def populate_indicators(self, dataframe: DataFrame,
                         metadata: dict) -> DataFrame:
     dataframe['3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe['open'],
                                                  dataframe['high'],
                                                  dataframe['low'],
                                                  dataframe['close'])
     dataframe['EVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe['open'],
                                                  dataframe['high'],
                                                  dataframe['low'],
                                                  dataframe['close'])
     dataframe['ABANDONEDBABY'] = ta.CDLEVENINGSTAR(dataframe['open'],
                                                    dataframe['high'],
                                                    dataframe['low'],
                                                    dataframe['close'])
     dataframe['HARAMI'] = ta.CDLHARAMI(dataframe['open'],
                                        dataframe['high'], dataframe['low'],
                                        dataframe['close'])
     dataframe['INVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(
         dataframe['open'], dataframe['high'], dataframe['low'],
         dataframe['close'])
     dataframe['ENGULFING'] = ta.CDLENGULFING(dataframe['open'],
                                              dataframe['high'],
                                              dataframe['low'],
                                              dataframe['close'])
     dataframe['hclose'] = (dataframe['open'] + dataframe['high'] +
                            dataframe['low'] + dataframe['close']) / 4
     dataframe['hopen'] = (
         (dataframe['open'].shift(2) + dataframe['close'].shift(2)) / 2)
     dataframe['hhigh'] = dataframe[['open', 'close', 'high']].max(axis=1)
     dataframe['hlow'] = dataframe[['open', 'close', 'low']].min(axis=1)
     dataframe['emac'] = ta.SMA(dataframe['hclose'], timeperiod=6)
     dataframe['emao'] = ta.SMA(dataframe['hopen'], timeperiod=6)
     return dataframe
예제 #2
0
    def scan_cdl_bearish(self):
        try:
            cdl = []
            cdl.append(abstract.CDLENGULFING(self.df))
            cdl.append(abstract.CDLPIERCING(self.df))
            cdl.append(abstract.CDLHARAMI(self.df))
            cdl.append(abstract.CDLHAMMER(self.df))
            cdl.append(abstract.CDLINVERTEDHAMMER(self.df))
            cdl.append(abstract.CDLMORNINGDOJISTAR(self.df))
            cdl.append(abstract.CDLMORNINGSTAR(self.df))
            cdl.append(abstract.CDLABANDONEDBABY(self.df))
            cdl.append(abstract.CDLKICKING(self.df))

            for i in range(0, 9):
                for a in range(0, 2):
                    if cdl[i][a] == 100:
                        print("Found")
                        print(i)
                        print(self.df.index[i])
        except:
            print("Error")
            return False
예제 #3
0
파일: gene.py 프로젝트: algobughk/apple
def gen_ta(path):
    df = pd.read_csv('h/%s.csv' % path)
    df = df.dropna()
    df = df.iloc[::-1]
    df = df.reset_index(drop=True)

    #生成技術指標
    sma5 = indicator(talib.MA(df['close'], 5), 'SMA_5')
    sma10 = indicator(talib.MA(df['close'], 10), 'SMA_10')
    sma50 = indicator(talib.MA(df['close'], 50), 'SMA_50')
    rsi = indicator(talib.RSI(df['close']), 'RSI_14')
    sma5u = sma5.diff().rename(columns={'SMA_5': 'SMA_5_u'})
    sma10u = sma10.diff().rename(columns={'SMA_10': 'SMA_10_u'})
    sma50u = sma50.diff().rename(columns={'SMA_50': 'SMA_50_u'})

    CDL2CROWS = indicator(abstract.CDL2CROWS(df), 'CDL2CROWS')
    CDL3BLACKCROWS = indicator(abstract.CDL3BLACKCROWS(df), 'CDL3BLACKCROWS')
    CDL3INSIDE = indicator(abstract.CDL3INSIDE(df), 'CDL3INSIDE')
    CDL3LINESTRIKE = indicator(abstract.CDL3LINESTRIKE(df), 'CDL3LINESTRIKE')
    CDL3OUTSIDE = indicator(abstract.CDL3OUTSIDE(df), 'CDL3OUTSIDE')
    CDL3STARSINSOUTH = indicator(abstract.CDL3STARSINSOUTH(df),
                                 'CDL3STARSINSOUTH')
    CDL3WHITESOLDIERS = indicator(abstract.CDL3WHITESOLDIERS(df),
                                  'CDL3WHITESOLDIERS')
    CDLABANDONEDBABY = indicator(abstract.CDLABANDONEDBABY(df),
                                 'CDLABANDONEDBABY')
    CDLADVANCEBLOCK = indicator(abstract.CDLADVANCEBLOCK(df),
                                'CDLADVANCEBLOCK')
    CDLBELTHOLD = indicator(abstract.CDLBELTHOLD(df), 'CDLBELTHOLD')
    CDLBREAKAWAY = indicator(abstract.CDLBREAKAWAY(df), 'CDLBREAKAWAY')
    CDLCLOSINGMARUBOZU = indicator(abstract.CDLCLOSINGMARUBOZU(df),
                                   'CDLCLOSINGMARUBOZU')
    CDLCONCEALBABYSWALL = indicator(abstract.CDLCONCEALBABYSWALL(df),
                                    'CDLCONCEALBABYSWALL')
    CDLCOUNTERATTACK = indicator(abstract.CDLCOUNTERATTACK(df),
                                 'CDLCOUNTERATTACK')
    CDLDARKCLOUDCOVER = indicator(abstract.CDLDARKCLOUDCOVER(df),
                                  'CDLDARKCLOUDCOVER')
    CDLDOJI = indicator(abstract.CDLDOJI(df), 'CDLDOJI')
    CDLDOJISTAR = indicator(abstract.CDLDOJISTAR(df), 'CDLDOJISTAR')
    CDLDRAGONFLYDOJI = indicator(abstract.CDLDRAGONFLYDOJI(df),
                                 'CDLDRAGONFLYDOJI')
    CDLENGULFING = indicator(abstract.CDLENGULFING(df), 'CDLENGULFING')
    CDLEVENINGDOJISTAR = indicator(abstract.CDLEVENINGDOJISTAR(df),
                                   'CDLEVENINGDOJISTAR')
    CDLEVENINGSTAR = indicator(abstract.CDLEVENINGSTAR(df), 'CDLEVENINGSTAR')
    CDLGAPSIDESIDEWHITE = indicator(abstract.CDLGAPSIDESIDEWHITE(df),
                                    'CDLGAPSIDESIDEWHITE')
    CDLGRAVESTONEDOJI = indicator(abstract.CDLGRAVESTONEDOJI(df),
                                  'CDLGRAVESTONEDOJI')
    CDLHAMMER = indicator(abstract.CDLHAMMER(df), 'CDLHAMMER')
    CDLHANGINGMAN = indicator(abstract.CDLHANGINGMAN(df), 'CDLHANGINGMAN')
    CDLHARAMI = indicator(abstract.CDLHARAMI(df), 'CDLHARAMI')
    CDLHARAMICROSS = indicator(abstract.CDLHARAMICROSS(df), 'CDLHARAMICROSS')
    CDLHIGHWAVE = indicator(abstract.CDLHIGHWAVE(df), 'CDLHIGHWAVE')
    CDLHIKKAKE = indicator(abstract.CDLHIKKAKE(df), 'CDLHIKKAKE')
    CDLHIKKAKEMOD = indicator(abstract.CDLHIKKAKEMOD(df), 'CDLHIKKAKEMOD')
    CDLHOMINGPIGEON = indicator(abstract.CDLHOMINGPIGEON(df),
                                'CDLHOMINGPIGEON')
    CDLIDENTICAL3CROWS = indicator(abstract.CDLIDENTICAL3CROWS(df),
                                   'CDLIDENTICAL3CROWS')
    CDLINNECK = indicator(abstract.CDLINNECK(df), 'CDLINNECK')
    CDLINVERTEDHAMMER = indicator(abstract.CDLINVERTEDHAMMER(df),
                                  'CDLINVERTEDHAMMER')
    CDLKICKING = indicator(abstract.CDLKICKING(df), 'CDLKICKING')
    CDLKICKINGBYLENGTH = indicator(abstract.CDLKICKINGBYLENGTH(df),
                                   'CDLKICKINGBYLENGTH')
    CDLLADDERBOTTOM = indicator(abstract.CDLLADDERBOTTOM(df),
                                'CDLLADDERBOTTOM')
    CDLLONGLEGGEDDOJI = indicator(abstract.CDLLONGLEGGEDDOJI(df),
                                  'CDLLONGLEGGEDDOJI')
    CDLLONGLINE = indicator(abstract.CDLLONGLINE(df), 'CDLLONGLINE')
    CDLMARUBOZU = indicator(abstract.CDLMARUBOZU(df), 'CDLMARUBOZU')
    CDLMATCHINGLOW = indicator(abstract.CDLMATCHINGLOW(df), 'CDLMATCHINGLOW')
    CDLMATHOLD = indicator(abstract.CDLMATHOLD(df), 'CDLMATHOLD')
    CDLMORNINGDOJISTAR = indicator(abstract.CDLMORNINGDOJISTAR(df),
                                   'CDLMORNINGDOJISTAR')
    CDLMORNINGSTAR = indicator(abstract.CDLMORNINGSTAR(df), 'CDLMORNINGSTAR')
    CDLONNECK = indicator(abstract.CDLONNECK(df), 'CDLONNECK')
    CDLPIERCING = indicator(abstract.CDLPIERCING(df), 'CDLPIERCING')
    CDLRICKSHAWMAN = indicator(abstract.CDLRICKSHAWMAN(df), 'CDLRICKSHAWMAN')
    CDLRISEFALL3METHODS = indicator(abstract.CDLRISEFALL3METHODS(df),
                                    'CDLRISEFALL3METHODS')
    CDLSEPARATINGLINES = indicator(abstract.CDLSEPARATINGLINES(df),
                                   'CDLSEPARATINGLINES')
    CDLSHOOTINGSTAR = indicator(abstract.CDLSHOOTINGSTAR(df),
                                'CDLSHOOTINGSTAR')
    CDLSHORTLINE = indicator(abstract.CDLSHORTLINE(df), 'CDLSHORTLINE')
    CDLSPINNINGTOP = indicator(abstract.CDLSPINNINGTOP(df), 'CDLSPINNINGTOP')
    CDLSTALLEDPATTERN = indicator(abstract.CDLSTALLEDPATTERN(df),
                                  'CDLSTALLEDPATTERN')
    CDLSTICKSANDWICH = indicator(abstract.CDLSTICKSANDWICH(df),
                                 'CDLSTICKSANDWICH')
    CDLTAKURI = indicator(abstract.CDLTAKURI(df), 'CDLTAKURI')
    CDLTASUKIGAP = indicator(abstract.CDLTASUKIGAP(df), 'CDLTASUKIGAP')
    CDLTHRUSTING = indicator(abstract.CDLTHRUSTING(df), 'CDLTHRUSTING')
    CDLTRISTAR = indicator(abstract.CDLTRISTAR(df), 'CDLTRISTAR')
    CDLUNIQUE3RIVER = indicator(abstract.CDLUNIQUE3RIVER(df),
                                'CDLUNIQUE3RIVER')
    CDLUPSIDEGAP2CROWS = indicator(abstract.CDLUPSIDEGAP2CROWS(df),
                                   'CDLUPSIDEGAP2CROWS')
    CDLXSIDEGAP3METHODS = indicator(abstract.CDLXSIDEGAP3METHODS(df),
                                    'CDLXSIDEGAP3METHODS')

    macd, macdsignal, macdhist = talib.MACD(df['close'])
    macd = indicator(macd, 'macd')
    macdsignal = indicator(macdsignal, 'macdsignal')
    macdhist = indicator(macdhist, 'macdhist')

    df = pd.concat([
        df, sma5, sma5u, sma10, sma10u, sma50, sma50u, rsi, CDL2CROWS,
        CDL3BLACKCROWS, CDL3INSIDE, CDL3LINESTRIKE, CDL3OUTSIDE,
        CDL3STARSINSOUTH, CDL3WHITESOLDIERS, CDLABANDONEDBABY, CDLADVANCEBLOCK,
        CDLBELTHOLD, CDLBREAKAWAY, CDLCLOSINGMARUBOZU, CDLCONCEALBABYSWALL,
        CDLCOUNTERATTACK, CDLDARKCLOUDCOVER, CDLDOJI, CDLDOJISTAR,
        CDLDRAGONFLYDOJI, CDLENGULFING, CDLEVENINGDOJISTAR, CDLEVENINGSTAR,
        CDLGAPSIDESIDEWHITE, CDLGRAVESTONEDOJI, CDLHAMMER, CDLHANGINGMAN,
        CDLHARAMI, CDLHARAMICROSS, CDLHIGHWAVE, CDLHIKKAKE, CDLHIKKAKEMOD,
        CDLHOMINGPIGEON, CDLIDENTICAL3CROWS, CDLINNECK, CDLINVERTEDHAMMER,
        CDLKICKING, CDLKICKINGBYLENGTH, CDLLADDERBOTTOM, CDLLONGLEGGEDDOJI,
        CDLLONGLINE, CDLMARUBOZU, CDLMATCHINGLOW, CDLMATHOLD,
        CDLMORNINGDOJISTAR, CDLMORNINGSTAR, CDLONNECK, CDLPIERCING,
        CDLRICKSHAWMAN, CDLRISEFALL3METHODS, CDLSEPARATINGLINES,
        CDLSHOOTINGSTAR, CDLSHORTLINE, CDLSPINNINGTOP, CDLSTALLEDPATTERN,
        CDLSTICKSANDWICH, CDLTAKURI, CDLTASUKIGAP, CDLTHRUSTING, CDLTRISTAR,
        CDLUNIQUE3RIVER, CDLUPSIDEGAP2CROWS, CDLXSIDEGAP3METHODS, macd,
        macdsignal, macdhist
    ],
                   axis=1)

    df['next_open'] = df['open'].shift(-1)
    df = df[:-1]

    df = df[(df['SMA_50'] > 0)]
    df = df.reset_index(drop=True)

    return df
예제 #4
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    def populate_indicators(self, dataframe: DataFrame,
                            metadata: dict) -> 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.
        :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """

        # Momentum Indicators
        # ------------------------------------

        # ADX
        dataframe['adx'] = ta.ADX(dataframe)

        # Plus Directional Indicator / Movement
        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
        dataframe['plus_di'] = ta.PLUS_DI(dataframe)

        # # Minus Directional Indicator / Movement
        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
        dataframe['minus_di'] = ta.MINUS_DI(dataframe)

        # Aroon, Aroon Oscillator
        aroon = ta.AROON(dataframe)
        dataframe['aroonup'] = aroon['aroonup']
        dataframe['aroondown'] = aroon['aroondown']
        dataframe['aroonosc'] = ta.AROONOSC(dataframe)

        # Awesome Oscillator
        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)

        # Keltner Channel
        keltner = qtpylib.keltner_channel(dataframe)
        dataframe["kc_upperband"] = keltner["upper"]
        dataframe["kc_lowerband"] = keltner["lower"]
        dataframe["kc_middleband"] = keltner["mid"]
        dataframe["kc_percent"] = (
            (dataframe["close"] - dataframe["kc_lowerband"]) /
            (dataframe["kc_upperband"] - dataframe["kc_lowerband"]))
        dataframe["kc_width"] = (
            (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) /
            dataframe["kc_middleband"])

        # Ultimate Oscillator
        dataframe['uo'] = ta.ULTOSC(dataframe)

        # Commodity Channel Index: values [Oversold:-100, Overbought:100]
        dataframe['cci'] = ta.CCI(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'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

        # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)

        # Stochastic Slow
        stoch = ta.STOCH(dataframe)
        dataframe['slowd'] = stoch['slowd']
        dataframe['slowk'] = stoch['slowk']

        # Stochastic Fast
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']

        # Stochastic RSI
        stoch_rsi = ta.STOCHRSI(dataframe)
        dataframe['fastd_rsi'] = stoch_rsi['fastd']
        dataframe['fastk_rsi'] = stoch_rsi['fastk']

        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']

        # MFI
        dataframe['mfi'] = ta.MFI(dataframe)

        # # ROC
        dataframe['roc'] = ta.ROC(dataframe)

        # Overlap Studies
        # ------------------------------------

        # 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']
        dataframe["bb_percent"] = (
            (dataframe["close"] - dataframe["bb_lowerband"]) /
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]))
        dataframe["bb_width"] = (
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) /
            dataframe["bb_middleband"])

        # Bollinger Bands - Weighted (EMA based instead of SMA)
        weighted_bollinger = qtpylib.weighted_bollinger_bands(
            qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe["wbb_upperband"] = weighted_bollinger["upper"]
        dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
        dataframe["wbb_middleband"] = weighted_bollinger["mid"]
        dataframe["wbb_percent"] = (
            (dataframe["close"] - dataframe["wbb_lowerband"]) /
            (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]))
        dataframe["wbb_width"] = (
            (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
            dataframe["wbb_middleband"])

        # EMA - Exponential Moving Average
        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
        dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)

        # SMA - Simple Moving Average
        dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
        dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
        dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
        dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
        dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
        dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)

        # Parabolic SAR
        dataframe['sar'] = ta.SAR(dataframe)

        # 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
        # ------------------------------------
        # Heikin Ashi Strategy
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['ha_high'] = heikinashi['high']
        dataframe['ha_low'] = heikinashi['low']

        # Retrieve best bid and best ask from the orderbook
        # ------------------------------------
        """
        # first check if dataprovider is available
        if self.dp:
            if self.dp.runmode in ('live', 'dry_run'):
                ob = self.dp.orderbook(metadata['pair'], 1)
                dataframe['best_bid'] = ob['bids'][0][0]
                dataframe['best_ask'] = ob['asks'][0][0]
        """

        return dataframe