412 lines
16 KiB
Python
412 lines
16 KiB
Python
"""
|
|
IStrategy interface
|
|
This module defines the interface to apply for strategies
|
|
"""
|
|
import logging
|
|
from abc import ABC, abstractmethod
|
|
from datetime import datetime
|
|
from enum import Enum
|
|
from typing import Dict, List, NamedTuple, Tuple
|
|
import warnings
|
|
|
|
import arrow
|
|
from pandas import DataFrame
|
|
|
|
from freqtrade import constants
|
|
from freqtrade.data.dataprovider import DataProvider
|
|
from freqtrade.persistence import Trade
|
|
from freqtrade.wallets import Wallets
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class SignalType(Enum):
|
|
"""
|
|
Enum to distinguish between buy and sell signals
|
|
"""
|
|
BUY = "buy"
|
|
SELL = "sell"
|
|
|
|
|
|
class SellType(Enum):
|
|
"""
|
|
Enum to distinguish between sell reasons
|
|
"""
|
|
ROI = "roi"
|
|
STOP_LOSS = "stop_loss"
|
|
STOPLOSS_ON_EXCHANGE = "stoploss_on_exchange"
|
|
TRAILING_STOP_LOSS = "trailing_stop_loss"
|
|
SELL_SIGNAL = "sell_signal"
|
|
FORCE_SELL = "force_sell"
|
|
NONE = ""
|
|
|
|
|
|
class SellCheckTuple(NamedTuple):
|
|
"""
|
|
NamedTuple for Sell type + reason
|
|
"""
|
|
sell_flag: bool
|
|
sell_type: SellType
|
|
|
|
|
|
class IStrategy(ABC):
|
|
"""
|
|
Interface for freqtrade strategies
|
|
Defines the mandatory structure must follow any custom strategies
|
|
|
|
Attributes you can use:
|
|
minimal_roi -> Dict: Minimal ROI designed for the strategy
|
|
stoploss -> float: optimal stoploss designed for the strategy
|
|
ticker_interval -> str: value of the ticker interval to use for the strategy
|
|
"""
|
|
|
|
_populate_fun_len: int = 0
|
|
_buy_fun_len: int = 0
|
|
_sell_fun_len: int = 0
|
|
# associated minimal roi
|
|
minimal_roi: Dict
|
|
|
|
# associated stoploss
|
|
stoploss: float
|
|
|
|
# trailing stoploss
|
|
trailing_stop: bool = False
|
|
trailing_stop_positive: float
|
|
trailing_stop_positive_offset: float
|
|
trailing_only_offset_is_reached = False
|
|
|
|
# associated ticker interval
|
|
ticker_interval: str
|
|
|
|
# Optional order types
|
|
order_types: Dict = {
|
|
'buy': 'limit',
|
|
'sell': 'limit',
|
|
'stoploss': 'limit',
|
|
'stoploss_on_exchange': False,
|
|
'stoploss_on_exchange_interval': 60,
|
|
}
|
|
|
|
# Optional time in force
|
|
order_time_in_force: Dict = {
|
|
'buy': 'gtc',
|
|
'sell': 'gtc',
|
|
}
|
|
|
|
# run "populate_indicators" only for new candle
|
|
process_only_new_candles: bool = False
|
|
|
|
# Class level variables (intentional) containing
|
|
# the dataprovider (dp) (access to other candles, historic data, ...)
|
|
# and wallets - access to the current balance.
|
|
dp: DataProvider
|
|
wallets: Wallets
|
|
|
|
def __init__(self, config: dict) -> None:
|
|
self.config = config
|
|
# Dict to determine if analysis is necessary
|
|
self._last_candle_seen_per_pair: Dict[str, datetime] = {}
|
|
|
|
@abstractmethod
|
|
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Populate indicators that will be used in the Buy and Sell strategy
|
|
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
|
|
:param metadata: Additional information, like the currently traded pair
|
|
:return: a Dataframe with all mandatory indicators for the strategies
|
|
"""
|
|
|
|
@abstractmethod
|
|
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Based on TA indicators, populates the buy signal for the given dataframe
|
|
:param dataframe: DataFrame
|
|
:param metadata: Additional information, like the currently traded pair
|
|
:return: DataFrame with buy column
|
|
"""
|
|
|
|
@abstractmethod
|
|
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Based on TA indicators, populates the sell signal for the given dataframe
|
|
:param dataframe: DataFrame
|
|
:param metadata: Additional information, like the currently traded pair
|
|
:return: DataFrame with sell column
|
|
"""
|
|
|
|
def informative_pairs(self) -> List[Tuple[str, str]]:
|
|
"""
|
|
Define additional, informative pair/interval combinations to be cached from the exchange.
|
|
These pair/interval combinations are non-tradeable, unless they are part
|
|
of the whitelist as well.
|
|
For more information, please consult the documentation
|
|
:return: List of tuples in the format (pair, interval)
|
|
Sample: return [("ETH/USDT", "5m"),
|
|
("BTC/USDT", "15m"),
|
|
]
|
|
"""
|
|
return []
|
|
|
|
def get_strategy_name(self) -> str:
|
|
"""
|
|
Returns strategy class name
|
|
"""
|
|
return self.__class__.__name__
|
|
|
|
def analyze_ticker(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Parses the given ticker history and returns a populated DataFrame
|
|
add several TA indicators and buy signal to it
|
|
:return DataFrame with ticker data and indicator data
|
|
"""
|
|
|
|
pair = str(metadata.get('pair'))
|
|
|
|
# Test if seen this pair and last candle before.
|
|
# always run if process_only_new_candles is set to false
|
|
if (not self.process_only_new_candles or
|
|
self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
|
|
# Defs that only make change on new candle data.
|
|
logger.debug("TA Analysis Launched")
|
|
dataframe = self.advise_indicators(dataframe, metadata)
|
|
dataframe = self.advise_buy(dataframe, metadata)
|
|
dataframe = self.advise_sell(dataframe, metadata)
|
|
self._last_candle_seen_per_pair[pair] = dataframe.iloc[-1]['date']
|
|
else:
|
|
logger.debug("Skipping TA Analysis for already analyzed candle")
|
|
dataframe['buy'] = 0
|
|
dataframe['sell'] = 0
|
|
|
|
# Other Defs in strategy that want to be called every loop here
|
|
# twitter_sell = self.watch_twitter_feed(dataframe, metadata)
|
|
logger.debug("Loop Analysis Launched")
|
|
|
|
return dataframe
|
|
|
|
def get_signal(self, pair: str, interval: str,
|
|
dataframe: DataFrame) -> Tuple[bool, bool]:
|
|
"""
|
|
Calculates current signal based several technical analysis indicators
|
|
:param pair: pair in format ANT/BTC
|
|
:param interval: Interval to use (in min)
|
|
:param dataframe: Dataframe to analyze
|
|
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
|
|
"""
|
|
if not isinstance(dataframe, DataFrame) or dataframe.empty:
|
|
logger.warning('Empty ticker history for pair %s', pair)
|
|
return False, False
|
|
|
|
try:
|
|
dataframe = self.analyze_ticker(dataframe, {'pair': pair})
|
|
except ValueError as error:
|
|
logger.warning(
|
|
'Unable to analyze ticker for pair %s: %s',
|
|
pair,
|
|
str(error)
|
|
)
|
|
return False, False
|
|
except Exception as error:
|
|
logger.exception(
|
|
'Unexpected error when analyzing ticker for pair %s: %s',
|
|
pair,
|
|
str(error)
|
|
)
|
|
return False, False
|
|
|
|
if dataframe.empty:
|
|
logger.warning('Empty dataframe for pair %s', pair)
|
|
return False, False
|
|
|
|
latest = dataframe.iloc[-1]
|
|
|
|
# Check if dataframe is out of date
|
|
signal_date = arrow.get(latest['date'])
|
|
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
|
|
offset = self.config.get('exchange', {}).get('outdated_offset', 5)
|
|
if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + offset))):
|
|
logger.warning(
|
|
'Outdated history for pair %s. Last tick is %s minutes old',
|
|
pair,
|
|
(arrow.utcnow() - signal_date).seconds // 60
|
|
)
|
|
return False, False
|
|
|
|
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
|
|
logger.debug(
|
|
'trigger: %s (pair=%s) buy=%s sell=%s',
|
|
latest['date'],
|
|
pair,
|
|
str(buy),
|
|
str(sell)
|
|
)
|
|
return buy, sell
|
|
|
|
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
|
|
sell: bool, low: float = None, high: float = None,
|
|
force_stoploss: float = 0) -> SellCheckTuple:
|
|
"""
|
|
This function evaluate if on the condition required to trigger a sell has been reached
|
|
if the threshold is reached and updates the trade record.
|
|
:return: True if trade should be sold, False otherwise
|
|
"""
|
|
|
|
# Set current rate to low for backtesting sell
|
|
current_rate = low or rate
|
|
current_profit = trade.calc_profit_percent(current_rate)
|
|
|
|
stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
|
|
current_time=date, current_profit=current_profit,
|
|
force_stoploss=force_stoploss)
|
|
|
|
if stoplossflag.sell_flag:
|
|
return stoplossflag
|
|
|
|
# Set current rate to low for backtesting sell
|
|
current_rate = high or rate
|
|
current_profit = trade.calc_profit_percent(current_rate)
|
|
experimental = self.config.get('experimental', {})
|
|
|
|
if buy and experimental.get('ignore_roi_if_buy_signal', False):
|
|
logger.debug('Buy signal still active - not selling.')
|
|
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
|
|
|
|
# Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
|
|
if self.min_roi_reached(trade=trade, current_profit=current_profit, current_time=date):
|
|
logger.debug('Required profit reached. Selling..')
|
|
return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
|
|
|
|
if experimental.get('sell_profit_only', False):
|
|
logger.debug('Checking if trade is profitable..')
|
|
if trade.calc_profit(rate=rate) <= 0:
|
|
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
|
|
if sell and not buy and experimental.get('use_sell_signal', False):
|
|
logger.debug('Sell signal received. Selling..')
|
|
return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)
|
|
|
|
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
|
|
|
|
def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: datetime,
|
|
current_profit: float, force_stoploss: float) -> SellCheckTuple:
|
|
"""
|
|
Based on current profit of the trade and configured (trailing) stoploss,
|
|
decides to sell or not
|
|
:param current_profit: current profit in percent
|
|
"""
|
|
|
|
trailing_stop = self.config.get('trailing_stop', False)
|
|
trade.adjust_stop_loss(trade.open_rate, force_stoploss if force_stoploss
|
|
else self.stoploss, initial=True)
|
|
|
|
# evaluate if the stoploss was hit if stoploss is not on exchange
|
|
if ((self.stoploss is not None) and
|
|
(trade.stop_loss >= current_rate) and
|
|
(not self.order_types.get('stoploss_on_exchange'))):
|
|
selltype = SellType.STOP_LOSS
|
|
# If Trailing stop (and max-rate did move above open rate)
|
|
if trailing_stop and trade.open_rate != trade.max_rate:
|
|
selltype = SellType.TRAILING_STOP_LOSS
|
|
logger.debug(
|
|
f"HIT STOP: current price at {current_rate:.6f}, "
|
|
f"stop loss is {trade.stop_loss:.6f}, "
|
|
f"initial stop loss was at {trade.initial_stop_loss:.6f}, "
|
|
f"trade opened at {trade.open_rate:.6f}")
|
|
logger.debug(f"trailing stop saved {trade.stop_loss - trade.initial_stop_loss:.6f}")
|
|
|
|
logger.debug('Stop loss hit.')
|
|
return SellCheckTuple(sell_flag=True, sell_type=selltype)
|
|
|
|
# update the stop loss afterwards, after all by definition it's supposed to be hanging
|
|
if trailing_stop:
|
|
|
|
# check if we have a special stop loss for positive condition
|
|
# and if profit is positive
|
|
stop_loss_value = force_stoploss if force_stoploss else self.stoploss
|
|
|
|
sl_offset = self.config.get('trailing_stop_positive_offset') or 0.0
|
|
|
|
if 'trailing_stop_positive' in self.config and current_profit > sl_offset:
|
|
|
|
# Ignore mypy error check in configuration that this is a float
|
|
stop_loss_value = self.config.get('trailing_stop_positive') # type: ignore
|
|
logger.debug(f"using positive stop loss mode: {stop_loss_value} "
|
|
f"with offset {sl_offset:.4g} "
|
|
f"since we have profit {current_profit:.4f}%")
|
|
|
|
# if trailing_only_offset_is_reached is true,
|
|
# we update trailing stoploss only if offset is reached.
|
|
tsl_only_offset = self.config.get('trailing_only_offset_is_reached', False)
|
|
if not (tsl_only_offset and current_profit < sl_offset):
|
|
trade.adjust_stop_loss(current_rate, stop_loss_value)
|
|
|
|
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
|
|
|
|
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
|
|
"""
|
|
Based an earlier trade and current price and ROI configuration, decides whether bot should
|
|
sell. Requires current_profit to be in percent!!
|
|
:return True if bot should sell at current rate
|
|
"""
|
|
|
|
# Check if time matches and current rate is above threshold
|
|
trade_dur = (current_time.timestamp() - trade.open_date.timestamp()) / 60
|
|
|
|
# Get highest entry in ROI dict where key >= trade-duration
|
|
roi_entry = max(list(filter(lambda x: trade_dur >= x, self.minimal_roi.keys())))
|
|
threshold = self.minimal_roi[roi_entry]
|
|
if current_profit > threshold:
|
|
return True
|
|
|
|
return False
|
|
|
|
def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
|
|
"""
|
|
Creates a dataframe and populates indicators for given ticker data
|
|
"""
|
|
return {pair: self.advise_indicators(pair_data, {'pair': pair})
|
|
for pair, pair_data in tickerdata.items()}
|
|
|
|
def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
"""
|
|
Populate indicators that will be used in the Buy and Sell strategy
|
|
This method should not be overridden.
|
|
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
|
|
:param metadata: Additional information, like the currently traded pair
|
|
:return: a Dataframe with all mandatory indicators for the strategies
|
|
"""
|
|
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
|
|
"""
|
|
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
|
|
"""
|
|
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)
|