701 lines
31 KiB
Python
701 lines
31 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, timezone
|
|
from enum import Enum
|
|
from typing import Dict, List, NamedTuple, Optional, Tuple
|
|
|
|
import arrow
|
|
from pandas import DataFrame
|
|
|
|
from freqtrade.constants import ListPairsWithTimeframes
|
|
from freqtrade.data.dataprovider import DataProvider
|
|
from freqtrade.exceptions import OperationalException, StrategyError
|
|
from freqtrade.exchange import timeframe_to_minutes
|
|
from freqtrade.exchange.exchange import timeframe_to_next_date
|
|
from freqtrade.persistence import PairLocks, Trade
|
|
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
|
|
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"
|
|
EMERGENCY_SELL = "emergency_sell"
|
|
NONE = ""
|
|
|
|
def __str__(self):
|
|
# explicitly convert to String to help with exporting data.
|
|
return self.value
|
|
|
|
|
|
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
|
|
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
|
|
# 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
|
|
|
|
# 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] = None
|
|
wallets: Optional[Wallets] = None
|
|
# 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] = {}
|
|
|
|
@abstractmethod
|
|
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
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
|
|
: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 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 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:
|
|
"""
|
|
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 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, **kwargs) -> bool:
|
|
"""
|
|
Called right before placing a buy 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.
|
|
: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 **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, **kwargs) -> bool:
|
|
"""
|
|
Called right before placing a regular sell 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 sold.
|
|
: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: Sell reason.
|
|
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
|
|
'sell_signal', 'force_sell', 'emergency_sell']
|
|
: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 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 currentrate
|
|
"""
|
|
return self.stoploss
|
|
|
|
def informative_pairs(self) -> ListPairsWithTimeframes:
|
|
"""
|
|
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 []
|
|
|
|
###
|
|
# END - Intended to be overridden by strategy
|
|
###
|
|
|
|
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 a buy 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 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'))
|
|
|
|
# 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['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 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 = ""
|
|
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:
|
|
if self.disable_dataframe_checks:
|
|
logger.warning(f"Dataframe returned from strategy has mismatching {message}.")
|
|
else:
|
|
raise StrategyError(f"Dataframe returned from strategy has mismatching {message}.")
|
|
|
|
def get_signal(self, pair: str, timeframe: str, dataframe: DataFrame) -> Tuple[bool, bool]:
|
|
"""
|
|
Calculates current signal based based on the buy / sell columns of the dataframe.
|
|
Used by Bot to get the signal to buy or sell
|
|
:param pair: pair in format ANT/BTC
|
|
:param timeframe: timeframe to use
|
|
:param dataframe: Analyzed dataframe to get signal from.
|
|
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
|
|
"""
|
|
if not isinstance(dataframe, DataFrame) or dataframe.empty:
|
|
logger.warning(f'Empty candle (OHLCV) data for pair {pair}')
|
|
return False, False
|
|
|
|
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 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 evaluates if one of the conditions required to trigger a sell
|
|
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)
|
|
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 ask_strategy.get('ignore_roi_if_buy_signal', False))
|
|
and self.min_roi_reached(trade=trade, current_profit=current_profit,
|
|
current_time=date))
|
|
|
|
if (ask_strategy.get('sell_profit_only', False)
|
|
and trade.calc_profit(rate=rate) <= ask_strategy.get('sell_profit_offset', 0)):
|
|
# Negative profits and sell_profit_only - ignore sell signal
|
|
sell_signal = False
|
|
else:
|
|
sell_signal = sell and not buy and 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.use_custom_stoploss:
|
|
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:
|
|
# 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)
|