693 lines
30 KiB
Python
693 lines
30 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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import warnings
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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 arrow
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from pandas import DataFrame
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from freqtrade.constants import ListPairsWithTimeframes
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.exceptions import OperationalException, StrategyError
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from freqtrade.exchange import timeframe_to_minutes
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from freqtrade.exchange.exchange import timeframe_to_next_date
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from freqtrade.persistence import PairLocks, Trade
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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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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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def __str__(self):
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# explicitly convert to String to help with exporting data.
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return self.value
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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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timeframe -> str: value of the timeframe (ticker interval) 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
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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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custom_stoploss: bool = False
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# associated timeframe
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ticker_interval: str # DEPRECATED
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timeframe: 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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# 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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# 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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# 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: 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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@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: 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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@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 check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
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"""
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Check buy timeout function callback.
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This method can be used to override the buy-timeout.
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It is called whenever a limit buy 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 dictionary as returned from CCXT.
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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 cancelled.
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"""
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return False
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def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
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"""
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Check sell timeout function callback.
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This method can be used to override the sell-timeout.
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It is called whenever a limit sell 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 dictionary as returned from CCXT.
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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 sell-order is cancelled.
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"""
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return False
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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 confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, **kwargs) -> bool:
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"""
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Called right before placing a buy 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.
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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 (quote) 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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:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
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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, sell_reason: str, **kwargs) -> bool:
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"""
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Called right before placing a regular sell 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 sold.
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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 quote currency.
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:param rate: Rate that's going to be used when using limit orders
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:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
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:param sell_reason: Sell reason.
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Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
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'sell_signal', 'force_sell', 'emergency_sell']
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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 sell-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 stoploss_value(self, pair: str, trade: Trade, current_rate: float, current_profit: float,
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**kwargs) -> float:
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"""
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Define custom stoploss logic
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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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:param pair: Pair that's about to be sold.
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:param trade: trade object.
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:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
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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 currentrate
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"""
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return self.stoploss
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def informative_pairs(self) -> ListPairsWithTimeframes:
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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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###
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# END - Intended to be overridden by strategy
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###
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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, reason: str = None) -> 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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:param reason: Optional string explaining why the pair was locked.
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"""
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PairLocks.lock_pair(pair, until, reason)
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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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PairLocks.unlock_pair(pair, datetime.now(timezone.utc))
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def is_pair_locked(self, pair: str, candle_date: datetime = None) -> bool:
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"""
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Checks if a pair is currently locked
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The 2nd, optional parameter ensures that locks are applied until the new candle arrives,
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and not stop at 14:00:00 - while the next candle arrives at 14:00:02 leaving a gap
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of 2 seconds for a buy to happen on an old signal.
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:param: pair: "Pair to check"
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:param candle_date: Date of the last candle. Optional, defaults to current date
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:returns: locking state of the pair in question.
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"""
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if not candle_date:
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# Simple call ...
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return PairLocks.is_pair_locked(pair)
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else:
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lock_time = timeframe_to_next_date(self.timeframe, candle_date)
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return PairLocks.is_pair_locked(pair, lock_time)
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def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Parses the given candle (OHLCV) data 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 data from exchange
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:param metadata: Metadata dictionary with additional data (e.g. 'pair')
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:return: DataFrame of candle (OHLCV) data with indicator data and signals added
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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 candle (OHLCV) data 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 data from exchange
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:param metadata: Metadata dictionary with additional data (e.g. 'pair')
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:return: DataFrame of candle (OHLCV) data with indicator data and signals added
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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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if self.dp:
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self.dp._set_cached_df(pair, self.timeframe, dataframe)
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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 analyze_pair(self, pair: str) -> None:
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"""
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Fetch data for this pair from dataprovider and analyze.
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Stores the dataframe into the dataprovider.
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The analyzed dataframe is then accessible via `dp.get_analyzed_dataframe()`.
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:param pair: Pair to analyze.
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"""
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if not self.dp:
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raise OperationalException("DataProvider not found.")
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dataframe = self.dp.ohlcv(pair, self.timeframe)
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if not isinstance(dataframe, DataFrame) or dataframe.empty:
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logger.warning('Empty candle (OHLCV) data for pair %s', pair)
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return
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try:
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df_len, df_close, df_date = self.preserve_df(dataframe)
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dataframe = strategy_safe_wrapper(
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self._analyze_ticker_internal, message=""
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)(dataframe, {'pair': pair})
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self.assert_df(dataframe, df_len, df_close, df_date)
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except StrategyError as error:
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logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
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return
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if dataframe.empty:
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logger.warning('Empty dataframe for pair %s', pair)
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return
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def analyze(self, pairs: List[str]) -> None:
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"""
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Analyze all pairs using analyze_pair().
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:param pairs: List of pairs to analyze
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"""
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for pair in pairs:
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self.analyze_pair(pair)
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@staticmethod
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def preserve_df(dataframe: DataFrame) -> Tuple[int, float, datetime]:
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""" keep some data for dataframes """
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return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
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def assert_df(self, dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
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"""
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Ensure dataframe (length, last candle) was not modified, and has all elements we need.
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"""
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message = ""
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if df_len != len(dataframe):
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message = "length"
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elif df_close != dataframe["close"].iloc[-1]:
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message = "last close price"
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elif df_date != dataframe["date"].iloc[-1]:
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message = "last date"
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if message:
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if self.disable_dataframe_checks:
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logger.warning(f"Dataframe returned from strategy has mismatching {message}.")
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else:
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raise StrategyError(f"Dataframe returned from strategy has mismatching {message}.")
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def get_signal(self, pair: str, timeframe: str, dataframe: DataFrame) -> Tuple[bool, bool]:
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"""
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Calculates current signal based based on the buy / sell columns of the dataframe.
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Used by Bot to get the signal to buy or sell
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:param pair: pair in format ANT/BTC
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:param timeframe: timeframe to use
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:param dataframe: Analyzed dataframe to get signal from.
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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(f'Empty candle (OHLCV) data for pair {pair}')
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return False, False
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latest_date = dataframe['date'].max()
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latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
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# Explicitly convert to arrow object to ensure the below comparison does not fail
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latest_date = arrow.get(latest_date)
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# Check if dataframe is out of date
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timeframe_minutes = timeframe_to_minutes(timeframe)
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offset = self.config.get('exchange', {}).get('outdated_offset', 5)
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if latest_date < (arrow.utcnow().shift(minutes=-(timeframe_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, int((arrow.utcnow() - latest_date).total_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('trigger: %s (pair=%s) buy=%s sell=%s',
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latest['date'], pair, str(buy), str(sell))
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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.
|
|
:param low: Only used during backtesting to simulate stoploss
|
|
:param high: Only used during backtesting, to simulate ROI
|
|
:param force_stoploss: Externally provided stoploss
|
|
:return: True if trade should be sold, False otherwise
|
|
"""
|
|
# Set current rate to low for backtesting sell
|
|
current_rate = low or rate
|
|
current_profit = trade.calc_profit_ratio(current_rate)
|
|
|
|
trade.adjust_min_max_rates(high or current_rate)
|
|
|
|
stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
|
|
current_time=date, current_profit=current_profit,
|
|
force_stoploss=force_stoploss, high=high)
|
|
|
|
# Set current rate to high for backtesting sell
|
|
current_rate = high or rate
|
|
current_profit = trade.calc_profit_ratio(current_rate)
|
|
config_ask_strategy = self.config.get('ask_strategy', {})
|
|
|
|
# if buy signal and ignore_roi is set, we don't need to evaluate min_roi.
|
|
roi_reached = (not (buy and config_ask_strategy.get('ignore_roi_if_buy_signal', False))
|
|
and self.min_roi_reached(trade=trade, current_profit=current_profit,
|
|
current_time=date))
|
|
|
|
if config_ask_strategy.get('sell_profit_only', False) and trade.calc_profit(rate=rate) <= 0:
|
|
# Negative profits and sell_profit_only - ignore sell signal
|
|
sell_signal = False
|
|
else:
|
|
sell_signal = sell and not buy and config_ask_strategy.get('use_sell_signal', True)
|
|
# TODO: return here if sell-signal should be favored over ROI
|
|
|
|
# Start evaluations
|
|
# Sequence:
|
|
# ROI (if not stoploss)
|
|
# Sell-signal
|
|
# Stoploss
|
|
if roi_reached and stoplossflag.sell_type != SellType.STOP_LOSS:
|
|
logger.debug(f"{trade.pair} - Required profit reached. sell_flag=True, "
|
|
f"sell_type=SellType.ROI")
|
|
return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
|
|
|
|
if sell_signal:
|
|
logger.debug(f"{trade.pair} - Sell signal received. sell_flag=True, "
|
|
f"sell_type=SellType.SELL_SIGNAL")
|
|
return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)
|
|
|
|
if stoplossflag.sell_flag:
|
|
|
|
logger.debug(f"{trade.pair} - Stoploss hit. sell_flag=True, "
|
|
f"sell_type={stoplossflag.sell_type}")
|
|
return stoplossflag
|
|
|
|
# This one is noisy, commented out...
|
|
# logger.debug(f"{trade.pair} - No sell signal. sell_flag=False")
|
|
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
|
|
|
|
def stop_loss_reached(self, current_rate: float, trade: Trade,
|
|
current_time: datetime, current_profit: float,
|
|
force_stoploss: float, high: float = None) -> SellCheckTuple:
|
|
"""
|
|
Based on current profit of the trade and configured (trailing) stoploss,
|
|
decides to sell or not
|
|
:param current_profit: current profit as ratio
|
|
"""
|
|
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)
|
|
|
|
if self.custom_stoploss:
|
|
stop_loss_value = strategy_safe_wrapper(self.stoploss_value, default_retval=None
|
|
)(pair=trade.pair, trade=trade,
|
|
current_rate=current_rate,
|
|
current_profit=current_profit)
|
|
# Sanity check - error cases will return None
|
|
if stop_loss_value:
|
|
# logger.info(f"{trade.pair} {stop_loss_value=} {current_profit=}")
|
|
trade.adjust_stop_loss(current_rate, stop_loss_value)
|
|
else:
|
|
logger.warning("CustomStoploss function did not return valid stoploss")
|
|
|
|
if self.trailing_stop:
|
|
# trailing stoploss handling
|
|
sl_offset = self.trailing_stop_positive_offset
|
|
|
|
# Make sure current_profit is calculated using high for backtesting.
|
|
high_profit = current_profit if not high else trade.calc_profit_ratio(high)
|
|
|
|
# Don't update stoploss if trailing_only_offset_is_reached is true.
|
|
if not (self.trailing_only_offset_is_reached and high_profit < sl_offset):
|
|
# Specific handling for trailing_stop_positive
|
|
if self.trailing_stop_positive is not None and high_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: {current_profit:.4f}%")
|
|
|
|
trade.adjust_stop_loss(high or current_rate, stop_loss_value)
|
|
|
|
# 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 ((trade.stop_loss >= current_rate) and
|
|
(not self.order_types.get('stoploss_on_exchange') or self.config['dry_run'])):
|
|
|
|
sell_type = SellType.STOP_LOSS
|
|
|
|
# If initial stoploss is not the same as current one then it is trailing.
|
|
if trade.initial_stop_loss != trade.stop_loss:
|
|
sell_type = SellType.TRAILING_STOP_LOSS
|
|
logger.debug(
|
|
f"{trade.pair} - HIT STOP: current price at {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}")
|
|
logger.debug(f"{trade.pair} - Trailing stop saved "
|
|
f"{trade.stop_loss - trade.initial_stop_loss:.6f}")
|
|
|
|
return SellCheckTuple(sell_flag=True, sell_type=sell_type)
|
|
|
|
return SellCheckTuple(sell_flag=False, sell_type=SellType.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 sell.
|
|
:param current_profit: current profit as ratio
|
|
:return: True if bot should sell at current rate
|
|
"""
|
|
# Check if time matches and current rate is above threshold
|
|
trade_dur = int((current_time.timestamp() - trade.open_date.timestamp()) // 60)
|
|
_, roi = self.min_roi_reached_entry(trade_dur)
|
|
if roi is None:
|
|
return False
|
|
else:
|
|
return current_profit > roi
|
|
|
|
def ohlcvdata_to_dataframe(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
|
|
"""
|
|
Populates indicators for given candle (OHLCV) data (for multiple pairs)
|
|
Does not run advice_buy or advise_sell!
|
|
Used by optimize operations only, not during dry / live runs.
|
|
Using .copy() to get a fresh copy of the dataframe for every strategy run.
|
|
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})
|
|
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 and Sell 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')}.")
|
|
if self._populate_fun_len == 2:
|
|
warnings.warn("deprecated - check out the Sample strategy to see "
|
|
"the current function headers!", DeprecationWarning)
|
|
return self.populate_indicators(dataframe) # type: ignore
|
|
else:
|
|
return self.populate_indicators(dataframe, metadata)
|
|
|
|
def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Based on TA indicators, populates the buy 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 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)
|