Move Analyze to a class
This commit is contained in:
Gerald Lonlas
@@ -1,121 +1,190 @@
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"""
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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 timedelta
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from enum import Enum
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from typing import Dict, List
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import arrow
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from datetime import datetime, timedelta
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from enum import Enum
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from pandas import DataFrame, to_datetime
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from typing import Dict, List
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from freqtrade.exchange import get_ticker_history
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from freqtrade.logger import Logger
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from freqtrade.strategy.strategy import Strategy
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logger = logging.getLogger(__name__)
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from freqtrade.persistence import Trade
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class SignalType(Enum):
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""" Enum to distinguish between buy and sell signals """
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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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def parse_ticker_dataframe(ticker: list) -> DataFrame:
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class Analyze(object):
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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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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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columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
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frame = DataFrame(ticker) \
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.rename(columns=columns)
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if 'BV' in frame:
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frame.drop('BV', 1, inplace=True)
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frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
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frame.sort_values('date', inplace=True)
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return frame
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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.logger = Logger(name=__name__).get_logger()
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self.config = config
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self.strategy = Strategy()
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self.strategy.init(self.config)
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def populate_indicators(dataframe: DataFrame) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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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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columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
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frame = DataFrame(ticker) \
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.rename(columns=columns)
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if 'BV' in frame:
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frame.drop('BV', 1, inplace=True)
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frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
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frame.sort_values('date', inplace=True)
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return frame
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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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strategy = Strategy()
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return strategy.populate_indicators(dataframe=dataframe)
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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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def populate_buy_trend(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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strategy = Strategy()
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return strategy.populate_buy_trend(dataframe=dataframe)
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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(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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strategy = Strategy()
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return strategy.populate_sell_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 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 analyze_ticker(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 = parse_ticker_dataframe(ticker_history)
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dataframe = populate_indicators(dataframe)
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dataframe = populate_buy_trend(dataframe)
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dataframe = populate_sell_trend(dataframe)
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return dataframe
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# FIX: Maybe return False, if an error has occured,
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# Otherwise we might mask an error as an non-signal-scenario
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def get_signal(self, pair: str, interval: int) -> (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 BTC_ANT or BTC-ANT
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:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
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"""
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ticker_hist = get_ticker_history(pair, interval)
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if not ticker_hist:
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self.logger.warning('Empty ticker history for pair %s', pair)
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return (False, False) # return 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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self.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) # return False ?
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except Exception as error:
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self.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) # return False ?
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# FIX: Maybe return False, if an error has occured,
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# Otherwise we might mask an error as an non-signal-scenario
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def get_signal(pair: str, interval: int) -> (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 BTC_ANT or BTC-ANT
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:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
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"""
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ticker_hist = 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) # return False ?
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if dataframe.empty:
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self.logger.warning('Empty dataframe for pair %s', pair)
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return (False, False) # return False ?
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try:
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dataframe = analyze_ticker(ticker_hist)
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except ValueError as ex:
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logger.warning('Unable to analyze ticker for pair %s: %s', pair, str(ex))
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return (False, False) # return False ?
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except Exception as ex:
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logger.exception('Unexpected error when analyzing ticker for pair %s: %s', pair, str(ex))
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return (False, False) # return False ?
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latest = dataframe.iloc[-1]
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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) # return False ?
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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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if signal_date < arrow.now() - timedelta(minutes=(interval + 5)):
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self.logger.warning('Too old dataframe for pair %s', pair)
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return (False, False) # return False ?
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latest = dataframe.iloc[-1]
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(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
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self.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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# Check if dataframe is out of date
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signal_date = arrow.get(latest['date'])
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if signal_date < arrow.now() - timedelta(minutes=(interval + 5)):
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logger.warning('Too old dataframe for pair %s', pair)
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return (False, False) # return False ?
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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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# 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_rate=rate, current_time=date):
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self.logger.debug('Executing sell due to ROI ...')
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return True
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(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
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logger.debug('trigger: %s (pair=%s) buy=%s sell=%s', latest['date'], pair, str(buy), str(sell))
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return (buy, sell)
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# Experimental: Check if the trade is profitable before selling it (avoid selling at loss)
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if self.config.get('experimental', {}).get('sell_profit_only', False):
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self.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 self.config.get('experimental', {}).get('use_sell_signal', False):
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self.logger.debug('Executing sell due to sell signal ...')
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return True
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return False
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def min_roi_reached(self, trade: Trade, current_rate: 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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current_profit = trade.calc_profit_percent(current_rate)
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if self.strategy.stoploss is not None and current_profit < float(self.strategy.stoploss):
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self.logger.debug('Stop loss hit.')
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return True
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# Check if time matches and current rate is above threshold
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time_diff = (current_time - trade.open_date).total_seconds() / 60
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for duration, threshold in sorted(self.strategy.minimal_roi.items()):
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if time_diff > float(duration) and current_profit > threshold:
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return True
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self.logger.debug(
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'Threshold not reached. (cur_profit: %1.2f%%)',
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float(current_profit) * 100.0
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)
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return False
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