import logging from datetime import timedelta import arrow import talib.abstract as ta from pandas import DataFrame, to_datetime from freqtrade.exchange import get_ticker_history from freqtrade.vendor.qtpylib.indicators import awesome_oscillator logger = logging.getLogger(__name__) def parse_ticker_dataframe(ticker: list) -> DataFrame: """ Analyses the trend for the given ticker history :param ticker: See exchange.get_ticker_history :return: DataFrame """ columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'} frame = DataFrame(ticker) \ .drop('BV', 1) \ .rename(columns=columns) frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True) frame.sort_values('date', inplace=True) return frame def populate_indicators(dataframe: DataFrame) -> DataFrame: """ Adds several different TA indicators to the given DataFrame """ dataframe['sar'] = ta.SAR(dataframe) dataframe['adx'] = ta.ADX(dataframe) stoch = ta.STOCHF(dataframe) dataframe['fastd'] = stoch['fastd'] dataframe['fastk'] = stoch['fastk'] dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband'] dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) dataframe['mfi'] = ta.MFI(dataframe) dataframe['rsi'] = ta.RSI(dataframe) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) dataframe['ao'] = awesome_oscillator(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] return dataframe def populate_buy_trend(dataframe: DataFrame) -> DataFrame: """ Based on TA indicators, populates the buy trend for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.ix[ (dataframe['close'] < dataframe['sma']) & (dataframe['tema'] <= dataframe['blower']) & (dataframe['mfi'] < 25) & (dataframe['fastd'] < 25) & (dataframe['adx'] > 30), 'buy'] = 1 dataframe.ix[dataframe['buy'] == 1, 'buy_price'] = dataframe['close'] return dataframe def analyze_ticker(pair: str) -> DataFrame: """ Get ticker data for given currency pair, push it to a DataFrame and add several TA indicators and buy signal to it :return DataFrame with ticker data and indicator data """ data = get_ticker_history(pair) dataframe = parse_ticker_dataframe(data) if dataframe.empty: logger.warning('Empty dataframe for pair %s', pair) return dataframe dataframe = populate_indicators(dataframe) dataframe = populate_buy_trend(dataframe) return dataframe def get_buy_signal(pair: str) -> bool: """ Calculates a buy signal based several technical analysis indicators :param pair: pair in format BTC_ANT or BTC-ANT :return: True if pair is good for buying, False otherwise """ dataframe = analyze_ticker(pair) if dataframe.empty: return False latest = dataframe.iloc[-1] # Check if dataframe is out of date signal_date = arrow.get(latest['date']) if signal_date < arrow.now() - timedelta(minutes=10): return False signal = latest['buy'] == 1 logger.debug('buy_trigger: %s (pair=%s, signal=%s)', latest['date'], pair, signal) return signal