497 lines
21 KiB
Python
497 lines
21 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, timezone
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from enum import Enum
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from typing import Dict, List, NamedTuple, Optional, Tuple
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import warnings
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import arrow
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from pandas import DataFrame
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.exchange import timeframe_to_minutes
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from freqtrade.persistence import Trade
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from freqtrade.wallets import Wallets
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logger = logging.getLogger(__name__)
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class SignalType(Enum):
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"""
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Enum to distinguish between buy and sell signals
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"""
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BUY = "buy"
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SELL = "sell"
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class SellType(Enum):
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"""
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Enum to distinguish between sell reasons
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"""
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ROI = "roi"
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STOP_LOSS = "stop_loss"
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STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"
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TRAILING_STOP_LOSS = "trailing_stop_loss"
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SELL_SIGNAL = "sell_signal"
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FORCE_SELL = "force_sell"
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EMERGENCY_SELL = "emergency_sell"
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NONE = ""
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class SellCheckTuple(NamedTuple):
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"""
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NamedTuple for Sell type + reason
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"""
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sell_flag: bool
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sell_type: SellType
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class IStrategy(ABC):
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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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ticker_interval -> str: value of the ticker interval to use for 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
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# Version 2 populate_* include metadata dict
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INTERFACE_VERSION: int = 2
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_populate_fun_len: int = 0
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_buy_fun_len: int = 0
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_sell_fun_len: int = 0
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# associated minimal roi
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minimal_roi: Dict
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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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# associated ticker interval
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ticker_interval: str
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# Optional order types
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order_types: Dict = {
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'buy': 'limit',
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'sell': '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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'buy': 'gtc',
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'sell': '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 = 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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# 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: Optional[DataProvider] = None
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wallets: Optional[Wallets] = None
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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: dict) -> 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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self._pair_locked_until: Dict[str, datetime] = {}
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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 and Sell strategy
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
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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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@abstractmethod
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy 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 buy column
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"""
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@abstractmethod
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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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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def informative_pairs(self) -> List[Tuple[str, str]]:
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"""
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Define additional, informative pair/interval combinations to be cached from the exchange.
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These pair/interval combinations are non-tradeable, unless they are part
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of the whitelist as well.
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For more information, please consult the documentation
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:return: List of tuples in the format (pair, interval)
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Sample: return [("ETH/USDT", "5m"),
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("BTC/USDT", "15m"),
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]
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"""
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return []
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def get_strategy_name(self) -> str:
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"""
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Returns strategy class name
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"""
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return self.__class__.__name__
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def lock_pair(self, pair: str, until: datetime) -> None:
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"""
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Locks pair until a given timestamp happens.
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Locked pairs are not analyzed, and are prevented from opening new trades.
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Locks can only count up (allowing users to lock pairs for a longer period of time).
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To remove a lock from a pair, use `unlock_pair()`
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:param pair: Pair to lock
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:param until: datetime in UTC until the pair should be blocked from opening new trades.
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Needs to be timezone aware `datetime.now(timezone.utc)`
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"""
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if pair not in self._pair_locked_until or self._pair_locked_until[pair] < until:
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self._pair_locked_until[pair] = until
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def unlock_pair(self, pair: str) -> None:
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"""
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Unlocks a pair previously locked using lock_pair.
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Not used by freqtrade itself, but intended to be used if users lock pairs
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manually from within the strategy, to allow an easy way to unlock pairs.
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:param pair: Unlock pair to allow trading again
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"""
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if pair in self._pair_locked_until:
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del self._pair_locked_until[pair]
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def is_pair_locked(self, pair: str) -> bool:
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"""
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Checks if a pair is currently locked
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"""
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if pair not in self._pair_locked_until:
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return False
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return self._pair_locked_until[pair] >= datetime.now(timezone.utc)
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def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Parses the given ticker history and returns a populated DataFrame
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add several TA indicators and buy signal to it
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:param dataframe: Dataframe containing ticker data
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:param metadata: Metadata dictionary with additional data (e.g. 'pair')
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:return: DataFrame with ticker data and indicator data
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"""
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logger.debug("TA Analysis Launched")
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dataframe = self.advise_indicators(dataframe, metadata)
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dataframe = self.advise_buy(dataframe, metadata)
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dataframe = self.advise_sell(dataframe, metadata)
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return dataframe
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def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Parses the given ticker history and returns a populated DataFrame
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add several TA indicators and buy signal to it
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WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
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:param dataframe: Dataframe containing ticker data
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:param metadata: Metadata dictionary with additional data (e.g. 'pair')
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:return: DataFrame with ticker data and indicator data
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"""
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pair = str(metadata.get('pair'))
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# Test if seen this pair and last candle before.
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# always run if process_only_new_candles is set to false
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if (not self.process_only_new_candles or
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self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
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# Defs that only make change on new candle data.
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dataframe = self.analyze_ticker(dataframe, metadata)
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self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
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else:
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logger.debug("Skipping TA Analysis for already analyzed candle")
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dataframe['buy'] = 0
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dataframe['sell'] = 0
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# Other Defs in strategy that want to be called every loop here
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# twitter_sell = self.watch_twitter_feed(dataframe, metadata)
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logger.debug("Loop Analysis Launched")
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return dataframe
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def get_signal(self, pair: str, interval: str,
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dataframe: DataFrame) -> Tuple[bool, bool]:
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"""
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Calculates current signal based several technical analysis indicators
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:param pair: pair in format ANT/BTC
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:param interval: Interval to use (in min)
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:param dataframe: Dataframe to analyze
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:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
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"""
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if not isinstance(dataframe, DataFrame) or dataframe.empty:
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logger.warning('Empty ticker history for pair %s', pair)
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return False, False
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try:
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dataframe = self._analyze_ticker_internal(dataframe, {'pair': pair})
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except ValueError as error:
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logger.warning(
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'Unable to analyze ticker for pair %s: %s',
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pair,
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str(error)
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)
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return False, False
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except Exception as error:
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logger.exception(
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'Unexpected error when analyzing ticker for pair %s: %s',
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pair,
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str(error)
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)
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return False, False
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if dataframe.empty:
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logger.warning('Empty dataframe for pair %s', pair)
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return False, False
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latest = dataframe.iloc[-1]
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# Check if dataframe is out of date
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signal_date = arrow.get(latest['date'])
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interval_minutes = timeframe_to_minutes(interval)
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offset = self.config.get('exchange', {}).get('outdated_offset', 5)
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if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + offset))):
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logger.warning(
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'Outdated history for pair %s. Last tick is %s minutes old',
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pair,
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(arrow.utcnow() - signal_date).seconds // 60
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)
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return False, False
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(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
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logger.debug(
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'trigger: %s (pair=%s) buy=%s sell=%s',
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latest['date'],
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pair,
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str(buy),
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str(sell)
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)
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return buy, sell
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def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
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sell: bool, low: float = None, high: float = None,
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force_stoploss: float = 0) -> SellCheckTuple:
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"""
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This function evaluates if one of the conditions required to trigger a sell
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has been reached, which can either be a stop-loss, ROI or sell-signal.
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:param low: Only used during backtesting to simulate stoploss
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:param high: Only used during backtesting, to simulate ROI
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:param force_stoploss: Externally provided stoploss
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:return: True if trade should be sold, False otherwise
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"""
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# Set current rate to low for backtesting sell
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current_rate = low or rate
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current_profit = trade.calc_profit_ratio(current_rate)
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trade.adjust_min_max_rates(high or current_rate)
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stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
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current_time=date, current_profit=current_profit,
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force_stoploss=force_stoploss, high=high)
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if stoplossflag.sell_flag:
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logger.debug(f"{trade.pair} - Stoploss hit. sell_flag=True, "
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f"sell_type={stoplossflag.sell_type}")
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return stoplossflag
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# Set current rate to high for backtesting sell
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current_rate = high or rate
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current_profit = trade.calc_profit_ratio(current_rate)
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config_ask_strategy = self.config.get('ask_strategy', {})
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if buy and config_ask_strategy.get('ignore_roi_if_buy_signal', False):
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# This one is noisy, commented out
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# logger.debug(f"{trade.pair} - Buy signal still active. sell_flag=False")
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return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
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# Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
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if self.min_roi_reached(trade=trade, current_profit=current_profit, current_time=date):
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logger.debug(f"{trade.pair} - Required profit reached. sell_flag=True, "
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f"sell_type=SellType.ROI")
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return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
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if config_ask_strategy.get('sell_profit_only', False):
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# This one is noisy, commented out
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# logger.debug(f"{trade.pair} - Checking if trade is profitable...")
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if trade.calc_profit(rate=rate) <= 0:
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# This one is noisy, commented out
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# logger.debug(f"{trade.pair} - Trade is not profitable. sell_flag=False")
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return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
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if sell and not buy and config_ask_strategy.get('use_sell_signal', True):
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logger.debug(f"{trade.pair} - Sell signal received. sell_flag=True, "
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f"sell_type=SellType.SELL_SIGNAL")
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return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)
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# This one is noisy, commented out...
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# logger.debug(f"{trade.pair} - No sell signal. sell_flag=False")
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return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
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def stop_loss_reached(self, current_rate: float, trade: Trade,
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current_time: datetime, current_profit: float,
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force_stoploss: float, high: float = None) -> SellCheckTuple:
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"""
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Based on current profit of the trade and configured (trailing) stoploss,
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decides to sell or not
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:param current_profit: current profit in percent
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"""
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stop_loss_value = force_stoploss if force_stoploss else self.stoploss
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# Initiate stoploss with open_rate. Does nothing if stoploss is already set.
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trade.adjust_stop_loss(trade.open_rate, stop_loss_value, initial=True)
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if self.trailing_stop:
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# trailing stoploss handling
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sl_offset = self.trailing_stop_positive_offset
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# Make sure current_profit is calculated using high for backtesting.
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high_profit = current_profit if not high else trade.calc_profit_ratio(high)
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# Don't update stoploss if trailing_only_offset_is_reached is true.
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if not (self.trailing_only_offset_is_reached and high_profit < sl_offset):
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# Specific handling for trailing_stop_positive
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if self.trailing_stop_positive is not None and high_profit > sl_offset:
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stop_loss_value = self.trailing_stop_positive
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logger.debug(f"{trade.pair} - Using positive stoploss: {stop_loss_value} "
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f"offset: {sl_offset:.4g} profit: {current_profit:.4f}%")
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trade.adjust_stop_loss(high or current_rate, stop_loss_value)
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# evaluate if the stoploss was hit if stoploss is not on exchange
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# in Dry-Run, this handles stoploss logic as well, as the logic will not be different to
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# regular stoploss handling.
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if ((self.stoploss is not None) and
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(trade.stop_loss >= current_rate) and
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(not self.order_types.get('stoploss_on_exchange') or self.config['dry_run'])):
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sell_type = SellType.STOP_LOSS
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# If initial stoploss is not the same as current one then it is trailing.
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if trade.initial_stop_loss != trade.stop_loss:
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sell_type = SellType.TRAILING_STOP_LOSS
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logger.debug(
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f"{trade.pair} - HIT STOP: current price at {current_rate:.6f}, "
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f"stoploss is {trade.stop_loss:.6f}, "
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f"initial stoploss was at {trade.initial_stop_loss:.6f}, "
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f"trade opened at {trade.open_rate:.6f}")
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logger.debug(f"{trade.pair} - Trailing stop saved "
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f"{trade.stop_loss - trade.initial_stop_loss:.6f}")
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return SellCheckTuple(sell_flag=True, sell_type=sell_type)
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return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
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def min_roi_reached_entry(self, trade_dur: int) -> Tuple[Optional[int], Optional[float]]:
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"""
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Based on trade duration defines the ROI entry that may have been reached.
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:param trade_dur: trade duration in minutes
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:return: minimal ROI entry value or None if none proper ROI entry was found.
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"""
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# Get highest entry in ROI dict where key <= trade-duration
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roi_list = list(filter(lambda x: x <= trade_dur, self.minimal_roi.keys()))
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if not roi_list:
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return None, None
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roi_entry = max(roi_list)
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return roi_entry, self.minimal_roi[roi_entry]
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def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
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"""
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Based on trade duration, current price and ROI configuration, decides whether bot should
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sell. Requires current_profit to be in percent!!
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:return: True if bot should sell at current rate
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"""
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# Check if time matches and current rate is above threshold
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trade_dur = int((current_time.timestamp() - trade.open_date.timestamp()) // 60)
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_, roi = self.min_roi_reached_entry(trade_dur)
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if roi is None:
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return False
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else:
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return current_profit > roi
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def tickerdata_to_dataframe(self, tickerdata: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
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"""
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Creates a dataframe and populates indicators for given ticker data
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Used by optimize operations only, not during dry / live runs.
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"""
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return {pair: self.advise_indicators(pair_data, {'pair': pair})
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for pair, pair_data in tickerdata.items()}
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def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Populate indicators that will be used in the Buy and Sell strategy
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This method should not be overridden.
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:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
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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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logger.debug(f"Populating indicators for pair {metadata.get('pair')}.")
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if self._populate_fun_len == 2:
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warnings.warn("deprecated - check out the Sample strategy to see "
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"the current function headers!", DeprecationWarning)
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return self.populate_indicators(dataframe) # type: ignore
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else:
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return self.populate_indicators(dataframe, metadata)
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def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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This method should not be overridden.
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:param dataframe: DataFrame
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:param pair: Additional information, like the currently traded pair
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:return: DataFrame with buy column
|
|
"""
|
|
logger.debug(f"Populating buy signals for pair {metadata.get('pair')}.")
|
|
if self._buy_fun_len == 2:
|
|
warnings.warn("deprecated - check out the Sample strategy to see "
|
|
"the current function headers!", DeprecationWarning)
|
|
return self.populate_buy_trend(dataframe) # type: ignore
|
|
else:
|
|
return self.populate_buy_trend(dataframe, metadata)
|
|
|
|
def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Based on TA indicators, populates the sell signal for the given dataframe
|
|
This method should not be overridden.
|
|
:param dataframe: DataFrame
|
|
:param pair: Additional information, like the currently traded pair
|
|
:return: DataFrame with sell column
|
|
"""
|
|
logger.debug(f"Populating sell signals for pair {metadata.get('pair')}.")
|
|
if self._sell_fun_len == 2:
|
|
warnings.warn("deprecated - check out the Sample strategy to see "
|
|
"the current function headers!", DeprecationWarning)
|
|
return self.populate_sell_trend(dataframe) # type: ignore
|
|
else:
|
|
return self.populate_sell_trend(dataframe, metadata)
|