stable/freqtrade/strategy/interface.py

"""
IStrategy interface
This module defines the interface to apply for strategies
"""
import logging
from abc import ABC, abstractmethod
from datetime import datetime
from enum import Enum
from typing import Dict, List, NamedTuple, Optional, Tuple
import warnings

import arrow
from pandas import DataFrame

from freqtrade.data.dataprovider import DataProvider
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.persistence import Trade
from freqtrade.wallets import Wallets


logger = logging.getLogger(__name__)


class SignalType(Enum):
    """
    Enum to distinguish between buy and sell signals
    """
    BUY = "buy"
    SELL = "sell"


class SellType(Enum):
    """
    Enum to distinguish between sell reasons
    """
    ROI = "roi"
    STOP_LOSS = "stop_loss"
    STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"
    TRAILING_STOP_LOSS = "trailing_stop_loss"
    SELL_SIGNAL = "sell_signal"
    FORCE_SELL = "force_sell"
    NONE = ""


class SellCheckTuple(NamedTuple):
    """
    NamedTuple for Sell type + reason
    """
    sell_flag: bool
    sell_type: SellType


class IStrategy(ABC):
    """
    Interface for freqtrade strategies
    Defines the mandatory structure must follow any custom strategies

    Attributes you can use:
        minimal_roi -> Dict: Minimal ROI designed for the strategy
        stoploss -> float: optimal stoploss designed for the strategy
        ticker_interval -> str: value of the ticker interval to use for the strategy
    """

    _populate_fun_len: int = 0
    _buy_fun_len: int = 0
    _sell_fun_len: int = 0
    # associated minimal roi
    minimal_roi: Dict

    # associated stoploss
    stoploss: float

    # trailing stoploss
    trailing_stop: bool = False
    trailing_stop_positive: float
    trailing_stop_positive_offset: float
    trailing_only_offset_is_reached = False

    # associated ticker interval
    ticker_interval: str

    # Optional order types
    order_types: Dict = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'limit',
        'stoploss_on_exchange': False,
        'stoploss_on_exchange_interval': 60,
    }

    # Optional time in force
    order_time_in_force: Dict = {
        'buy': 'gtc',
        'sell': 'gtc',
    }

    # run "populate_indicators" only for new candle
    process_only_new_candles: bool = False

    # Class level variables (intentional) containing
    # the dataprovider (dp) (access to other candles, historic data, ...)
    # and wallets - access to the current balance.
    dp: DataProvider
    wallets: Wallets

    def __init__(self, config: dict) -> None:
        self.config = config
        # Dict to determine if analysis is necessary
        self._last_candle_seen_per_pair: Dict[str, datetime] = {}

    @abstractmethod
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Populate indicators that will be used in the Buy and Sell strategy
        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """

    @abstractmethod
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """

    @abstractmethod
    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with sell column
        """

    def informative_pairs(self) -> List[Tuple[str, str]]:
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, 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 get_strategy_name(self) -> str:
        """
        Returns strategy class name
        """
        return self.__class__.__name__

    def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Parses the given ticker history and returns a populated DataFrame
        add several TA indicators and buy signal to it
        :param dataframe: Dataframe containing ticker data
        :param metadata: Metadata dictionary with additional data (e.g. 'pair')
        :return: DataFrame with ticker data and indicator data
        """
        logger.debug("TA Analysis Launched")
        dataframe = self.advise_indicators(dataframe, metadata)
        dataframe = self.advise_buy(dataframe, metadata)
        dataframe = self.advise_sell(dataframe, metadata)
        return dataframe

    def _analyze_ticker_int(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Parses the given ticker history 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 ticker data
        :param metadata: Metadata dictionary with additional data (e.g. 'pair')
        :return: DataFrame with ticker data and indicator data
        """

        pair = str(metadata.get('pair'))

        # 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
                self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
            # 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']
        else:
            logger.debug("Skipping TA Analysis for already analyzed candle")
            dataframe['buy'] = 0
            dataframe['sell'] = 0

        # Other Defs in strategy that want to be called every loop here
        # twitter_sell = self.watch_twitter_feed(dataframe, metadata)
        logger.debug("Loop Analysis Launched")

        return dataframe

    def get_signal(self, pair: str, interval: str,
                   dataframe: DataFrame) -> Tuple[bool, bool]:
        """
        Calculates current signal based several technical analysis indicators
        :param pair: pair in format ANT/BTC
        :param interval: Interval to use (in min)
        :param dataframe: Dataframe to analyze
        :return: (Buy, Sell) A bool-tuple indicating buy/sell signal
        """
        if not isinstance(dataframe, DataFrame) or dataframe.empty:
            logger.warning('Empty ticker history for pair %s', pair)
            return False, False

        try:
            dataframe = self._analyze_ticker_int(dataframe, {'pair': pair})
        except ValueError as error:
            logger.warning(
                'Unable to analyze ticker for pair %s: %s',
                pair,
                str(error)
            )
            return False, False
        except Exception as error:
            logger.exception(
                'Unexpected error when analyzing ticker for pair %s: %s',
                pair,
                str(error)
            )
            return False, False

        if dataframe.empty:
            logger.warning('Empty dataframe for pair %s', pair)
            return False, False

        latest = dataframe.iloc[-1]

        # Check if dataframe is out of date
        signal_date = arrow.get(latest['date'])
        interval_minutes = timeframe_to_minutes(interval)
        offset = self.config.get('exchange', {}).get('outdated_offset', 5)
        if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + offset))):
            logger.warning(
                'Outdated history for pair %s. Last tick is %s minutes old',
                pair,
                (arrow.utcnow() - signal_date).seconds // 60
            )
            return False, False

        (buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
        logger.debug(
            'trigger: %s (pair=%s) buy=%s sell=%s',
            latest['date'],
            pair,
            str(buy),
            str(sell)
        )
        return buy, sell

    def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
                    sell: bool, low: float = None, high: float = None,
                    force_stoploss: float = 0) -> SellCheckTuple:
        """
        This function evaluate if on the condition required to trigger a sell has been reached
        if the threshold is reached and updates the trade record.
        :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_percent(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)

        if stoplossflag.sell_flag:
            return stoplossflag

        # Set current rate to high for backtesting sell
        current_rate = high or rate
        current_profit = trade.calc_profit_percent(current_rate)
        experimental = self.config.get('experimental', {})

        if buy and experimental.get('ignore_roi_if_buy_signal', False):
            logger.debug('Buy signal still active - not selling.')
            return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)

        # Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
        if self.min_roi_reached(trade=trade, current_profit=current_profit, current_time=date):
            logger.debug('Required profit reached. Selling..')
            return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)

        if experimental.get('sell_profit_only', False):
            logger.debug('Checking if trade is profitable..')
            if trade.calc_profit(rate=rate) <= 0:
                return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
        if sell and not buy and experimental.get('use_sell_signal', False):
            logger.debug('Sell signal received. Selling..')
            return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)

        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 in percent
        """

        trailing_stop = self.config.get('trailing_stop', False)
        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 trailing_stop:
            # trailing stoploss handling
            sl_offset = self.config.get('trailing_stop_positive_offset') or 0.0
            tsl_only_offset = self.config.get('trailing_only_offset_is_reached', False)

            # Make sure current_profit is calculated using high for backtesting.
            high_profit = current_profit if not high else trade.calc_profit_percent(high)

            # Don't update stoploss if trailing_only_offset_is_reached is true.
            if not (tsl_only_offset and high_profit < sl_offset):
                # Specific handling for trailing_stop_positive
                if 'trailing_stop_positive' in self.config and high_profit > sl_offset:
                    # Ignore mypy error check in configuration that this is a float
                    stop_loss_value = self.config.get('trailing_stop_positive')  # type: ignore
                    logger.debug(f"using positive stop loss: {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
        if ((self.stoploss is not None) and
            (trade.stop_loss >= current_rate) and
                (not self.order_types.get('stoploss_on_exchange'))):

            selltype = 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:
                selltype = SellType.TRAILING_STOP_LOSS
                logger.debug(
                    f"HIT STOP: current price at {current_rate:.6f}, "
                    f"stop loss is {trade.stop_loss:.6f}, "
                    f"initial stop loss was at {trade.initial_stop_loss:.6f}, "
                    f"trade opened at {trade.open_rate:.6f}")
                logger.debug(f"trailing stop saved {trade.stop_loss - trade.initial_stop_loss:.6f}")

            logger.debug('Stop loss hit.')
            return SellCheckTuple(sell_flag=True, sell_type=selltype)

        return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)

    def min_roi_reached_entry(self, trade_dur: 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
        roi_entry = max(roi_list)
        return self.minimal_roi[roi_entry]

    def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
        """
        Based on trade duration, current price and ROI configuration, decides whether bot should
        sell. Requires current_profit to be in percent!!
        :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 tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
        """
        Creates a dataframe and populates indicators for given ticker data
        """
        return {pair: self.advise_indicators(pair_data, {'pair': pair})
                for pair, pair_data in tickerdata.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: Raw data from the exchange and parsed by parse_ticker_dataframe()
        :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)