freqtrade_origin/freqtrade/strategy/interface.py

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"""
IStrategy interface
This module defines the interface to apply for strategies
"""
import logging
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from abc import ABC, abstractmethod
from datetime import datetime
from enum import Enum
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from typing import Dict, List, Tuple
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import arrow
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from pandas import DataFrame
from freqtrade import constants
from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
from freqtrade.exchange import Exchange
from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
class SignalType(Enum):
"""
Enum to distinguish between buy and sell signals
"""
BUY = "buy"
SELL = "sell"
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class SellType(Enum):
"""
Enum to distinguish between sell reasons
"""
ROI = "roi"
STOP_LOSS = "stop_loss"
TRAILING_STOP_LOSS = "trailing_stop_loss"
SELL_SIGNAL = "sell_signal"
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FORCE_SELL = "force_sell"
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NONE = ""
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class IStrategy(ABC):
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"""
Interface for freqtrade strategies
Defines the mandatory structure must follow any custom strategies
Attributes you can use:
minimal_roi -> Dict: Minimal ROI designed for the strategy
stoploss -> float: optimal stoploss designed for the strategy
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ticker_interval -> str: value of the ticker interval to use for the strategy
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"""
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minimal_roi: Dict
stoploss: float
ticker_interval: str
def __init__(self, config: dict) -> None:
self.config = config
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@abstractmethod
def populate_indicators(self, dataframe: DataFrame) -> 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()
:return: a Dataframe with all mandatory indicators for the strategies
"""
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
@abstractmethod
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
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:return: DataFrame with sell column
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"""
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return self.strategy.populate_sell_trend(dataframe=dataframe)
def get_strategy_name(self) -> str:
"""
Returns strategy class name
"""
return self.strategy.__class__.__name__
def analyze_ticker(self, ticker_history: List[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
"""
dataframe = parse_ticker_dataframe(ticker_history)
dataframe = self.populate_indicators(dataframe)
dataframe = self.populate_buy_trend(dataframe)
dataframe = self.populate_sell_trend(dataframe)
return dataframe
def get_signal(self, exchange: Exchange, pair: str, interval: str) -> 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)
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
ticker_hist = exchange.get_ticker_history(pair, interval)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return False, False
try:
dataframe = self.analyze_ticker(ticker_hist)
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]
if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + 5))):
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
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def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
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sell: bool) -> Tuple[bool, SellType]:
"""
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
"""
current_profit = trade.calc_profit_percent(rate)
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stoplossflag = self.stop_loss_reached(current_rate=rate, trade=trade, current_time=date,
current_profit=current_profit)
if stoplossflag[0]:
return (True, stoplossflag[1])
experimental = self.config.get('experimental', {})
if buy and experimental.get('ignore_roi_if_buy_signal', False):
logger.debug('Buy signal still active - not selling.')
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return (False, 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..')
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return (True, SellType.ROI)
if experimental.get('sell_profit_only', False):
logger.debug('Checking if trade is profitable..')
if trade.calc_profit(rate=rate) <= 0:
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return (False, SellType.NONE)
if sell and not buy and experimental.get('use_sell_signal', False):
logger.debug('Sell signal received. Selling..')
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return (True, SellType.SELL_SIGNAL)
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return (False, SellType.NONE)
def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: datetime,
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current_profit: float) -> Tuple[bool, SellType]:
"""
Based on current profit of the trade and configured (trailing) stoploss,
decides to sell or not
"""
trailing_stop = self.config.get('trailing_stop', False)
trade.adjust_stop_loss(trade.open_rate, self.stoploss, initial=True)
# evaluate if the stoploss was hit
if self.stoploss is not None and trade.stop_loss >= current_rate:
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selltype = SellType.STOP_LOSS
if trailing_stop:
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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.')
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return (True, 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 = self.stoploss
if 'trailing_stop_positive' in self.config and current_profit > 0:
# 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"since we have profit {current_profit}")
trade.adjust_stop_loss(current_rate, stop_loss_value)
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return (False, 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
:return True if bot should sell at current rate
"""
# Check if time matches and current rate is above threshold
time_diff = (current_time.timestamp() - trade.open_date.timestamp()) / 60
for duration, threshold in self.minimal_roi.items():
if time_diff <= duration:
return False
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.populate_indicators(parse_ticker_dataframe(pair_data))
for pair, pair_data in tickerdata.items()}