136 lines
4.5 KiB
Python
136 lines
4.5 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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from enum import Enum
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import logging
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from datetime import timedelta
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import arrow
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import talib.abstract as ta
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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.vendor.qtpylib.indicators import awesome_oscillator, crossed_above
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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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.drop('BV', 1) \
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.rename(columns=columns)
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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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"""
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dataframe['sar'] = ta.SAR(dataframe)
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dataframe['adx'] = ta.ADX(dataframe)
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stoch = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch['fastd']
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dataframe['fastk'] = stoch['fastk']
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dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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dataframe['mfi'] = ta.MFI(dataframe)
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dataframe['rsi'] = ta.RSI(dataframe)
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dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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dataframe['ao'] = awesome_oscillator(dataframe)
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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hilbert = ta.HT_SINE(dataframe)
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dataframe['htsine'] = hilbert['sine']
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dataframe['htleadsine'] = hilbert['leadsine']
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return 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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dataframe.loc[
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(dataframe['tema'] <= dataframe['blower']) &
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(dataframe['rsi'] < 37) &
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(dataframe['fastd'] < 48) &
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(dataframe['adx'] > 31),
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'buy'] = 1
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return 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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dataframe.loc[
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(crossed_above(dataframe['rsi'], 70)),
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'sell'] = 1
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return dataframe
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def analyze_ticker(pair: str) -> DataFrame:
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"""
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Get ticker data for given currency pair, push it to a DataFrame and
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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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ticker_hist = get_ticker_history(pair)
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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 DataFrame()
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dataframe = parse_ticker_dataframe(ticker_hist)
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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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# TODO: buy_price and sell_price are only used by the plotter, should probably be moved there
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dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
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dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
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return dataframe
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def get_signal(pair: str, signal: SignalType) -> 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 if pair is good for buying, False otherwise
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"""
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dataframe = analyze_ticker(pair)
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if dataframe.empty:
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return 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
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result = latest[signal.value] == 1
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logger.debug('%s_trigger: %s (pair=%s, signal=%s)', signal.value, latest['date'], pair, result)
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return result
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