stable/freqtrade/strategy/interface.py

1007 lines
46 KiB
Python

"""
IStrategy interface
This module defines the interface to apply for strategies
"""
import logging
import warnings
from abc import ABC, abstractmethod
from datetime import datetime, timedelta, timezone
from typing import Dict, List, Optional, Tuple, Union
import arrow
from pandas import DataFrame
from freqtrade.constants import ListPairsWithTimeframes
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import SellType, SignalDirection, SignalTagType, SignalType
from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
from freqtrade.exchange.exchange import timeframe_to_next_date
from freqtrade.persistence import PairLocks, Trade
from freqtrade.strategy.hyper import HyperStrategyMixin
from freqtrade.strategy.informative_decorator import (InformativeData, PopulateIndicators,
_create_and_merge_informative_pair,
_format_pair_name)
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from freqtrade.wallets import Wallets
logger = logging.getLogger(__name__)
CUSTOM_SELL_MAX_LENGTH = 64
class SellCheckTuple(object):
"""
NamedTuple for Sell type + reason
"""
sell_type: SellType
sell_reason: str = ''
def __init__(self, sell_type: SellType, sell_reason: str = ''):
self.sell_type = sell_type
self.sell_reason = sell_reason or sell_type.value
@property
def sell_flag(self):
return self.sell_type != SellType.NONE
class IStrategy(ABC, HyperStrategyMixin):
"""
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
timeframe -> str: value of the timeframe (ticker interval) to use with the strategy
"""
# Strategy interface version
# Default to version 2
# Version 1 is the initial interface without metadata dict
# Version 2 populate_* include metadata dict
INTERFACE_VERSION: int = 2
_populate_fun_len: int = 0
_buy_fun_len: int = 0
_sell_fun_len: int = 0
_ft_params_from_file: Dict = {}
# associated minimal roi
minimal_roi: Dict
# associated stoploss
stoploss: float
# trailing stoploss
trailing_stop: bool = False
trailing_stop_positive: Optional[float] = None
trailing_stop_positive_offset: float = 0.0
trailing_only_offset_is_reached = False
use_custom_stoploss: bool = False
# associated timeframe
ticker_interval: str # DEPRECATED
timeframe: 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
use_sell_signal: bool
sell_profit_only: bool
sell_profit_offset: float
ignore_roi_if_buy_signal: bool
# Number of seconds after which the candle will no longer result in a buy on expired candles
ignore_buying_expired_candle_after: int = 0
# Disable checking the dataframe (converts the error into a warning message)
disable_dataframe_checks: bool = False
# Count of candles the strategy requires before producing valid signals
startup_candle_count: int = 0
# Protections
protections: List = []
# Class level variables (intentional) containing
# the dataprovider (dp) (access to other candles, historic data, ...)
# and wallets - access to the current balance.
dp: Optional[DataProvider]
wallets: Optional[Wallets] = None
# Filled from configuration
stake_currency: str
# container variable for strategy source code
__source__: str = ''
# Definition of plot_config. See plotting documentation for more details.
plot_config: Dict = {}
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] = {}
super().__init__(config)
# Gather informative pairs from @informative-decorated methods.
self._ft_informative: List[Tuple[InformativeData, PopulateIndicators]] = []
for attr_name in dir(self.__class__):
cls_method = getattr(self.__class__, attr_name)
if not callable(cls_method):
continue
informative_data_list = getattr(cls_method, '_ft_informative', None)
if not isinstance(informative_data_list, list):
# Type check is required because mocker would return a mock object that evaluates to
# True, confusing this code.
continue
strategy_timeframe_minutes = timeframe_to_minutes(self.timeframe)
for informative_data in informative_data_list:
if timeframe_to_minutes(informative_data.timeframe) < strategy_timeframe_minutes:
raise OperationalException('Informative timeframe must be equal or higher than '
'strategy timeframe!')
self._ft_informative.append((informative_data, cls_method))
@abstractmethod
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy, Sell, Short, Exit_short strategy
: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
"""
return dataframe
@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
"""
return dataframe
@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
"""
return dataframe
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
"""
Check buy timeout function callback.
This method can be used to override the enter-timeout.
It is called whenever a limit entry 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 entry order is cancelled.
"""
return False
def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
"""
Check sell timeout function callback.
This method can be used to override the exit-timeout.
It is called whenever a (long) limit sell order or (short) limit buy
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 (long)sell/(short)buy-order is cancelled.
"""
return False
def bot_loop_start(self, **kwargs) -> None:
"""
Called at the start of the bot iteration (one loop).
Might be used to perform pair-independent tasks
(e.g. gather some remote resource for comparison)
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
"""
pass
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, current_time: datetime,
side: str, **kwargs) -> bool:
"""
Called right before placing a entry order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be bought/shorted.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param current_time: datetime object, containing the current datetime
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process
"""
return True
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str,
current_time: datetime, **kwargs) -> bool:
"""
Called right before placing a regular exit order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair for trade that's about to be exited.
:param trade: trade object.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param sell_reason: Exit reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param current_time: datetime object, containing the current datetime
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True, then the sell-order/exit_short-order is placed on the exchange.
False aborts the process
"""
return True
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> float:
"""
Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
e.g. returning -0.05 would create a stoploss 5% below current_rate.
The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns the initial stoploss value
Only called when use_custom_stoploss is set to True.
:param pair: Pair that's currently analyzed
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New stoploss value, relative to the current_rate
"""
return self.stoploss
def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
**kwargs) -> float:
"""
Custom entry price logic, returning the new entry price.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns None, orderbook is used to set entry price
:param pair: Pair that's currently analyzed
:param current_time: datetime object, containing the current datetime
:param proposed_rate: Rate, calculated based on pricing settings in ask_strategy.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New entry price value if provided
"""
return proposed_rate
def custom_exit_price(self, pair: str, trade: Trade,
current_time: datetime, proposed_rate: float,
current_profit: float, **kwargs) -> float:
"""
Custom exit price logic, returning the new exit price.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns None, orderbook is used to set exit price
:param pair: Pair that's currently analyzed
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param proposed_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return float: New exit price value if provided
"""
return proposed_rate
def custom_sell(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
"""
Custom exit signal logic indicating that specified position should be sold. Returning a
string or True from this method is equal to setting exit signal on a candle at specified
time. This method is not called when exit signal is set.
This method should be overridden to create exit signals that depend on trade parameters. For
example you could implement an exit relative to the candle when the trade was opened,
or a custom 1:2 risk-reward ROI.
Custom exit reason max length is 64. Exceeding characters will be removed.
:param pair: Pair that's currently analyzed
:param trade: trade object.
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param current_profit: Current profit (as ratio), calculated based on current_rate.
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return: To execute exit, return a string with custom sell reason or True. Otherwise return
None or False.
"""
return None
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
side: str, **kwargs) -> float:
"""
Customize stake size for each new trade.
:param pair: Pair that's currently analyzed
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param proposed_stake: A stake amount proposed by the bot.
:param min_stake: Minimal stake size allowed by exchange.
:param max_stake: Balance available for trading.
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:return: A stake size, which is between min_stake and max_stake.
"""
return proposed_stake
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, side: str,
**kwargs) -> float:
"""
Customize leverage for each new trade. This method is not called when edge module is
enabled.
:param pair: Pair that's currently analyzed
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
:param proposed_leverage: A leverage proposed by the bot.
:param max_leverage: Max leverage allowed on this pair
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:return: A leverage amount, which is between 1.0 and max_leverage.
"""
return 1.0
def informative_pairs(self) -> ListPairsWithTimeframes:
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
###
# END - Intended to be overridden by strategy
###
def gather_informative_pairs(self) -> ListPairsWithTimeframes:
"""
Internal method which gathers all informative pairs (user or automatically defined).
"""
informative_pairs = self.informative_pairs()
for inf_data, _ in self._ft_informative:
if inf_data.asset:
pair_tf = (_format_pair_name(self.config, inf_data.asset), inf_data.timeframe)
informative_pairs.append(pair_tf)
else:
if not self.dp:
raise OperationalException('@informative decorator with unspecified asset '
'requires DataProvider instance.')
for pair in self.dp.current_whitelist():
informative_pairs.append((pair, inf_data.timeframe))
return list(set(informative_pairs))
def get_strategy_name(self) -> str:
"""
Returns strategy class name
"""
return self.__class__.__name__
def lock_pair(self, pair: str, until: datetime, reason: str = None) -> None:
"""
Locks pair until a given timestamp happens.
Locked pairs are not analyzed, and are prevented from opening new trades.
Locks can only count up (allowing users to lock pairs for a longer period of time).
To remove a lock from a pair, use `unlock_pair()`
:param pair: Pair to lock
:param until: datetime in UTC until the pair should be blocked from opening new trades.
Needs to be timezone aware `datetime.now(timezone.utc)`
:param reason: Optional string explaining why the pair was locked.
"""
PairLocks.lock_pair(pair, until, reason)
def unlock_pair(self, pair: str) -> None:
"""
Unlocks a pair previously locked using lock_pair.
Not used by freqtrade itself, but intended to be used if users lock pairs
manually from within the strategy, to allow an easy way to unlock pairs.
:param pair: Unlock pair to allow trading again
"""
PairLocks.unlock_pair(pair, datetime.now(timezone.utc))
def is_pair_locked(self, pair: str, candle_date: datetime = None) -> bool:
"""
Checks if a pair is currently locked
The 2nd, optional parameter ensures that locks are applied until the new candle arrives,
and not stop at 14:00:00 - while the next candle arrives at 14:00:02 leaving a gap
of 2 seconds for an entry order to happen on an old signal.
:param pair: "Pair to check"
:param candle_date: Date of the last candle. Optional, defaults to current date
:returns: locking state of the pair in question.
"""
if not candle_date:
# Simple call ...
return PairLocks.is_pair_locked(pair)
else:
lock_time = timeframe_to_next_date(self.timeframe, candle_date)
return PairLocks.is_pair_locked(pair, lock_time)
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and entry order signal to it
:param dataframe: Dataframe containing data from exchange
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
logger.debug("TA Analysis Launched")
dataframe = self.advise_indicators(dataframe, metadata)
dataframe = self.advise_entry(dataframe, metadata)
dataframe = self.advise_exit(dataframe, metadata)
return dataframe
def _analyze_ticker_internal(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Parses the given candle (OHLCV) data and returns a populated DataFrame
add several TA indicators and buy signal to it
WARNING: Used internally only, may skip analysis if `process_only_new_candles` is set.
:param dataframe: Dataframe containing data from exchange
:param metadata: Metadata dictionary with additional data (e.g. 'pair')
:return: DataFrame of candle (OHLCV) data with indicator data and signals added
"""
pair = str(metadata.get('pair'))
# 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']
if self.dp:
self.dp._set_cached_df(pair, self.timeframe, dataframe)
else:
logger.debug("Skipping TA Analysis for already analyzed candle")
dataframe[SignalType.ENTER_LONG.value] = 0
dataframe[SignalType.EXIT_LONG.value] = 0
dataframe[SignalType.ENTER_SHORT.value] = 0
dataframe[SignalType.EXIT_SHORT.value] = 0
dataframe[SignalTagType.ENTER_TAG.value] = None
# 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 analyze_pair(self, pair: str) -> None:
"""
Fetch data for this pair from dataprovider and analyze.
Stores the dataframe into the dataprovider.
The analyzed dataframe is then accessible via `dp.get_analyzed_dataframe()`.
:param pair: Pair to analyze.
"""
if not self.dp:
raise OperationalException("DataProvider not found.")
dataframe = self.dp.ohlcv(pair, self.timeframe)
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning('Empty candle (OHLCV) data for pair %s', pair)
return
try:
df_len, df_close, df_date = self.preserve_df(dataframe)
dataframe = strategy_safe_wrapper(
self._analyze_ticker_internal, message=""
)(dataframe, {'pair': pair})
self.assert_df(dataframe, df_len, df_close, df_date)
except StrategyError as error:
logger.warning(f"Unable to analyze candle (OHLCV) data for pair {pair}: {error}")
return
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return
def analyze(self, pairs: List[str]) -> None:
"""
Analyze all pairs using analyze_pair().
:param pairs: List of pairs to analyze
"""
for pair in pairs:
self.analyze_pair(pair)
@staticmethod
def preserve_df(dataframe: DataFrame) -> Tuple[int, float, datetime]:
""" keep some data for dataframes """
return len(dataframe), dataframe["close"].iloc[-1], dataframe["date"].iloc[-1]
def assert_df(self, dataframe: DataFrame, df_len: int, df_close: float, df_date: datetime):
"""
Ensure dataframe (length, last candle) was not modified, and has all elements we need.
"""
message_template = "Dataframe returned from strategy has mismatching {}."
message = ""
if dataframe is None:
message = "No dataframe returned (return statement missing?)."
elif 'enter_long' not in dataframe:
message = "enter_long/buy column not set."
elif df_len != len(dataframe):
message = message_template.format("length")
elif df_close != dataframe["close"].iloc[-1]:
message = message_template.format("last close price")
elif df_date != dataframe["date"].iloc[-1]:
message = message_template.format("last date")
if message:
if self.disable_dataframe_checks:
logger.warning(message)
else:
raise StrategyError(message)
def get_latest_candle(
self,
pair: str,
timeframe: str,
dataframe: DataFrame,
) -> Tuple[Optional[DataFrame], Optional[arrow.Arrow]]:
"""
Calculates current signal based based on the entry order or exit order
columns of the dataframe.
Used by Bot to get the signal to buy, sell, short, or exit_short
:param pair: pair in format ANT/BTC
:param timeframe: timeframe to use
:param dataframe: Analyzed dataframe to get signal from.
:return: (None, None) or (Dataframe, latest_date) - corresponding to the last candle
"""
if not isinstance(dataframe, DataFrame) or dataframe.empty:
logger.warning(f'Empty candle (OHLCV) data for pair {pair}')
return None, None
latest_date = dataframe['date'].max()
latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
# Explicitly convert to arrow object to ensure the below comparison does not fail
latest_date = arrow.get(latest_date)
# Check if dataframe is out of date
timeframe_minutes = timeframe_to_minutes(timeframe)
offset = self.config.get('exchange', {}).get('outdated_offset', 5)
if latest_date < (arrow.utcnow().shift(minutes=-(timeframe_minutes * 2 + offset))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair, int((arrow.utcnow() - latest_date).total_seconds() // 60)
)
return None, None
return latest, latest_date
def get_exit_signal(
self,
pair: str,
timeframe: str,
dataframe: DataFrame,
is_short: bool = None
) -> Tuple[bool, bool]:
"""
Calculates current exit signal based based on the buy/short or sell/exit_short
columns of the dataframe.
Used by Bot to get the signal to exit.
depending on is_short, looks at "short" or "long" columns.
:param pair: pair in format ANT/BTC
:param timeframe: timeframe to use
:param dataframe: Analyzed dataframe to get signal from.
:param is_short: Indicating existing trade direction.
:return: (enter, exit) A bool-tuple with enter / exit values.
"""
latest, latest_date = self.get_latest_candle(pair, timeframe, dataframe)
if latest is None:
return False, False
if is_short:
enter = latest.get(SignalType.ENTER_SHORT.value, 0) == 1
exit_ = latest.get(SignalType.EXIT_SHORT.value, 0) == 1
else:
enter = latest[SignalType.ENTER_LONG.value] == 1
exit_ = latest.get(SignalType.EXIT_LONG.value, 0) == 1
logger.debug(f"exit-trigger: {latest['date']} (pair={pair}) "
f"enter={enter} exit={exit_}")
return enter, exit_
def get_entry_signal(
self,
pair: str,
timeframe: str,
dataframe: DataFrame,
) -> Tuple[Optional[SignalDirection], Optional[str]]:
"""
Calculates current entry signal based based on the buy/short or sell/exit_short
columns of the dataframe.
Used by Bot to get the signal to buy, sell, short, or exit_short
:param pair: pair in format ANT/BTC
:param timeframe: timeframe to use
:param dataframe: Analyzed dataframe to get signal from.
:return: (SignalDirection, entry_tag)
"""
latest, latest_date = self.get_latest_candle(pair, timeframe, dataframe)
if latest is None or latest_date is None:
return None, None
enter_long = latest[SignalType.ENTER_LONG.value] == 1
exit_long = latest.get(SignalType.EXIT_LONG.value, 0) == 1
enter_short = latest.get(SignalType.ENTER_SHORT.value, 0) == 1
exit_short = latest.get(SignalType.EXIT_SHORT.value, 0) == 1
enter_signal: Optional[SignalDirection] = None
enter_tag_value: Optional[str] = None
if enter_long == 1 and not any([exit_long, enter_short]):
enter_signal = SignalDirection.LONG
enter_tag_value = latest.get(SignalTagType.ENTER_TAG.value, None)
if enter_short == 1 and not any([exit_short, enter_long]):
enter_signal = SignalDirection.SHORT
enter_tag_value = latest.get(SignalTagType.ENTER_TAG.value, None)
timeframe_seconds = timeframe_to_seconds(timeframe)
if self.ignore_expired_candle(
latest_date=latest_date.datetime,
current_time=datetime.now(timezone.utc),
timeframe_seconds=timeframe_seconds,
enter=bool(enter_signal)
):
return None, enter_tag_value
logger.debug(f"entry trigger: {latest['date']} (pair={pair}) "
f"enter={enter_long} enter_tag_value={enter_tag_value}")
return enter_signal, enter_tag_value
def ignore_expired_candle(
self,
latest_date: datetime,
current_time: datetime,
timeframe_seconds: int,
enter: bool
):
if self.ignore_buying_expired_candle_after and enter:
time_delta = current_time - (latest_date + timedelta(seconds=timeframe_seconds))
return time_delta.total_seconds() > self.ignore_buying_expired_candle_after
else:
return False
def should_exit(self, trade: Trade, rate: float, date: datetime, *,
enter: bool, exit_: bool,
low: float = None, high: float = None,
force_stoploss: float = 0) -> SellCheckTuple:
"""
This function evaluates if one of the conditions required to trigger an exit order
has been reached, which can either be a stop-loss, ROI or exit-signal.
:param low: Only used during backtesting to simulate (long)stoploss/(short)ROI
:param high: Only used during backtesting, to simulate (short)stoploss/(long)ROI
:param force_stoploss: Externally provided stoploss
:return: True if trade should be exited, False otherwise
"""
current_rate = rate
current_profit = trade.calc_profit_ratio(current_rate)
trade.adjust_min_max_rates(high or current_rate, low or current_rate)
stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
current_time=date, current_profit=current_profit,
force_stoploss=force_stoploss, low=low, high=high)
# Set current rate to high for backtesting sell
current_rate = high or rate
current_profit = trade.calc_profit_ratio(current_rate)
# if enter signal and ignore_roi is set, we don't need to evaluate min_roi.
roi_reached = (not (enter and self.ignore_roi_if_buy_signal)
and self.min_roi_reached(trade=trade, current_profit=current_profit,
current_time=date))
sell_signal = SellType.NONE
custom_reason = ''
# use provided rate in backtesting, not high/low.
current_rate = rate
current_profit = trade.calc_profit_ratio(current_rate)
if (self.sell_profit_only and current_profit <= self.sell_profit_offset):
# sell_profit_only and profit doesn't reach the offset - ignore sell signal
pass
elif self.use_sell_signal and not enter:
if exit_:
sell_signal = SellType.SELL_SIGNAL
else:
trade_type = "exit_short" if trade.is_short else "sell"
custom_reason = strategy_safe_wrapper(self.custom_sell, default_retval=False)(
pair=trade.pair, trade=trade, current_time=date, current_rate=current_rate,
current_profit=current_profit)
if custom_reason:
sell_signal = SellType.CUSTOM_SELL
if isinstance(custom_reason, str):
if len(custom_reason) > CUSTOM_SELL_MAX_LENGTH:
logger.warning(f'Custom {trade_type} reason returned from '
f'custom_{trade_type} is too long and was trimmed'
f'to {CUSTOM_SELL_MAX_LENGTH} characters.')
custom_reason = custom_reason[:CUSTOM_SELL_MAX_LENGTH]
else:
custom_reason = None
# TODO: return here if exit-signal should be favored over ROI
# Start evaluations
# Sequence:
# ROI (if not stoploss)
# Exit-signal
# Stoploss
if roi_reached and stoplossflag.sell_type != SellType.STOP_LOSS:
logger.debug(f"{trade.pair} - Required profit reached. sell_type=SellType.ROI")
return SellCheckTuple(sell_type=SellType.ROI)
if sell_signal != SellType.NONE:
logger.debug(f"{trade.pair} - Sell signal received. "
f"sell_type=SellType.{sell_signal.name}" +
(f", custom_reason={custom_reason}" if custom_reason else ""))
return SellCheckTuple(sell_type=sell_signal, sell_reason=custom_reason)
if stoplossflag.sell_flag:
logger.debug(f"{trade.pair} - Stoploss hit. sell_type={stoplossflag.sell_type}")
return stoplossflag
# This one is noisy, commented out...
# logger.debug(f"{trade.pair} - No exit signal.")
return SellCheckTuple(sell_type=SellType.NONE)
def stop_loss_reached(self, current_rate: float, trade: Trade,
current_time: datetime, current_profit: float,
force_stoploss: float, low: float = None,
high: float = None) -> SellCheckTuple:
"""
Based on current profit of the trade and configured (trailing) stoploss,
decides to exit or not
:param current_profit: current profit as ratio
:param low: Low value of this candle, only set in backtesting
:param high: High value of this candle, only set in backtesting
"""
stop_loss_value = force_stoploss if force_stoploss else self.stoploss
# Initiate stoploss with open_rate. Does nothing if stoploss is already set.
trade.adjust_stop_loss(trade.open_rate, stop_loss_value, initial=True)
dir_correct = (trade.stop_loss < (low or current_rate)
if not trade.is_short else
trade.stop_loss > (high or current_rate)
)
if self.use_custom_stoploss and dir_correct:
stop_loss_value = strategy_safe_wrapper(self.custom_stoploss, default_retval=None
)(pair=trade.pair, trade=trade,
current_time=current_time,
current_rate=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 and (
(trade.stop_loss < (low or current_rate) and not trade.is_short) or
(trade.stop_loss > (high or current_rate) and trade.is_short)
):
# trailing stoploss handling
sl_offset = self.trailing_stop_positive_offset
# Make sure current_profit is calculated using high for backtesting.
bound = low if trade.is_short else high
bound_profit = current_profit if not bound else trade.calc_profit_ratio(bound)
# Don't update stoploss if trailing_only_offset_is_reached is true.
if not (self.trailing_only_offset_is_reached and (
(bound_profit < sl_offset and not trade.is_short) or
(bound_profit > sl_offset and trade.is_short)
)):
# Specific handling for trailing_stop_positive
if self.trailing_stop_positive is not None and (
(bound_profit > sl_offset and not trade.is_short) or
(bound_profit < sl_offset and trade.is_short)
):
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(bound 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 >= (low or current_rate) and not trade.is_short) or
((trade.stop_loss <= (high or current_rate) and trade.is_short))
) 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 "
f"{((high if trade.is_short else low) or current_rate):.6f}, "
f"stoploss is {trade.stop_loss:.6f}, "
f"initial stoploss was at {trade.initial_stop_loss:.6f}, "
f"trade opened at {trade.open_rate:.6f}")
new_stoploss = (
trade.stop_loss + trade.initial_stop_loss
if trade.is_short else
trade.stop_loss - trade.initial_stop_loss
)
logger.debug(f"{trade.pair} - Trailing stop saved "
f"{new_stoploss:.6f}")
return SellCheckTuple(sell_type=sell_type)
return SellCheckTuple(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_utc.timestamp()) // 60)
_, roi = self.min_roi_reached_entry(trade_dur)
if roi is None:
return False
else:
return current_profit > roi
def advise_all_indicators(self, data: Dict[str, DataFrame]) -> Dict[str, DataFrame]:
"""
Populates indicators for given candle (OHLCV) data (for multiple pairs)
Does not run advise_entry or advise_exit!
Used by optimize operations only, not during dry / live runs.
Using .copy() to get a fresh copy of the dataframe for every strategy run.
Also copy on output to avoid PerformanceWarnings pandas 1.3.0 started to show.
Has positive effects on memory usage for whatever reason - also when
using only one strategy.
"""
return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy()
for pair, pair_data in data.items()}
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Populate indicators that will be used in the Buy, Sell, short, exit_short strategy
This method should not be overridden.
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
logger.debug(f"Populating indicators for pair {metadata.get('pair')}.")
# call populate_indicators_Nm() which were tagged with @informative decorator.
for inf_data, populate_fn in self._ft_informative:
dataframe = _create_and_merge_informative_pair(
self, dataframe, metadata, inf_data, populate_fn)
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_entry(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the entry order signal for the given dataframe
This method should not be overridden.
:param dataframe: DataFrame
:param metadata: Additional information dictionary, with details like the
currently traded pair
:return: DataFrame with buy column
"""
logger.debug(f"Populating enter signals for pair {metadata.get('pair')}.")
if self._buy_fun_len == 2:
warnings.warn("deprecated - check out the Sample strategy to see "
"the current function headers!", DeprecationWarning)
df = self.populate_buy_trend(dataframe) # type: ignore
else:
df = self.populate_buy_trend(dataframe, metadata)
if 'enter_long' not in df.columns:
df = df.rename({'buy': 'enter_long', 'buy_tag': 'enter_tag'}, axis='columns')
return df
def advise_exit(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the exit order signal for the given dataframe
This method should not be overridden.
:param dataframe: DataFrame
:param metadata: Additional information dictionary, with details like the
currently traded pair
:return: DataFrame with sell column
"""
logger.debug(f"Populating exit 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)
df = self.populate_sell_trend(dataframe) # type: ignore
else:
df = self.populate_sell_trend(dataframe, metadata)
if 'exit_long' not in df.columns:
df = df.rename({'sell': 'exit_long'}, axis='columns')
return df