Delete helper.py

helper.py

@@ -1,280 +0,0 @@
-import pandas as pd
-import numpy as np
-
-# check if the library folder already exists, to avoid building everytime you load the pahe
-# import streamlit as st
-import requests
-import os
-import sys
-import subprocess
-if not os.path.isdir("/tmp/ta-lib"):
-
-    # Download ta-lib to disk
-    with open("/tmp/ta-lib-0.4.0-src.tar.gz", "wb") as file:
-        response = requests.get(
-            "http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz"
-        )
-        file.write(response.content)
-    # get our current dir, to configure it back again. Just house keeping
-    default_cwd = os.getcwd()
-    os.chdir("/tmp")
-    # untar
-    os.system("tar -zxvf ta-lib-0.4.0-src.tar.gz")
-    os.chdir("/tmp/ta-lib")
-    os.system("ls -la /app/equity/")
-    # build
-    os.system("./configure --prefix=/home/appuser")
-    os.system("make")
-    # install
-    os.system("make install")
-    # back to the cwd
-    os.chdir(default_cwd)
-    sys.stdout.flush()
-
-# add the library to our current environment
-from ctypes import *
-
-lib = CDLL("/home/appuser/lib/libta_lib.so.0.0.0")
-# import library
-try:
-    import talib as ta
-except ImportError:
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "--global-option=build_ext", "--global-option=-L/home/appuser/lib/", "--global-option=-I/home/appuser/include/", "ta-lib"])
-finally:
-    import talib as ta
-
-    
-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