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118 lines
3.8 KiB
Python
118 lines
3.8 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 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 pandas import DataFrame, to_datetime
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from freqtrade.exchange import get_ticker_history
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from freqtrade.strategy.strategy import Strategy
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logger = logging.getLogger(__name__)
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class SignalType(Enum):
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""" Enum to distinguish between buy and sell signals """
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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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"""
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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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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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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_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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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 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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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: (True, False) if pair is good for buying and not for selling
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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)
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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)
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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)
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if dataframe.empty:
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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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if signal_date < arrow.now() - timedelta(minutes=10):
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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('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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