stable/freqtrade/strategy/interface.py

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"""
IStrategy interface
This module defines the interface to apply for strategies
"""
import logging
import warnings
from abc import ABC, abstractmethod
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from datetime import datetime, timezone
from enum import Enum
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from typing import Dict, NamedTuple, Optional, Tuple
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import arrow
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from pandas import DataFrame
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from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import StrategyError
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.persistence import Trade
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
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from freqtrade.constants import ListPairsWithTimeframes
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from freqtrade.wallets import Wallets
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logger = logging.getLogger(__name__)
class SignalType(Enum):
"""
Enum to distinguish between buy and sell signals
"""
BUY = "buy"
SELL = "sell"
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class SellType(Enum):
"""
Enum to distinguish between sell reasons
"""
ROI = "roi"
STOP_LOSS = "stop_loss"
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STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"
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TRAILING_STOP_LOSS = "trailing_stop_loss"
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):
"""
NamedTuple for Sell type + reason
"""
sell_flag: bool
sell_type: SellType
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class IStrategy(ABC):
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"""
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 timeframe (ticker interval) to use with the strategy
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"""
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# Strategy interface version
# Default to version 2
# Version 1 is the initial interface without metadata dict
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# Version 2 populate_* include metadata dict
INTERFACE_VERSION: int = 2
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_populate_fun_len: int = 0
_buy_fun_len: int = 0
_sell_fun_len: int = 0
# associated minimal roi
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minimal_roi: Dict
# associated stoploss
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stoploss: float
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# trailing stoploss
trailing_stop: bool = False
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
order_types: Dict = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False,
'stoploss_on_exchange_interval': 60,
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}
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# Optional time in force
order_time_in_force: Dict = {
'buy': 'gtc',
'sell': 'gtc',
}
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# run "populate_indicators" only for new candle
process_only_new_candles: bool = False
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# Count of candles the strategy requires before producing valid signals
startup_candle_count: int = 0
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# Class level variables (intentional) containing
# the dataprovider (dp) (access to other candles, historic data, ...)
# and wallets - access to the current balance.
dp: Optional[DataProvider] = None
wallets: Optional[Wallets] = None
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# Definition of plot_config. See plotting documentation for more details.
plot_config: Dict = {}
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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] = {}
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self._pair_locked_until: Dict[str, datetime] = {}
@abstractmethod
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
Populate indicators that will be used in the Buy and Sell strategy
:param dataframe: DataFrame with data from the exchange
:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
"""
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
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:return: DataFrame with buy column
"""
@abstractmethod
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
: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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"""
Check buy timeout function callback.
This method can be used to override the buy-timeout.
It is called whenever a limit buy order has been created,
and is not yet fully filled.
Configuration options in `unfilledtimeout` will be verified before this,
so ensure to set these timeouts high enough.
When not implemented by a strategy, this simply returns False.
:param pair: Pair the trade is for
:param trade: trade object.
:param order: Order dictionary as returned from CCXT.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is cancelled.
"""
return False
def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
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"""
Check sell timeout function callback.
This method can be used to override the sell-timeout.
It is called whenever a limit sell order has been created,
and is not yet fully filled.
Configuration options in `unfilledtimeout` will be verified before this,
so ensure to set these timeouts high enough.
When not implemented by a strategy, this simply returns False.
:param pair: Pair the trade is for
:param trade: trade object.
:param order: Order dictionary as returned from CCXT.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is cancelled.
"""
return False
def informative_pairs(self) -> ListPairsWithTimeframes:
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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
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 []
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def get_strategy_name(self) -> str:
"""
Returns strategy class name
"""
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return self.__class__.__name__
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def lock_pair(self, pair: str, until: datetime) -> None:
"""
Locks pair until a given timestamp happens.
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).
To remove a lock from a pair, use `unlock_pair()`
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:param pair: Pair to lock
:param until: datetime in UTC until the pair should be blocked from opening new trades.
Needs to be timezone aware `datetime.now(timezone.utc)`
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"""
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if pair not in self._pair_locked_until or self._pair_locked_until[pair] < until:
self._pair_locked_until[pair] = until
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def unlock_pair(self, pair: str) -> None:
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"""
Unlocks a pair previously locked using lock_pair.
Not used by freqtrade itself, but intended to be used if users lock pairs
manually from within the strategy, to allow an easy way to unlock pairs.
:param pair: Unlock pair to allow trading again
"""
if pair in self._pair_locked_until:
del self._pair_locked_until[pair]
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def is_pair_locked(self, pair: str) -> bool:
"""
Checks if a pair is currently locked
"""
if pair not in self._pair_locked_until:
return False
return self._pair_locked_until[pair] >= datetime.now(timezone.utc)
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def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and buy signal to it
:param dataframe: Dataframe containing data from exchange
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
logger.debug("TA Analysis Launched")
dataframe = self.advise_indicators(dataframe, metadata)
dataframe = self.advise_buy(dataframe, metadata)
dataframe = self.advise_sell(dataframe, metadata)
return dataframe
def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and buy signal to it
WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
:param dataframe: Dataframe containing data from exchange
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
pair = str(metadata.get('pair'))
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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
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']):
# Defs that only make change on new candle data.
dataframe = self.analyze_ticker(dataframe, metadata)
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self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
else:
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logger.debug("Skipping TA Analysis for already analyzed candle")
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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)
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logger.debug("Loop Analysis Launched")
return dataframe
@staticmethod
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def preserve_df(dataframe: DataFrame) -> Tuple[int, float, datetime]:
""" keep some data for dataframes """
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return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
@staticmethod
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def assert_df(dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
""" make sure data is unmodified """
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message = ""
if df_len != len(dataframe):
message = "length"
elif df_close != dataframe["close"].iloc[-1]:
message = "last close price"
elif df_date != dataframe["date"].iloc[-1]:
message = "last date"
if message:
raise StrategyError(f"Dataframe returned from strategy has mismatching {message}.")
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)
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: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 candle (OHLCV) data for pair %s', pair)
return False, False
try:
df_len, df_close, df_date = self.preserve_df(dataframe)
dataframe = strategy_safe_wrapper(
self._analyze_ticker_internal, message=""
)(dataframe, {'pair': pair})
self.assert_df(dataframe, df_len, df_close, df_date)
except StrategyError as error:
logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
return False, False
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return False, False
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latest_date = dataframe['date'].max()
latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
# Explicitly convert to arrow object to ensure the below comparison does not fail
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latest_date = arrow.get(latest_date)
# Check if dataframe is out of date
interval_minutes = timeframe_to_minutes(interval)
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offset = self.config.get('exchange', {}).get('outdated_offset', 5)
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if latest_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + offset))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
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(arrow.utcnow() - latest_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
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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,
force_stoploss: float = 0) -> SellCheckTuple:
"""
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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
: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
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current_rate = low or rate
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,
current_time=date, current_profit=current_profit,
force_stoploss=force_stoploss, high=high)
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if stoplossflag.sell_flag:
logger.debug(f"{trade.pair} - Stoploss hit. sell_flag=True, "
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
current_profit = trade.calc_profit_ratio(current_rate)
config_ask_strategy = self.config.get('ask_strategy', {})
if buy and config_ask_strategy.get('ignore_roi_if_buy_signal', False):
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# This one is noisy, commented out
# 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)
# 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(f"{trade.pair} - Required profit reached. sell_flag=True, "
f"sell_type=SellType.ROI")
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return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
if config_ask_strategy.get('sell_profit_only', False):
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# This one is noisy, commented out
# logger.debug(f"{trade.pair} - Checking if trade is profitable...")
if trade.calc_profit(rate=rate) <= 0:
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# This one is noisy, commented out
# 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):
logger.debug(f"{trade.pair} - Sell signal received. sell_flag=True, "
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,
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
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:param current_profit: current profit as ratio
"""
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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.
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
if self.trailing_stop_positive is not None and high_profit > sl_offset:
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}%")
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
# in Dry-Run, this handles stoploss logic as well, as the logic will not be different to
# regular stoploss handling.
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if ((self.stoploss is not None) and
(trade.stop_loss >= current_rate) and
(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.
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}, "
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]]:
"""
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.
"""
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# 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:
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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]
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
"""
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Based on trade duration, current profit of the trade and ROI configuration,
decides whether bot should sell.
:param current_profit: current profit as ratio
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: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)
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_, 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]:
"""
Creates a dataframe and populates indicators for given candle (OHLCV) data
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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)