""" Functions to analyze ticker data with indicators and produce buy and sell signals """ import logging from datetime import timedelta from enum import Enum from typing import Dict, List import arrow import talib.abstract as ta from pandas import DataFrame, to_datetime import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.exchange import get_ticker_history logger = logging.getLogger(__name__) class SignalType(Enum): """ Enum to distinguish between buy and sell signals """ BUY = "buy" SELL = "sell" 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 Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ # Momentum Indicator # ------------------------------------ # ADX dataframe['adx'] = ta.ADX(dataframe) # Awesome oscillator dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) """ # Commodity Channel Index: values Oversold:<-100, Overbought:>100 dataframe['cci'] = ta.CCI(dataframe) """ # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # MFI dataframe['mfi'] = ta.MFI(dataframe) # Minus Directional Indicator / Movement dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) # Plus Directional Indicator / Movement dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) """ # ROC dataframe['roc'] = ta.ROC(dataframe) """ # RSI dataframe['rsi'] = ta.RSI(dataframe) """ # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stoch stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] """ # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] """ # Stoch RSI stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] """ # Overlap Studies # ------------------------------------ # Previous Bollinger bands # Because ta.BBANDS implementation is broken with small numbers, it actually # returns middle band for all the three bands. Switch to qtpylib.bollinger_bands # and use middle band instead. dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband'] """ # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] """ # EMA - Exponential Moving Average 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) # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # Cycle Indicator # ------------------------------------ # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ """ # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # Inverted Hammer: values [0, 100] dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # Dragonfly Doji: values [0, 100] dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # Piercing Line: values [0, 100] dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # Morningstar: values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # Three White Soldiers: values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] """ # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ """ # Hanging Man: values [0, 100] dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # Shooting Star: values [0, 100] dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # Gravestone Doji: values [0, 100] dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # Dark Cloud Cover: values [0, 100] dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # Evening Doji Star: values [0, 100] dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # Evening Star: values [0, 100] dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) """ # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ """ # Three Line Strike: values [0, -100, 100] dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # Spinning Top: values [0, -100, 100] dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # Engulfing: values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # Harami: values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # Three Outside Up/Down: values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # Three Inside Up/Down: values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] """ # Chart type # ------------------------------------ """ # Heikinashi stategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] """ return dataframe def populate_buy_trend(dataframe: DataFrame) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( (dataframe['rsi'] < 35) & (dataframe['fastd'] < 35) & (dataframe['adx'] > 30) & (dataframe['plus_di'] > 0.5) ) | ( (dataframe['adx'] > 65) & (dataframe['plus_di'] > 0.5) ), 'buy'] = 1 return dataframe def populate_sell_trend(dataframe: DataFrame) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with buy column """ dataframe.loc[ ( ( (qtpylib.crossed_above(dataframe['rsi'], 70)) | (qtpylib.crossed_above(dataframe['fastd'], 70)) ) & (dataframe['adx'] > 10) & (dataframe['minus_di'] > 0) ) | ( (dataframe['adx'] > 70) & (dataframe['minus_di'] > 0.5) ), 'sell'] = 1 return dataframe def analyze_ticker(ticker_history: List[Dict]) -> DataFrame: """ Parses the given ticker history and returns a populated DataFrame add several TA indicators and buy signal to it :return DataFrame with ticker data and indicator data """ dataframe = parse_ticker_dataframe(ticker_history) dataframe = populate_indicators(dataframe) dataframe = populate_buy_trend(dataframe) dataframe = populate_sell_trend(dataframe) return dataframe def get_signal(pair: str, signal: SignalType) -> bool: """ Calculates current 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 """ ticker_hist = get_ticker_history(pair) if not ticker_hist: logger.warning('Empty ticker history for pair %s', pair) return False try: dataframe = analyze_ticker(ticker_hist) except ValueError as ex: logger.warning('Unable to analyze ticker for pair %s: %s', pair, str(ex)) return False except Exception as ex: logger.exception('Unexpected error when analyzing ticker for pair %s: %s', pair, str(ex)) return False if dataframe.empty: logger.warning('Empty dataframe for pair %s', pair) 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): logger.warning('Too old dataframe for pair %s', pair) return False # FIX: 20180109, there could be some confusion because we will make a # boolean result (execute the action or not depending on the signal). # But the above checks can also return False, and we hide that. result = latest[signal.value] == 1 logger.debug('%s_trigger: %s (pair=%s, signal=%s)', signal.value, latest['date'], pair, result) return result