import pandas as pd import talib as ta import numpy as np def format_date(df): format = '%Y-%m-%d %H:%M:%S' df['Datetime'] = pd.to_datetime(df['date'] + ' ' + df['time'], format=format) df = df.set_index(pd.DatetimeIndex(df['Datetime'])) df = df.drop('Datetime', axis=1) return df # https://stackoverflow.com/questions/39684548/convert-the-string-2-90k-to-2900-or-5-2m-to-5200000-in-pandas-dataframe def replace_vol(df): df.volume = (df.volume.replace(r'[KM]+$', '', regex=True).astype(float) * \ df.volume.str.extract(r'[\d\.]+([KM]+)', expand=False) .fillna(1) .replace(['K','M'], [10**3, 10**6]).astype(int)) return df def get_all_features(df): #get_overlap_studies # BBANDS - Bollinger Bands df['bbub'], df['bbmb'], df['bblb'] = ta.BBANDS(df['close']) # DEMA - Double Exponential Moving Average df['DEMA_100'] = ta.DEMA(df['close'],timeperiod=100) df['DEMA_30'] = ta.DEMA(df['close'],timeperiod=30) df['DEMA_5'] = ta.DEMA(df['close'],timeperiod=5) # EMA - Exponential Moving Average df['EMA_100'] = ta.EMA(df['close'],timeperiod=100) df['EMA_30'] = ta.EMA(df['close'],timeperiod=30) df['EMA_5'] = ta.EMA(df['close'],timeperiod=5) # HT_TRENDLINE - Hilbert Transform - Instantaneous Trendline df['HT_TRENDLINE'] = ta.HT_TRENDLINE(df['close']) # KAMA - Kaufman Adaptive Moving Average df['KAMA'] = ta.KAMA(df['close']) # MA - Moving average df['MA_100'] = ta.MA(df['close'],timeperiod=100) df['MA_30'] = ta.MA(df['close'],timeperiod=30) df['MA_5'] = ta.MA(df['close'],timeperiod=5) # MAMA - MESA Adaptive Moving Average df['MAMA'], df['FAMA'] = ta.MAMA(df['close']) # MIDPOINT - MidPoint over period df['MIDPOINT'] = ta.MIDPOINT(df['close']) # MIDPRICE - Midpoint Price over period df['MIDPRICE'] = ta.MIDPRICE(df.high, df.low, timeperiod=14) # SAR - Parabolic SAR df['SAR'] = ta.SAR(df.high, df.low, acceleration=0, maximum=0) # SAREXT - Parabolic SAR - Extended df['SAREXT'] = ta.SAREXT(df.high, df.low, startvalue=0, offsetonreverse=0, accelerationinitlong=0, accelerationlong=0, accelerationmaxlong=0, accelerationinitshort=0, accelerationshort=0, accelerationmaxshort=0) # SMA - Simple Moving Average df['SMA_100'] = ta.SMA(df['close'],timeperiod=100) df['SMA_30'] = ta.SMA(df['close'],timeperiod=30) df['SMA_5'] = ta.SMA(df['close'],timeperiod=5) # T3 - Triple Exponential Moving Average (T3) df['T3'] = ta.T3(df.close, timeperiod=5, vfactor=0) # TEMA - Triple Exponential Moving Average df['TEMA_100'] = ta.TEMA(df['close'],timeperiod=100) df['TEMA_30'] = ta.TEMA(df['close'],timeperiod=30) df['TEMA_5'] = ta.TEMA(df['close'],timeperiod=5) # TRIMA - Triangular Moving Average df['TRIMA_100'] = ta.TRIMA(df['close'],timeperiod=100) df['TRIMA_30'] = ta.TRIMA(df['close'],timeperiod=30) df['TRIMA_5'] = ta.TRIMA(df['close'],timeperiod=5) # WMA - Weighted Moving Average df['WMA_100'] = ta.WMA(df['close'],timeperiod=100) df['WMA_30'] = ta.WMA(df['close'],timeperiod=30) df['WMA_5'] = ta.WMA(df['close'],timeperiod=5) #get_momentum_indicator # ADX - Average Directional Movement Index df['ADX'] = ta.ADX(df.high, df.low, df.close, timeperiod=14) # ADXR - Average Directional Movement Index Rating df['ADXR'] = ta.ADXR(df.high, df.low, df.close, timeperiod=14) # APO - Absolute Price Oscillator df['APO'] = ta.APO(df.close, fastperiod=12, slowperiod=26, matype=0) # AROON - Aroon df['AROON_DWN'],df['AROON_UP'] = ta.AROON(df.high, df.low, timeperiod=14) # AROONOSC - Aroon Oscillator df['AROONOSC'] = ta.AROONOSC(df.high, df.low, timeperiod=14) # BOP - Balance Of Power df['BOP'] = ta.BOP(df.open, df.high, df.low, df.close) # CCI - Commodity Channel Index df['CCI'] = ta.CCI(df.high, df.low, df.close, timeperiod=14) # CMO - Chande Momentum Oscillator df['CMO']= ta.CMO(df.close, timeperiod=14) # DX - Directional Movement Index df['DX'] = ta.DX(df.high, df.low, df.close, timeperiod=14) # MACD - Moving Average Convergence/Divergence df['MACD'], df['MACD_SGNL'], df['MACD_HIST'] = ta.MACD(df.close, fastperiod=12, slowperiod=26, signalperiod=9) # MACDFIX - Moving Average Convergence/Divergence Fix 12/26 df['MACDF'], df['MACDF_SGNL'], df['MACDF_HIST'] = ta.MACDFIX(df.close) # MFI - Money Flow Index df['MFI'] = ta.MFI(df.high, df.low, df.close, df.volume, timeperiod=14) # MINUS_DI - Minus Directional Indicator df['MINUS_DI'] = ta.MINUS_DI(df.high, df.low, df.close, timeperiod=14) # MINUS_DM - Minus Directional Movement df['MINUS_DM'] = ta.MINUS_DM(df.high, df.low, timeperiod=14) # MOM - Momentum df['MOM'] = ta.MOM(df.close, timeperiod=10) # PLUS_DI - Plus Directional Indicator df['PLUS_DI'] = ta.PLUS_DI(df.high, df.low, df.close, timeperiod=14) # PLUS_DM - Plus Directional Indicator df['PLUS_DM'] = ta.PLUS_DM(df.high, df.low, timeperiod=14) # PPO - Percentage Price Oscillator df['PPO'] = ta.PPO(df.close, fastperiod=12, slowperiod=26, matype=0) # ROC - Rate of change : ((price/prevPrice)-1)*100 df['ROC'] = ta.ROC(df.close, timeperiod=10) # ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice df['ROCP'] = ta.ROCP(df.close, timeperiod=10) # ROCR - Rate of change Percentage: (price-prevPrice)/prevPrice df['ROCR'] = ta.ROCR(df.close, timeperiod=10) # ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100 df['ROCR100'] = ta.ROCR100(df.close, timeperiod=10) # RSI - Relative Strength Index df['RSI'] = ta.RSI(df.close, timeperiod=14) # STOCH - Stochastic df['STOCH_SLWK'], df['STOCH_SLWD'] = ta.STOCH(df.high, df.low, df.close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) # STOCHF - Stochastic Fast df['STOCH_FSTK'], df['STOCH_FSTD'] = ta.STOCHF(df.high, df.low, df.close, fastk_period=5, fastd_period=3, fastd_matype=0) # STOCHRSI - Stochastic Relative Strength Index df['STOCHRSI_FSTK'], df['STOCHRSI_FSTD'] = ta.STOCHRSI(df.close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0) # TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA df['TRIX'] = ta.TRIX(df.close, timeperiod=30) # ULTOSC - Ultimate Oscillator df['ULTOSC'] = ta.ULTOSC(df.high, df.low, df.close, timeperiod1=7, timeperiod2=14, timeperiod3=28) # WILLR - Williams' %R df['WILLR'] = ta.WILLR(df.high, df.low, df.close, timeperiod=14) # get_volume_indicator # AD - Chaikin A/D Line df['AD'] = ta.AD(df.high, df.low, df.close, df.volume) # ADOSC - Chaikin A/D Oscillator df['ADOSC'] = ta.ADOSC(df.high, df.low, df.close, df.volume, fastperiod=3, slowperiod=10) # OBV - On Balance Volume df['OBV'] = ta.OBV(df.close, df.volume) # get_volatility_indicator # ATR - Average True Range df['ATR'] = ta.ATR(df.high, df.low, df.close, timeperiod=14) # NATR - Normalized Average True Range df['NATR'] = ta.NATR(df.high, df.low, df.close, timeperiod=14) # TRANGE - True Range df['TRANGE'] = ta.TRANGE(df.high, df.low, df.close) # get_transform_price # AVGPRICE - Average Price df['AVGPRICE'] = ta.AVGPRICE(df.open, df.high, df.low, df.close) # MEDPRICE - Median Price df['MEDPRICE'] = ta.MEDPRICE(df.high, df.low) # TYPPRICE - Typical Price df['TYPPRICE'] = ta.TYPPRICE(df.high, df.low, df.close) # WCLPRICE - Weighted Close Price df['WCLPRICE'] = ta.WCLPRICE(df.high, df.low, df.close) # get_cycle_indicator # HT_DCPERIOD - Hilbert Transform - Dominant Cycle Period df['HT_DCPERIOD'] = ta.HT_DCPERIOD(df.close) # HT_DCPHASE - Hilbert Transform - Dominant Cycle Phase df['HT_DCPHASE'] = ta.HT_DCPHASE(df.close) # HT_PHASOR - Hilbert Transform - Phasor Components df['HT_PHASOR_IP'], df['HT_PHASOR_QD'] = ta.HT_PHASOR(df.close) # HT_SINE - Hilbert Transform - SineWave df['HT_SINE'], df['HT_SINE_LEADSINE'] = ta.HT_SINE(df.close) # HT_TRENDMODE - Hilbert Transform - Trend vs Cycle Mode df['HT_TRENDMODE'] = ta.HT_TRENDMODE(df.close) return df def feature_main(df): df['time'] = df['time'].map(lambda x: np.sum(list(map(int, str(x).split(':'))))) df = get_all_features(df) values = {} for col in df.columns: idx = df.reset_index()[col].first_valid_index() values[col] = df.iloc[idx][col] df = df.fillna(value=values) return df