272 lines
10 KiB
Python
272 lines
10 KiB
Python
"""
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Functions to analyze ticker data with indicators and produce buy and sell signals
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"""
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import logging
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from datetime import datetime
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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, to_datetime
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from freqtrade import constants
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from freqtrade.exchange import Exchange
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from freqtrade.persistence import Trade
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from freqtrade.strategy.resolver import IStrategy, StrategyResolver
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logger = logging.getLogger(__name__)
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class SignalType(Enum):
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"""
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Enum to distinguish between buy and sell signals
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"""
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BUY = "buy"
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SELL = "sell"
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class Analyze(object):
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"""
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Analyze class contains everything the bot need to determine if the situation is good for
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buying or selling.
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"""
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def __init__(self, config: dict) -> None:
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"""
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Init Analyze
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:param config: Bot configuration (use the one from Configuration())
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"""
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self.config = config
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self.strategy: IStrategy = StrategyResolver(self.config).strategy
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@staticmethod
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def parse_ticker_dataframe(ticker: list) -> DataFrame:
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"""
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Analyses the trend for the given ticker history
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:param ticker: See exchange.get_ticker_history
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:return: DataFrame
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"""
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cols = ['date', 'open', 'high', 'low', 'close', 'volume']
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frame = DataFrame(ticker, columns=cols)
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frame['date'] = to_datetime(frame['date'],
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unit='ms',
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utc=True,
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infer_datetime_format=True)
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# group by index and aggregate results to eliminate duplicate ticks
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frame = frame.groupby(by='date', as_index=False, sort=True).agg({
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'open': 'first',
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'high': 'max',
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'low': 'min',
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'close': 'last',
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'volume': 'max',
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})
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frame.drop(frame.tail(1).index, inplace=True) # eliminate partial candle
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return frame
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def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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"""
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return self.strategy.populate_indicators(dataframe=dataframe)
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def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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return self.strategy.populate_buy_trend(dataframe=dataframe)
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def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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return self.strategy.populate_sell_trend(dataframe=dataframe)
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def get_ticker_interval(self) -> str:
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"""
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Return ticker interval to use
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:return: Ticker interval value to use
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"""
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return self.strategy.ticker_interval
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def get_stoploss(self) -> float:
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"""
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Return stoploss to use
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:return: Strategy stoploss value to use
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"""
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return self.strategy.stoploss
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def analyze_ticker(self, ticker_history: List[Dict]) -> DataFrame:
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"""
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Parses the given ticker history and returns a populated DataFrame
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add several TA indicators and buy signal to it
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:return DataFrame with ticker data and indicator data
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"""
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dataframe = self.parse_ticker_dataframe(ticker_history)
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dataframe = self.populate_indicators(dataframe)
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dataframe = self.populate_buy_trend(dataframe)
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dataframe = self.populate_sell_trend(dataframe)
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return dataframe
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def get_signal(self, exchange: Exchange, pair: str, interval: str) -> Tuple[bool, bool]:
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"""
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Calculates current signal based several technical analysis indicators
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:param pair: pair in format ANT/BTC
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:param interval: Interval to use (in min)
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:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
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"""
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ticker_hist = exchange.get_ticker_history(pair, interval)
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if not ticker_hist:
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logger.warning('Empty ticker history for pair %s', pair)
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return False, False
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try:
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dataframe = self.analyze_ticker(ticker_hist)
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except ValueError as error:
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logger.warning(
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'Unable to analyze ticker for pair %s: %s',
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pair,
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str(error)
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)
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return False, False
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except Exception as error:
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logger.exception(
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'Unexpected error when analyzing ticker for pair %s: %s',
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pair,
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str(error)
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)
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return False, False
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if dataframe.empty:
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logger.warning('Empty dataframe for pair %s', pair)
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return False, False
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latest = dataframe.iloc[-1]
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# Check if dataframe is out of date
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signal_date = arrow.get(latest['date'])
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interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
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if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + 5))):
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logger.warning(
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'Outdated history for pair %s. Last tick is %s minutes old',
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pair,
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(arrow.utcnow() - signal_date).seconds // 60
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)
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return False, False
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(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
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logger.debug(
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'trigger: %s (pair=%s) buy=%s sell=%s',
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latest['date'],
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pair,
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str(buy),
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str(sell)
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)
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return buy, sell
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def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool, sell: bool) -> bool:
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"""
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This function evaluate if on the condition required to trigger a sell has been reached
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if the threshold is reached and updates the trade record.
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:return: True if trade should be sold, False otherwise
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"""
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current_profit = trade.calc_profit_percent(rate)
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if self.stop_loss_reached(current_rate=rate, trade=trade, current_time=date,
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current_profit=current_profit):
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return True
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experimental = self.config.get('experimental', {})
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if buy and experimental.get('ignore_roi_if_buy_signal', False):
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logger.debug('Buy signal still active - not selling.')
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return False
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# Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
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if self.min_roi_reached(trade=trade, current_profit=current_profit, current_time=date):
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logger.debug('Required profit reached. Selling..')
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return True
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if experimental.get('sell_profit_only', False):
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logger.debug('Checking if trade is profitable..')
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if trade.calc_profit(rate=rate) <= 0:
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return False
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if sell and not buy and experimental.get('use_sell_signal', False):
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logger.debug('Sell signal received. Selling..')
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return True
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return False
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def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: datetime,
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current_profit: float) -> bool:
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"""
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Based on current profit of the trade and configured (trailing) stoploss,
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decides to sell or not
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"""
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trailing_stop = self.config.get('trailing_stop', False)
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trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss, initial=True)
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# evaluate if the stoploss was hit
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if self.strategy.stoploss is not None and trade.stop_loss >= current_rate:
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if trailing_stop:
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logger.debug(
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f"HIT STOP: current price at {current_rate:.6f}, "
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f"stop loss is {trade.stop_loss:.6f}, "
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f"initial stop loss was at {trade.initial_stop_loss:.6f}, "
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f"trade opened at {trade.open_rate:.6f}")
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logger.debug(f"trailing stop saved {trade.stop_loss - trade.initial_stop_loss:.6f}")
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logger.debug('Stop loss hit.')
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return True
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# update the stop loss afterwards, after all by definition it's supposed to be hanging
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if trailing_stop:
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# check if we have a special stop loss for positive condition
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# and if profit is positive
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stop_loss_value = self.strategy.stoploss
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if 'trailing_stop_positive' in self.config and current_profit > 0:
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# Ignore mypy error check in configuration that this is a float
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stop_loss_value = self.config.get('trailing_stop_positive') # type: ignore
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logger.debug(f"using positive stop loss mode: {stop_loss_value} "
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f"since we have profit {current_profit}")
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trade.adjust_stop_loss(current_rate, stop_loss_value)
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return False
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def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
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"""
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Based an earlier trade and current price and ROI configuration, decides whether bot should
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sell
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:return True if bot should sell at current rate
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"""
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# Check if time matches and current rate is above threshold
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time_diff = (current_time.timestamp() - trade.open_date.timestamp()) / 60
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for duration, threshold in self.strategy.minimal_roi.items():
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if time_diff <= duration:
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return False
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if current_profit > threshold:
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return True
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return False
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def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
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"""
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Creates a dataframe and populates indicators for given ticker data
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"""
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return {pair: self.populate_indicators(self.parse_ticker_dataframe(pair_data))
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for pair, pair_data in tickerdata.items()}
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