示例#1
0
def add_indicators(data: pd.DataFrame) -> pd.DataFrame:
    """
    This method creates technical indicators, based on the OHLC and volume bars
    :param data: pandas DataFrame, containing open, high, low and close and
                 optional volume columns
    :return: DataFrame with added technical indicators
    """
    assert 'open' in data.columns, "open column not present or with different name"
    assert 'high' in data.columns, "high column not present or with different name"
    assert 'low' in data.columns, "low column not present or with different name"
    assert 'close' in data.columns, "close column not present or with different name"

    try:
        data['RSI'] = ta.rsi(data["close"])
        data['TSI'] = ta.tsi(data["close"])
        data['UO'] = ta.uo(data["high"], data["low"], data["close"])
        data['AO'] = ta.ao(data["high"], data["low"])
        data['MACD_diff'] = ta.macd_diff(data["close"])
        data['Vortex_pos'] = ta.vortex_indicator_pos(data["high"], data["low"], data["close"])
        data['Vortex_neg'] = ta.vortex_indicator_neg(data["high"], data["low"], data["close"])
        data['Vortex_diff'] = abs(data['Vortex_pos'] - data['Vortex_neg'])
        data['Trix'] = ta.trix(data["close"])
        data['Mass_index'] = ta.mass_index(data["high"], data["low"])
        data['CCI'] = ta.cci(data["high"], data["low"], data["close"])
        data['DPO'] = ta.dpo(data["close"])
        data['KST'] = ta.kst(data["close"])
        data['KST_sig'] = ta.kst_sig(data["close"])
        data['KST_diff'] = (data['KST'] - data['KST_sig'])
        data['Aroon_up'] = ta.aroon_up(data["close"])
        data['Aroon_down'] = ta.aroon_down(data["close"])
        data['Aroon_ind'] = (data['Aroon_up'] - data['Aroon_down'])
        data['BBH'] = ta.bollinger_hband(data["close"])
        data['BBL'] = ta.bollinger_lband(data["close"])
        data['BBM'] = ta.bollinger_mavg(data["close"])
        data['BBHI'] = ta.bollinger_hband_indicator(data["close"])
        data['BBLI'] = ta.bollinger_lband_indicator(data["close"])
        data['KCHI'] = ta.keltner_channel_hband_indicator(data["high"], data["low"], data["close"])
        data['KCLI'] = ta.keltner_channel_lband_indicator(data["high"], data["low"], data["close"])
        data['DCHI'] = ta.donchian_channel_hband_indicator(data["close"])
        data['DCLI'] = ta.donchian_channel_lband_indicator(data["close"])
        data['DR'] = ta.daily_return(data["close"])
        data['DLR'] = ta.daily_log_return(data["close"])

        if 'volume' in data.columns:
            data['MFI'] = ta.money_flow_index(data["high"], data["low"], data["close"], data["volume"])
            data['ADI'] = ta.acc_dist_index(data["high"], data["low"], data["close"], data["volume"])
            data['OBV'] = ta.on_balance_volume(data["close"], data["volume"])
            data['CMF'] = ta.chaikin_money_flow(data["high"], data["low"], data["close"], data["volume"])
            data['FI'] = ta.force_index(data["close"], data["volume"])
            data['EM'] = ta.ease_of_movement(data["high"], data["low"], data["close"], data["volume"])
            data['VPT'] = ta.volume_price_trend(data["close"], data["volume"])
            data['NVI'] = ta.negative_volume_index(data["close"], data["volume"])

        data.fillna(method='bfill', inplace=True)

        return data

    except (AssertionError, Exception) as error:
        raise IndicatorsError(error)
        LOGGER.error(error)
示例#2
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 def kst():
     kst = ta.kst(close,
                  r1=10,
                  r2=15,
                  r3=20,
                  r4=30,
                  n1=10,
                  n2=10,
                  n3=10,
                  n4=15,
                  fillna=False)
     kst_sig = ta.kst_sig(close,
                          r1=10,
                          r2=15,
                          r3=20,
                          r4=30,
                          n1=10,
                          n2=10,
                          n3=10,
                          n4=15,
                          nsig=9,
                          fillna=False)
     if kst[-1] < kst_sig[-1]:
         trn_kst_status = "Sell"
     elif kst[-1] > kst_sig[-1]:
         trn_kst_status = "Buy"
     else:
         trn_kst_status = "Hold"
     return trn_kst_status
示例#3
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def enrich_sampleset(df):
    df['OBV'] = ta.on_balance_volume(df['close'], df['volume'])
    df['ADX'] = ta.adx(df['high'], df['low'], df['close'], n=14)
    df['VPT'] = ta.volume_price_trend(df['close'], df['volume'])
    df['ATR'] = ta.average_true_range(df['high'], df['low'], df['close'], n=14)
    df['MFI'] = ta.money_flow_index(df['high'], df['low'], df['close'],
                                    df['volume'])
    df['KST'] = ta.kst(df['close'])
    return ta.utils.dropna(df)
示例#4
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def add_indicators(df):
    df['RSI'] = ta.rsi(df["Close"])
    df['MFI'] = ta.money_flow_index(df["High"], df["Low"], df["Close"],
                                    df["Volume"])
    df['TSI'] = ta.tsi(df["Close"])
    df['UO'] = ta.uo(df["High"], df["Low"], df["Close"])
    df['AO'] = ta.ao(df["High"], df["Low"])

    df['MACD_diff'] = ta.macd_diff(df["Close"])
    df['Vortex_pos'] = ta.vortex_indicator_pos(df["High"], df["Low"],
                                               df["Close"])
    df['Vortex_neg'] = ta.vortex_indicator_neg(df["High"], df["Low"],
                                               df["Close"])
    df['Vortex_diff'] = abs(df['Vortex_pos'] - df['Vortex_neg'])
    df['Trix'] = ta.trix(df["Close"])
    df['Mass_index'] = ta.mass_index(df["High"], df["Low"])
    df['CCI'] = ta.cci(df["High"], df["Low"], df["Close"])
    df['DPO'] = ta.dpo(df["Close"])
    df['KST'] = ta.kst(df["Close"])
    df['KST_sig'] = ta.kst_sig(df["Close"])
    df['KST_diff'] = (df['KST'] - df['KST_sig'])
    df['Aroon_up'] = ta.aroon_up(df["Close"])
    df['Aroon_down'] = ta.aroon_down(df["Close"])
    df['Aroon_ind'] = (df['Aroon_up'] - df['Aroon_down'])

    df['BBH'] = ta.bollinger_hband(df["Close"])
    df['BBL'] = ta.bollinger_lband(df["Close"])
    df['BBM'] = ta.bollinger_mavg(df["Close"])
    df['BBHI'] = ta.bollinger_hband_indicator(df["Close"])
    df['BBLI'] = ta.bollinger_lband_indicator(df["Close"])
    df['KCHI'] = ta.keltner_channel_hband_indicator(df["High"], df["Low"],
                                                    df["Close"])
    df['KCLI'] = ta.keltner_channel_lband_indicator(df["High"], df["Low"],
                                                    df["Close"])
    df['DCHI'] = ta.donchian_channel_hband_indicator(df["Close"])
    df['DCLI'] = ta.donchian_channel_lband_indicator(df["Close"])

    df['ADI'] = ta.acc_dist_index(df["High"], df["Low"], df["Close"],
                                  df["Volume"])
    df['OBV'] = ta.on_balance_volume(df["Close"], df["Volume"])
    df['CMF'] = ta.chaikin_money_flow(df["High"], df["Low"], df["Close"],
                                      df["Volume"])
    df['FI'] = ta.force_index(df["Close"], df["Volume"])
    df['EM'] = ta.ease_of_movement(df["High"], df["Low"], df["Close"],
                                   df["Volume"])
    df['VPT'] = ta.volume_price_trend(df["Close"], df["Volume"])
    df['NVI'] = ta.negative_volume_index(df["Close"], df["Volume"])

    df['DR'] = ta.daily_return(df["Close"])
    df['DLR'] = ta.daily_log_return(df["Close"])

    df.fillna(method='bfill', inplace=True)

    return df
def add_candle_indicators(df, l, ck, hk, lk, vk):
    df[l + 'rsi'] = ta.rsi(df[ck])
    df[l + 'mfi'] = ta.money_flow_index(df[hk], df[lk], df[ck], df[vk])
    df[l + 'tsi'] = ta.tsi(df[ck])
    df[l + 'uo'] = ta.uo(df[hk], df[lk], df[ck])
    df[l + 'ao'] = ta.ao(df[hk], df[lk])
    df[l + 'macd_diff'] = ta.macd_diff(df[ck])
    df[l + 'vortex_pos'] = ta.vortex_indicator_pos(df[hk], df[lk], df[ck])
    df[l + 'vortex_neg'] = ta.vortex_indicator_neg(df[hk], df[lk], df[ck])
    df[l + 'vortex_diff'] = abs(df[l + 'vortex_pos'] - df[l + 'vortex_neg'])
    df[l + 'trix'] = ta.trix(df[ck])
    df[l + 'mass_index'] = ta.mass_index(df[hk], df[lk])
    df[l + 'cci'] = ta.cci(df[hk], df[lk], df[ck])
    df[l + 'dpo'] = ta.dpo(df[ck])
    df[l + 'kst'] = ta.kst(df[ck])
    df[l + 'kst_sig'] = ta.kst_sig(df[ck])
    df[l + 'kst_diff'] = (df[l + 'kst'] - df[l + 'kst_sig'])
    df[l + 'aroon_up'] = ta.aroon_up(df[ck])
    df[l + 'aroon_down'] = ta.aroon_down(df[ck])
    df[l + 'aroon_ind'] = (df[l + 'aroon_up'] - df[l + 'aroon_down'])
    df[l + 'bbh'] = ta.bollinger_hband(df[ck])
    df[l + 'bbl'] = ta.bollinger_lband(df[ck])
    df[l + 'bbm'] = ta.bollinger_mavg(df[ck])
    df[l + 'bbhi'] = ta.bollinger_hband_indicator(df[ck])
    df[l + 'bbli'] = ta.bollinger_lband_indicator(df[ck])
    df[l + 'kchi'] = ta.keltner_channel_hband_indicator(df[hk], df[lk], df[ck])
    df[l + 'kcli'] = ta.keltner_channel_lband_indicator(df[hk], df[lk], df[ck])
    df[l + 'dchi'] = ta.donchian_channel_hband_indicator(df[ck])
    df[l + 'dcli'] = ta.donchian_channel_lband_indicator(df[ck])
    df[l + 'adi'] = ta.acc_dist_index(df[hk], df[lk], df[ck], df[vk])
    df[l + 'obv'] = ta.on_balance_volume(df[ck], df[vk])
    df[l + 'cmf'] = ta.chaikin_money_flow(df[hk], df[lk], df[ck], df[vk])
    df[l + 'fi'] = ta.force_index(df[ck], df[vk])
    df[l + 'em'] = ta.ease_of_movement(df[hk], df[lk], df[ck], df[vk])
    df[l + 'vpt'] = ta.volume_price_trend(df[ck], df[vk])
    df[l + 'nvi'] = ta.negative_volume_index(df[ck], df[vk])
    df[l + 'dr'] = ta.daily_return(df[ck])
    df[l + 'dlr'] = ta.daily_log_return(df[ck])
    df[l + 'ma50'] = df[ck].rolling(window=50).mean()
    df[l + 'ma100'] = df[ck].rolling(window=100).mean()
    df[l + '26ema'] = df[[ck]].ewm(span=26).mean()
    df[l + '12ema'] = df[[ck]].ewm(span=12).mean()
    df[l + 'macd'] = (df[l + '12ema'] - df[l + '26ema'])
    df[l + '100sd'] = df[[ck]].rolling(100).std()
    df[l + 'upper_band'] = df[l + 'ma100'] + (df[l + '100sd'] * 2)
    df[l + 'lower_band'] = df[l + 'ma100'] - (df[l + '100sd'] * 2)
    df[l + 'ema'] = df[ck].ewm(com=0.5).mean()
    df[l + 'momentum'] = df[ck] - 1
    return df
def process_data(data):
    data['BB_5'] = ta.bollinger_mavg(
        data['CLOSE'], 5)  #bollinger_moving average 5 trading periods
    data['BB_10'] = ta.bollinger_mavg(
        data['CLOSE'], 10)  #bollinger_moving average 10 trading periods
    data['BB_20'] = ta.bollinger_mavg(
        data['CLOSE'], 20)  # bollinger_moving average 20 periods
    data['ADX'] = ta.adx(data['HIGH'], data['LOW'], data['CLOSE'],
                         14)  #Average Directional Index
    data['ATR'] = ta.average_true_range(data['HIGH'], data['LOW'],
                                        data['CLOSE'], 14)  #Average True Range
    data['CCI'] = ta.cci(data['HIGH'], data['LOW'], data['CLOSE'],
                         14)  #Commodity Channel Index
    data['DCH'] = ta.donchian_channel_hband(
        data['CLOSE'])  #Donchian Channel High Band
    data['DCL'] = ta.donchian_channel_lband(
        data['CLOSE'])  #Donchian Channel Low Band
    data['DPO'] = ta.dpo(data['CLOSE'])  #Detrend Price Oscilator
    data['EMAf'] = ta.ema_fast(
        data['CLOSE'])  #Expornential Moving Average fast
    data['EMAs'] = ta.ema_slow(
        data['CLOSE'])  #Expornential Moving Average slow
    data['FI'] = ta.force_index(
        data['CLOSE'],
        data['VOLUME'])  # Force Index(reveals the value of a trend)
    data['ICHa'] = ta.ichimoku_a(data['HIGH'], data['LOW'])  #Ichimoku A
    data['ICHb'] = ta.ichimoku_b(data['HIGH'], data['LOW'])  #Ichimoku B
    data['KC'] = ta.keltner_channel_central(
        data['HIGH'], data['LOW'], data['CLOSE'])  #Keltner channel(KC) Central
    data['KST'] = ta.kst(
        data['CLOSE']
    )  #KST Oscillator (KST) identify major stock market cycle junctures
    data['MACD'] = ta.macd(
        data['CLOSE'])  # Moving Average convergence divergence
    data['OBV'] = ta.on_balance_volume_mean(
        data['CLOSE'], data['VOLUME'])  # on_balance_volume_mean
    data['RSI'] = ta.rsi(data['CLOSE'])  # Relative Strength Index (RSI)
    data['TRIX'] = ta.trix(
        data['CLOSE']
    )  #Shows the percent rate of change of a triple exponentially smoothed moving average
    data['TSI'] = ta.tsi(data['CLOSE'])  #True strength index (TSI)
    data['ROC1'] = (data['CLOSE'] - data['OPEN']) / data['OPEN']
    data['RET'] = data['CLOSE'].pct_change()
    data['y'] = np.where(data['OPEN'] <= data['CLOSE'], 1, -1)
    data = data.dropna()
    return data
示例#7
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def add_indicators(df):
    df['RSI'] = ta.rsi(df["Close"])
    df['TSI'] = ta.tsi(df["Close"])
    df['UO'] = ta.uo(df["High"], df["Low"], df["Close"])
    df['AO'] = ta.ao(df["High"], df["Low"])

    df['MACD_diff'] = ta.macd_diff(df["Close"])
    df['Vortex_pos'] = ta.vortex_indicator_pos(df["High"], df["Low"],
                                               df["Close"])
    df['Vortex_neg'] = ta.vortex_indicator_neg(df["High"], df["Low"],
                                               df["Close"])
    df['Vortex_diff'] = abs(df['Vortex_pos'] - df['Vortex_neg'])
    df['Trix'] = ta.trix(df["Close"])
    df['Mass_index'] = ta.mass_index(df["High"], df["Low"])
    df['CCI'] = ta.cci(df["High"], df["Low"], df["Close"])
    df['DPO'] = ta.dpo(df["Close"])
    df['KST'] = ta.kst(df["Close"])
    df['KST_sig'] = ta.kst_sig(df["Close"])
    df['KST_diff'] = (df['KST'] - df['KST_sig'])
    df['Aroon_up'] = ta.aroon_up(df["Close"])
    df['Aroon_down'] = ta.aroon_down(df["Close"])
    df['Aroon_ind'] = (df['Aroon_up'] - df['Aroon_down'])

    df['BBH'] = ta.bollinger_hband(df["Close"])
    df['BBL'] = ta.bollinger_lband(df["Close"])
    df['BBM'] = ta.bollinger_mavg(df["Close"])
    df['BBHI'] = ta.bollinger_hband_indicator(df["Close"])
    df['BBLI'] = ta.bollinger_lband_indicator(df["Close"])
    df['KCHI'] = ta.keltner_channel_hband_indicator(df["High"], df["Low"],
                                                    df["Close"])
    df['KCLI'] = ta.keltner_channel_lband_indicator(df["High"], df["Low"],
                                                    df["Close"])
    df['DCHI'] = ta.donchian_channel_hband_indicator(df["Close"])
    df['DCLI'] = ta.donchian_channel_lband_indicator(df["Close"])

    df['DR'] = ta.daily_return(df["Close"])
    df['DLR'] = ta.daily_log_return(df["Close"])

    df.fillna(method='bfill', inplace=True)

    return df
示例#8
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dd['s03_return_3']=dd.s00_return_3.shift(3)
dd['s05_return_1']=dd.s00_return_1.shift(5)
dd['s05_return_3']=dd.s00_return_3.shift(5)


dd['s00_rsi_14']=ta.rsi(dd.Close, 14)
dd['s00_rsi_7']=ta.rsi(dd.Close, 7)
dd['s00_willR_14']=ta.wr(dd.High,dd.Low,dd.Close,14)
dd['s00_willR_7']=ta.wr(dd.High,dd.Low,dd.Close,7)
dd['s00_stoch_sig_14_3']=ta.stoch_signal(dd.High,dd.Low,dd.Close,14,3)
dd['s00_stoch_sig_7_3']=ta.stoch_signal(dd.High,dd.Low,dd.Close,7,3)
dd['s00_cci_20_0015']=ta.cci(dd.High,dd.Low,dd.Close,20,0.015)
dd['s00_cci_20_005']=ta.cci(dd.High,dd.Low,dd.Close,20,0.05)
dd['s00_macd_12_26_9']=ta.macd_diff(dd.Close,12,26,9)
dd['s00_macd_7_14_9']=ta.macd_diff(dd.Close,7,14, 9)
dd['s00_kst_9']=ta.kst(dd.Close)-ta.kst_sig(dd.Close)

dd['s01_rsi_14']=dd.s00_rsi_14.shift(+1)
dd['s01_rsi_7']=dd.s00_rsi_7.shift(+1)
dd['s01_willR_14']=dd.s00_willR_14.shift(+1)
dd['s01_willR_7']=dd.s00_willR_7.shift(+1)
dd['s01_stoch_sig_14_3']=dd.s00_stoch_sig_14_3.shift(+1)
dd['s01_stoch_sig_7_3']=dd.s00_stoch_sig_7_3.shift(+1)
dd['s01_cci_20_0015']=dd.s00_cci_20_0015.shift(+1)
dd['s01_cci_20_005']=dd.s00_cci_20_005.shift(+1)
dd['s01_macd_12_26_9']=dd.s00_macd_12_26_9.shift(+1)
dd['s01_macd_7_14_9']=dd.s00_macd_7_14_9.shift(+1)
dd['s01_kst_9']=dd.s00_kst_9.shift(+1)

dd['s03_rsi_14']=dd.s00_rsi_14.shift(+3)
dd['s03_rsi_7']=dd.s00_rsi_7.shift(+3)
示例#9
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ta_df['MACD_signal'] = ta.macd_signal(df["Close"])
ta_df['MACD_diff'] = ta.macd_diff(df["Close"])
ta_df['EMA_fast'] = ta.ema_indicator(df["Close"])
ta_df['EMA_slow'] = ta.ema_indicator(df["Close"])
ta_df['Vortex_pos'] = ta.vortex_indicator_pos(
    df["High"], df["Low"], df["Close"])
ta_df['Vortex_neg'] = ta.vortex_indicator_neg(
    df["High"], df["Low"], df["Close"])
ta_df['Vortex_diff'] = abs(
    ta_df['Vortex_pos'] -
    ta_df['Vortex_neg'])
ta_df['Trix'] = ta.trix(df["Close"])
ta_df['Mass_index'] = ta.mass_index(df["High"], df["Low"])
ta_df['CCI'] = ta.cci(df["High"], df["Low"], df["Close"])
ta_df['DPO'] = ta.dpo(df["Close"])
ta_df['KST'] = ta.kst(df["Close"])
ta_df['KST_sig'] = ta.kst_sig(df["Close"])
ta_df['KST_diff'] = (
    ta_df['KST'] -
    ta_df['KST_sig'])
ta_df['Ichimoku_a'] = ta.ichimoku_a(df["High"], df["Low"], visual=True)
ta_df['Ichimoku_b'] = ta.ichimoku_b(df["High"], df["Low"], visual=True)
ta_df['Aroon_up'] = ta.aroon_up(df["Close"])
ta_df['Aroon_down'] = ta.aroon_down(df["Close"])
ta_df['Aroon_ind'] = (
    ta_df['Aroon_up'] -
    ta_df['Aroon_down']
)

ta_df['ATR'] = ta.average_true_range(
    df["High"],
示例#10
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def add_technical_indicators(df):
    """
    Args:
        df (pd.DataFrame): The processed dataframe returned by `process_data`.

    Returns:
        pd.DataFrame: The updated dataframe with the technical indicators inside.

    Acknowledgements:
        - Thanks for Adam King for this compilation of technical indicators!
          The original file and code can be found here:
          https://github.com/notadamking/RLTrader/blob/e5b83b1571f9fcfa6a67a2a810222f1f1751996c/util/indicators.py

    """

    # Add momentum indicators
    df["AO"] = ta.ao(df["High"], df["Low"])
    df["MFI"] = ta.money_flow_index(df["High"], df["Low"], df["Close"],
                                    df["Volume"])
    df["RSI"] = ta.rsi(df["Close"])
    df["TSI"] = ta.tsi(df["Close"])
    df["UO"] = ta.uo(df["High"], df["Low"], df["Close"])

    # Add trend indicators
    df["Aroon_up"] = ta.aroon_up(df["Close"])
    df["Aroon_down"] = ta.aroon_down(df["Close"])
    df["Aroon_ind"] = (df["Aroon_up"] - df["Aroon_down"])
    df["CCI"] = ta.cci(df["High"], df["Low"], df["Close"])
    df["DPO"] = ta.dpo(df["Close"])
    df["KST"] = ta.kst(df["Close"])
    df["KST_sig"] = ta.kst_sig(df["Close"])
    df["KST_diff"] = (df["KST"] - df["KST_sig"])
    df["MACD_diff"] = ta.macd_diff(df["Close"])
    df["Mass_index"] = ta.mass_index(df["High"], df["Low"])
    df["Trix"] = ta.trix(df["Close"])
    df["Vortex_pos"] = ta.vortex_indicator_pos(df["High"], df["Low"],
                                               df["Close"])
    df["Vortex_neg"] = ta.vortex_indicator_neg(df["High"], df["Low"],
                                               df["Close"])
    df["Vortex_diff"] = abs(df["Vortex_pos"] - df["Vortex_neg"])

    # Add volatility indicators
    df["BBH"] = ta.bollinger_hband(df["Close"])
    df["BBL"] = ta.bollinger_lband(df["Close"])
    df["BBM"] = ta.bollinger_mavg(df["Close"])
    df["BBHI"] = ta.bollinger_hband_indicator(df["Close"])
    df["BBLI"] = ta.bollinger_lband_indicator(df["Close"])
    df["KCHI"] = ta.keltner_channel_hband_indicator(df["High"], df["Low"],
                                                    df["Close"])
    df["KCLI"] = ta.keltner_channel_lband_indicator(df["High"], df["Low"],
                                                    df["Close"])
    df["DCHI"] = ta.donchian_channel_hband_indicator(df["Close"])
    df["DCLI"] = ta.donchian_channel_lband_indicator(df["Close"])

    # Volume indicators
    df["ADI"] = ta.acc_dist_index(df["High"], df["Low"], df["Close"],
                                  df["Volume"])
    df["CMF"] = ta.chaikin_money_flow(df["High"], df["Low"], df["Close"],
                                      df["Volume"])
    df["EM"] = ta.ease_of_movement(df["High"], df["Low"], df["Close"],
                                   df["Volume"])
    df["FI"] = ta.force_index(df["Close"], df["Volume"])
    df["NVI"] = ta.negative_volume_index(df["Close"], df["Volume"])
    df["OBV"] = ta.on_balance_volume(df["Close"], df["Volume"])
    df["VPT"] = ta.volume_price_trend(df["Close"], df["Volume"])

    # Add miscellaneous indicators
    df["DR"] = ta.daily_return(df["Close"])
    df["DLR"] = ta.daily_log_return(df["Close"])

    # Fill in NaN values
    df.fillna(method="bfill", inplace=True)  # First try `bfill`
    df.fillna(value=0,
              inplace=True)  # Then replace the rest of the NANs with 0s

    return df