Example #1
0
def add_volume_indicators(data: pd.DataFrame) -> pd.DataFrame:
    """Adds the volume indicators.

    Parameters
    ----------
    data : pd.DataFrame
        A dataframe with daily stock values. Must include: open, high,
        low, close and volume. It should also be sorted in a descending
        manner.

    Returns
    -------
    pd.DataFrame
        The input dataframe with the indicators added.
    """
    chaikin = ChaikinMoneyFlowIndicator(data['high'], data['low'],
                                        data['close'], data['volume'])
    mfi = MFIIndicator(data['high'], data['low'], data['close'],
                       data['volume'])
    obv = OnBalanceVolumeIndicator(data['close'], data['volume'])

    data.loc[:, 'chaikin'] = chaikin.chaikin_money_flow()
    data.loc[:, 'mfi'] = mfi.money_flow_index()
    data.loc[:, 'obv'] = obv.on_balance_volume()

    return data
Example #2
0
 def OBV(self, df):
     '''
 data range(0,inf)
 '''
     df_OBV = df.copy()
     obv = OnBalanceVolumeIndicator(df['Close'], df['Volume'])
     df['OBV'] = obv.on_balance_volume()
     return df
Example #3
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 def test_obv2(self):
     target = 'OBV'
     result = OnBalanceVolumeIndicator(close=self._df['Close'],
                                       volume=self._df['Volume'],
                                       fillna=False).on_balance_volume()
     pd.testing.assert_series_equal(self._df[target].tail(),
                                    result.tail(),
                                    check_names=False)
def get_indicators(df):
    """
        Add set of technical indicators to the dataframe, return original data frame with new features
    """
    feature_df = df.copy()
    feature_df['RSI'] = RSIIndicator(close=df[CLOSE]).rsi()
    feature_df['Stochastic'] = StochasticOscillator(high=df[HIGH],
                                                    low=df[LOW],
                                                    close=df[CLOSE]).stoch()
    feature_df['Stochastic_signal'] = StochasticOscillator(
        high=df[HIGH], low=df[LOW], close=df[CLOSE]).stoch_signal()
    feature_df['ADI'] = AccDistIndexIndicator(
        high=df[HIGH], low=df[LOW], close=df[CLOSE],
        volume=df[VOLUME]).acc_dist_index()
    feature_df['OBV'] = OnBalanceVolumeIndicator(
        close=df[CLOSE], volume=df[VOLUME]).on_balance_volume()
    feature_df['ATR'] = AverageTrueRange(high=df[HIGH],
                                         low=df[LOW],
                                         close=df[CLOSE]).average_true_range()
    feature_df['ADX'] = ADXIndicator(high=df[HIGH],
                                     low=df[LOW],
                                     close=df[CLOSE]).adx()
    feature_df['ADX_pos'] = ADXIndicator(high=df[HIGH],
                                         low=df[LOW],
                                         close=df[CLOSE]).adx_pos()
    feature_df['ADX_neg'] = ADXIndicator(high=df[HIGH],
                                         low=df[LOW],
                                         close=df[CLOSE]).adx_neg()
    feature_df['MACD'] = MACD(close=df[CLOSE]).macd()
    feature_df['MACD_diff'] = MACD(close=df[CLOSE]).macd_diff()
    feature_df['MACD_signal'] = MACD(close=df[CLOSE]).macd_signal()
    return feature_df
Example #5
0
def add_volume_ta(df: pd.DataFrame, high: str, low: str, close: str, volume: str,
                  fillna: bool = False, colprefix: str = "") -> pd.DataFrame:
    """Add volume technical analysis features to dataframe.

    Args:
        df (pandas.core.frame.DataFrame): Dataframe base.
        high (str): Name of 'high' column.
        low (str): Name of 'low' column.
        close (str): Name of 'close' column.
        volume (str): Name of 'volume' column.
        fillna(bool): if True, fill nan values.
        colprefix(str): Prefix column names inserted

    Returns:
        pandas.core.frame.DataFrame: Dataframe with new features.
    """

    # Accumulation Distribution Index
    df[f'{colprefix}volume_adi'] = AccDistIndexIndicator(
        high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).acc_dist_index()

    # On Balance Volume
    df[f'{colprefix}volume_obv'] = OnBalanceVolumeIndicator(
        close=df[close], volume=df[volume], fillna=fillna).on_balance_volume()

    # Chaikin Money Flow
    df[f'{colprefix}volume_cmf'] = ChaikinMoneyFlowIndicator(
        high=df[high], low=df[low], close=df[close], volume=df[volume], fillna=fillna).chaikin_money_flow()

    # Force Index
    df[f'{colprefix}volume_fi'] = ForceIndexIndicator(
        close=df[close], volume=df[volume], n=13, fillna=fillna).force_index()

    # Money Flow Indicator
    df[f'{colprefix}volume_mfi'] = MFIIndicator(
        high=df[high], low=df[low], close=df[close], volume=df[volume], n=14, fillna=fillna).money_flow_index()

    # Ease of Movement
    indicator = EaseOfMovementIndicator(high=df[high], low=df[low], volume=df[volume], n=14, fillna=fillna)
    df[f'{colprefix}volume_em'] = indicator.ease_of_movement()
    df[f'{colprefix}volume_sma_em'] = indicator.sma_ease_of_movement()

    # Volume Price Trend
    df[f'{colprefix}volume_vpt'] = VolumePriceTrendIndicator(
        close=df[close], volume=df[volume], fillna=fillna).volume_price_trend()

    # Negative Volume Index
    df[f'{colprefix}volume_nvi'] = NegativeVolumeIndexIndicator(
        close=df[close], volume=df[volume], fillna=fillna).negative_volume_index()

    # Volume Weighted Average Price
    df[f'{colprefix}volume_vwap'] = VolumeWeightedAveragePrice(
        high=df[high], low=df[low], close=df[close], volume=df[volume], n=14, fillna=fillna
    ).volume_weighted_average_price()

    return df
    def __init__(self, symbols):

        # data = json.loads(symbols)
        # df_stock = pd.json_normalize(symbols)
        # df_stock = pd.read_csv(fn,names = ['sym']).drop_duplicates()
        df_stock = pd.DataFrame(symbols)
        ls_stock = df_stock['sym'].to_list()

        df_stock = df_stock.reset_index()

        df_stock.columns = ['sort', 'sym']

        df_stock.head()

        # In[3]:

        start = dt.date.today() + relativedelta(days=-150)
        end = dt.date.today() + relativedelta(days=-0)

        ls_tickers = ls_stock

        ls_df = []
        for ticker in ls_tickers:
            try:
                df = web.DataReader(ticker, 'yahoo', start, end)
            except Exception as e:
                print(str(e))
                continue
            df['sym'] = ticker
            ls_df.append(df.copy())

        df_price = pd.concat(ls_df).reset_index()
        df_price.columns = [
            'dte', 'hgh', 'low', 'opn', 'cls', 'vol', 'cls_adj', 'sym'
        ]
        df_price.sort_values(['sym', 'dte'], inplace=True)

        df_price = df_price[['dte', 'sym', 'hgh', 'low', 'cls', 'vol']].copy()

        df_price['curr'] = end

        df_price['curr'] = pd.to_datetime(df_price['curr'])
        df_price['dte'] = pd.to_datetime(df_price['dte'])

        df_price['ndays'] = (df_price['curr'] - df_price['dte']).dt.days

        df_price['ndays'] = df_price.groupby(['sym'])['ndays'].rank()

        df_price[df_price['sym'] == 'SPY'].head()

        # In[4]:

        ls_df = []
        ls_tickers = ls_stock

        for ticker in ls_tickers:

            #df = dropna(df_price[df_price['sym']==ticker])
            df = df_price[df_price['sym'] == ticker].copy()

            indicator_bb = BollingerBands(close=df['cls'],
                                          window=20,
                                          window_dev=2)
            indicator_macd = MACD(close=df['cls'],
                                  window_fast=12,
                                  window_slow=26,
                                  window_sign=9)
            indicator_rsi14 = RSIIndicator(close=df['cls'], window=14)
            indicator_cci20 = cci(high=df['hgh'],
                                  low=df['low'],
                                  close=df['cls'],
                                  window=20,
                                  constant=0.015)
            indicator_obv = OnBalanceVolumeIndicator(close=df['cls'],
                                                     volume=df['vol'],
                                                     fillna=True)

            indicator_vol_sma20 = SMAIndicator(close=df['vol'], window=20)

            indicator_ema03 = EMAIndicator(close=df['cls'], window=3)
            indicator_ema05 = EMAIndicator(close=df['cls'], window=5)
            indicator_ema08 = EMAIndicator(close=df['cls'], window=8)
            indicator_ema10 = EMAIndicator(close=df['cls'], window=10)
            indicator_ema12 = EMAIndicator(close=df['cls'], window=12)
            indicator_ema15 = EMAIndicator(close=df['cls'], window=15)
            indicator_ema30 = EMAIndicator(close=df['cls'], window=30)
            indicator_ema35 = EMAIndicator(close=df['cls'], window=35)
            indicator_ema40 = EMAIndicator(close=df['cls'], window=40)
            indicator_ema45 = EMAIndicator(close=df['cls'], window=45)
            indicator_ema50 = EMAIndicator(close=df['cls'], window=50)
            indicator_ema60 = EMAIndicator(close=df['cls'], window=60)

            # Add Bollinger Band high indicator
            df['bb_bbhi'] = indicator_bb.bollinger_hband_indicator()

            # Add Bollinger Band low indicator
            df['bb_bbli'] = indicator_bb.bollinger_lband_indicator()

            #df['macd'] = indicator_macd.macd()
            df['macd'] = indicator_macd.macd_diff()
            #df['macd_signal'] = indicator_macd.macd_signal()

            df['obv'] = indicator_obv.on_balance_volume()

            df['vol_sma20'] = indicator_vol_sma20.sma_indicator()

            df['ema03'] = indicator_ema03.ema_indicator()
            df['ema05'] = indicator_ema05.ema_indicator()
            df['ema08'] = indicator_ema08.ema_indicator()
            df['ema10'] = indicator_ema10.ema_indicator()
            df['ema12'] = indicator_ema12.ema_indicator()
            df['ema15'] = indicator_ema15.ema_indicator()
            df['ema30'] = indicator_ema30.ema_indicator()
            df['ema35'] = indicator_ema35.ema_indicator()
            df['ema40'] = indicator_ema40.ema_indicator()
            df['ema45'] = indicator_ema45.ema_indicator()
            df['ema50'] = indicator_ema50.ema_indicator()
            df['ema60'] = indicator_ema60.ema_indicator()

            df['rsi14'] = indicator_rsi14.rsi()
            df['cci20'] = indicator_cci20

            ls_df.append(df.copy())

        df = pd.concat(ls_df)

        df['score_vol_sma20'] = df[['vol',
                                    'vol_sma20']].apply(lambda x: x[0] / x[1],
                                                        axis=1)

        df['emash_min'] = df[[
            'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15'
        ]].min(axis=1)
        df['emash_max'] = df[[
            'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15'
        ]].max(axis=1)
        df['emash_avg'] = df[[
            'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15'
        ]].mean(axis=1)

        #df['score_short'] = df[['cls','emash_min','emash_max','emash_min']].apply(lambda x: 100 * (x[0]-x[1])/(x[2]-x[3]),axis=1)

        df['emalg_min'] = df[[
            'ema30', 'ema35', 'ema40', 'ema45', 'ema50', 'ema60'
        ]].min(axis=1)
        df['emalg_max'] = df[[
            'ema30', 'ema35', 'ema40', 'ema45', 'ema50', 'ema60'
        ]].max(axis=1)
        df['emalg_avg'] = df[[
            'ema30', 'ema35', 'ema40', 'ema45', 'ema50', 'ema60'
        ]].mean(axis=1)

        #df['score_long'] = df[['cls','emalg_min','emalg_max','emalg_min']].apply(lambda x: 100 * (x[0]-x[1])/(x[2]-x[3]),axis=1)

        df['ema_min'] = df[[
            'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15', 'ema30',
            'ema35', 'ema40', 'ema45', 'ema50', 'ema60'
        ]].min(axis=1)
        df['ema_max'] = df[[
            'ema03', 'ema05', 'ema08', 'ema10', 'ema12', 'ema15', 'ema30',
            'ema35', 'ema40', 'ema45', 'ema50', 'ema60'
        ]].max(axis=1)

        df['score_ovlp_ema'] = df[[
            'emash_min', 'emalg_max', 'ema_max', 'ema_min'
        ]].apply(lambda x: 100 * (x[0] - x[1]) / (x[2] - x[3]), axis=1)

        df = pd.merge(df_stock, df, on=['sym'],
                      how='inner').sort_values(['sort', 'ndays'])

        decimals = pd.Series([1, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0],
                             index=[
                                 'cls', 'ndays', 'vol', 'score_vol_sma20',
                                 'bb_bbhi', 'bb_bbli', 'macd', 'obv', 'rsi14',
                                 'cci20', 'score_ovlp_ema'
                             ])

        cols = [
            'ndays', 'dte', 'sort', 'sym', 'cls', 'vol', 'score_vol_sma20',
            'bb_bbhi', 'bb_bbli', 'macd', 'obv', 'rsi14', 'cci20',
            'score_ovlp_ema'
        ]

        df = df[df['ndays'] <= 10][cols].round(decimals).copy()

        print(df['score_ovlp_ema'].min(), df['score_ovlp_ema'].max())

        df[df['sym'] == 'QQQ'].head(50)
        self.df = df
Example #7
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 def setUpClass(cls):
     cls._df = pd.read_csv(cls._filename, sep=",")
     cls._params = dict(close=cls._df["Close"],
                        volume=cls._df["Volume"],
                        fillna=False)
     cls._indicator = OnBalanceVolumeIndicator(**cls._params)
Example #8
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    #df = dropna(df_price[df_price['sym']==ticker])
    df = df_price[df_price['sym'] == ticker].copy()

    indicator_bb = BollingerBands(close=df['cls'], window=20, window_dev=2)
    indicator_macd = MACD(close=df['cls'],
                          window_fast=12,
                          window_slow=26,
                          window_sign=9)
    indicator_rsi14 = RSIIndicator(close=df['cls'], window=14)
    indicator_cci20 = cci(high=df['hgh'],
                          low=df['low'],
                          close=df['cls'],
                          window=20,
                          constant=0.015)
    indicator_obv = OnBalanceVolumeIndicator(close=df['cls'],
                                             volume=df['vol'],
                                             fillna=True)

    indicator_vol_sma20 = SMAIndicator(close=df['vol'], window=20)

    indicator_ema03 = EMAIndicator(close=df['cls'], window=3)
    indicator_ema05 = EMAIndicator(close=df['cls'], window=5)
    indicator_ema08 = EMAIndicator(close=df['cls'], window=8)
    indicator_ema10 = EMAIndicator(close=df['cls'], window=10)
    indicator_ema12 = EMAIndicator(close=df['cls'], window=12)
    indicator_ema15 = EMAIndicator(close=df['cls'], window=15)
    indicator_ema30 = EMAIndicator(close=df['cls'], window=30)
    indicator_ema35 = EMAIndicator(close=df['cls'], window=35)
    indicator_ema40 = EMAIndicator(close=df['cls'], window=40)
    indicator_ema45 = EMAIndicator(close=df['cls'], window=45)
    indicator_ema50 = EMAIndicator(close=df['cls'], window=50)