957 lines
44 KiB
Python
957 lines
44 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, 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.persistence.models import LocalTrade, Order
|
|
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:
|
|
"""
|
|
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
|
|
|
|
# Position adjustment is disabled by default
|
|
position_adjustment_enable: bool = False
|
|
max_entry_position_adjustment: int = -1
|
|
|
|
# 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 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
|
|
"""
|
|
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 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 check_buy_timeout(self, pair: str, trade: Trade, order: dict,
|
|
current_time: datetime, **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 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 is returned, then the buy-order is cancelled.
|
|
"""
|
|
return False
|
|
|
|
def check_sell_timeout(self, pair: str, trade: Trade, order: dict,
|
|
current_time: datetime, **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 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 is returned, then the sell-order is cancelled.
|
|
"""
|
|
return False
|
|
|
|
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
|
time_in_force: str, current_time: datetime, entry_tag: Optional[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 current_time: datetime object, containing the current datetime
|
|
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
|
|
: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 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 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 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 current_rate
|
|
"""
|
|
return self.stoploss
|
|
|
|
def custom_entry_price(self, pair: str, current_time: datetime, proposed_rate: float,
|
|
entry_tag: Optional[str], **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 entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
|
|
: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 sell signal logic indicating that specified position should be sold. Returning a
|
|
string or True from this method is equal to setting sell signal on a candle at specified
|
|
time. This method is not called when sell signal is set.
|
|
|
|
This method should be overridden to create sell signals that depend on trade parameters. For
|
|
example you could implement a sell relative to the candle when the trade was opened,
|
|
or a custom 1:2 risk-reward ROI.
|
|
|
|
Custom sell 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 sell, 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,
|
|
entry_tag: Optional[str], **kwargs) -> float:
|
|
"""
|
|
Customize stake size 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_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 entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
|
|
:return: A stake size, which is between min_stake and max_stake.
|
|
"""
|
|
return proposed_stake
|
|
|
|
def adjust_trade_position(self, trade: Trade, current_time: datetime,
|
|
current_rate: float, current_profit: float, min_stake: float,
|
|
max_stake: float, **kwargs) -> Optional[float]:
|
|
"""
|
|
Custom trade adjustment logic, returning the stake amount that a trade should be increased.
|
|
This means extra buy orders with additional fees.
|
|
Only called when `position_adjustment_enable` is set to True.
|
|
|
|
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
|
|
|
|
When not implemented by a strategy, returns None
|
|
|
|
:param trade: trade object.
|
|
:param current_time: datetime object, containing the current datetime
|
|
:param current_rate: Current buy rate.
|
|
:param current_profit: Current profit (as ratio), calculated based on current_rate.
|
|
:param min_stake: Minimal stake size allowed by exchange.
|
|
:param max_stake: Balance available for trading.
|
|
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
|
|
:return float: Stake amount to adjust your trade
|
|
"""
|
|
return None
|
|
|
|
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 []
|
|
|
|
def version(self) -> Optional[str]:
|
|
"""
|
|
Returns version of the strategy.
|
|
"""
|
|
return None
|
|
|
|
###
|
|
# 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 unlock_reason(self, reason: str) -> None:
|
|
"""
|
|
Unlocks all pairs previously locked using lock_pair with specified reason.
|
|
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 reason: Unlock pairs to allow trading again
|
|
"""
|
|
PairLocks.unlock_reason(reason, 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
|
|
dataframe['buy_tag'] = None
|
|
dataframe['exit_tag'] = 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 'buy' not in dataframe:
|
|
message = "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_signal(
|
|
self,
|
|
pair: str,
|
|
timeframe: str,
|
|
dataframe: DataFrame
|
|
) -> Tuple[bool, bool, Optional[str], Optional[str]]:
|
|
"""
|
|
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, 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 False, False, None, None
|
|
|
|
buy = latest[SignalType.BUY.value] == 1
|
|
|
|
sell = False
|
|
if SignalType.SELL.value in latest:
|
|
sell = latest[SignalType.SELL.value] == 1
|
|
|
|
buy_tag = latest.get(SignalTagType.BUY_TAG.value, None)
|
|
exit_tag = latest.get(SignalTagType.EXIT_TAG.value, None)
|
|
# Tags can be None, which does not resolve to False.
|
|
buy_tag = buy_tag if isinstance(buy_tag, str) else None
|
|
exit_tag = exit_tag if isinstance(exit_tag, str) else None
|
|
|
|
logger.debug('trigger: %s (pair=%s) buy=%s sell=%s',
|
|
latest['date'], pair, str(buy), str(sell))
|
|
timeframe_seconds = timeframe_to_seconds(timeframe)
|
|
if self.ignore_expired_candle(latest_date=latest_date,
|
|
current_time=datetime.now(timezone.utc),
|
|
timeframe_seconds=timeframe_seconds,
|
|
buy=buy):
|
|
return False, sell, buy_tag, exit_tag
|
|
return buy, sell, buy_tag, exit_tag
|
|
|
|
def ignore_expired_candle(self, latest_date: datetime, current_time: datetime,
|
|
timeframe_seconds: int, buy: bool):
|
|
if self.ignore_buying_expired_candle_after and buy:
|
|
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_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
|
|
"""
|
|
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 buy signal and ignore_roi is set, we don't need to evaluate min_roi.
|
|
roi_reached = (not (buy 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 buy:
|
|
if sell:
|
|
sell_signal = SellType.SELL_SIGNAL
|
|
else:
|
|
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 sell reason returned from custom_sell is too '
|
|
f'long and was trimmed to {CUSTOM_SELL_MAX_LENGTH} '
|
|
f'characters.')
|
|
custom_reason = custom_reason[:CUSTOM_SELL_MAX_LENGTH]
|
|
else:
|
|
custom_reason = None
|
|
if sell_signal in (SellType.CUSTOM_SELL, SellType.SELL_SIGNAL):
|
|
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)
|
|
|
|
# Start evaluations
|
|
# Sequence:
|
|
# Sell-signal
|
|
# ROI (if not stoploss)
|
|
# 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 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 sell 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 sell 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)
|
|
|
|
if self.use_custom_stoploss and trade.stop_loss < (low or current_rate):
|
|
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):
|
|
# 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:.2%}")
|
|
|
|
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 >= (low or 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 {(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}")
|
|
logger.debug(f"{trade.pair} - Trailing stop saved "
|
|
f"{trade.stop_loss - trade.initial_stop_loss:.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 ft_check_timed_out(self, side: str, trade: LocalTrade, order: Order,
|
|
current_time: datetime) -> bool:
|
|
"""
|
|
FT Internal method.
|
|
Check if timeout is active, and if the order is still open and timed out
|
|
"""
|
|
timeout = self.config.get('unfilledtimeout', {}).get(side)
|
|
if timeout is not None:
|
|
timeout_unit = self.config.get('unfilledtimeout', {}).get('unit', 'minutes')
|
|
timeout_kwargs = {timeout_unit: -timeout}
|
|
timeout_threshold = current_time + timedelta(**timeout_kwargs)
|
|
timedout = (order.status == 'open' and order.side == side
|
|
and order.order_date_utc < timeout_threshold)
|
|
if timedout:
|
|
return True
|
|
time_method = self.check_sell_timeout if order.side == 'sell' else self.check_buy_timeout
|
|
|
|
return strategy_safe_wrapper(time_method,
|
|
default_retval=False)(
|
|
pair=trade.pair, trade=trade, order=order,
|
|
current_time=current_time)
|
|
|
|
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_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.
|
|
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 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')}.")
|
|
|
|
# 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_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 metadata: Additional information dictionary, with details 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 metadata: Additional information dictionary, with details 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)
|