
|
|
"""
|
|
return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy()
|
|
for pair, pair_data in data.items()}
|
|
|
|
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Populate indicators that will be used in the Buy, Sell, short, exit_short strategy
|
|
This method should not be overridden.
|
|
:param dataframe: Dataframe with data from the exchange
|
|
:param metadata: Additional information, like the currently traded pair
|
|
:return: a Dataframe with all mandatory indicators for the strategies
|
|
"""
|
|
logger.debug(f"Populating indicators for pair {metadata.get('pair')}.")
|
|
|
|
# call populate_indicators_Nm() which were tagged with @informative decorator.
|
|
for inf_data, populate_fn in self._ft_informative:
|
|
dataframe = _create_and_merge_informative_pair(
|
|
self, dataframe, metadata, inf_data, populate_fn)
|
|
|
|
return self.populate_indicators(dataframe, metadata)
|
|
|
|
def advise_entry(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Based on TA indicators, populates the entry order signal for the given dataframe
|
|
This method should not be overridden.
|
|
:param dataframe: DataFrame
|
|
:param metadata: Additional information dictionary, with details like the
|
|
currently traded pair
|
|
:return: DataFrame with buy column
|
|
"""
|
|
|
|
logger.debug(f"Populating enter signals for pair {metadata.get('pair')}.")
|
|
|
|
df = self.populate_entry_trend(dataframe, metadata)
|
|
if 'enter_long' not in df.columns:
|
|
df = df.rename({'buy': 'enter_long', 'buy_tag': 'enter_tag'}, axis='columns')
|
|
|
|
return df
|
|
|
|
def advise_exit(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Based on TA indicators, populates the exit order signal for the given dataframe
|
|
This method should not be overridden.
|
|
:param dataframe: DataFrame
|
|
:param metadata: Additional information dictionary, with details like the
|
|
currently traded pair
|
|
:return: DataFrame with exit column
|
|
"""
|
|
logger.debug(f"Populating exit signals for pair {metadata.get('pair')}.")
|
|
df = self.populate_exit_trend(dataframe, metadata)
|
|
if 'exit_long' not in df.columns:
|
|
df = df.rename({'sell': 'exit_long'}, axis='columns')
|
|
return df
|