mirror of
https://github.com/freqtrade/freqtrade.git
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116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
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
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logging.basicConfig(level=logging.DEBUG,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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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['cci'] = ta.CCI(dataframe)
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dataframe['rsi'] = ta.RSI(dataframe)
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dataframe['mom'] = ta.MOM(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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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 trend 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.ix[
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(dataframe['close'] < dataframe['sma']) &
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(dataframe['tema'] <= dataframe['blower']) &
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(dataframe['mfi'] < 25) &
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(dataframe['fastd'] < 25) &
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(dataframe['adx'] > 30),
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'buy'] = 1
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dataframe.ix[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
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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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data = get_ticker_history(pair)
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dataframe = parse_ticker_dataframe(data)
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if dataframe.empty:
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logger.warning('Empty dataframe for pair %s', pair)
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return dataframe
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dataframe = populate_indicators(dataframe)
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dataframe = populate_buy_trend(dataframe)
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return dataframe
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def get_buy_signal(pair: str) -> bool:
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"""
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Calculates a buy 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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signal = latest['buy'] == 1
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logger.debug('buy_trigger: %s (pair=%s, signal=%s)', latest['date'], pair, signal)
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return signal
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