analysis_ta_statistic_functions.py

import numpy as np
import pandas as pd
import talib as ta
from talib import MA_Type
import os
import configparser 

parser = configparser.ConfigParser()
parser.read('config.ini')

current_dir = os.path.dirname(os.path.realpath(__file__))

stock_symbol = parser.get('analysis','stock_symbol')
base_dir = parser.get('directory','base_dir')
in_dir = parser.get('directory','company_stock_marketprice_baseprice_prefilter')
out_dir = parser.get('directory','company_stock_marketprice_processed')

def main():
    # read csv file and transform it to datafeed (df):
    df = pd.read_csv(current_dir+"/"+base_dir+"/"+in_dir+"/"+in_dir+'_'+stock_symbol+'.csv')

    # set numpy datafeed from df:
    df_numpy = {
        'Date': np.array(df['date']),
        'Open': np.array(df['open'], dtype='float'),
        'High': np.array(df['high'], dtype='float'),
        'Low': np.array(df['low'], dtype='float'),
        'Close': np.array(df['close'], dtype='float'),
        'Volume': np.array(df['volume'], dtype='float')
        }

    date = df_numpy['Date']
    openp = df_numpy['Open']
    high = df_numpy['High']
    low = df_numpy['Low']
    close = df_numpy['Close']
    volume = df_numpy['Volume']



    #########################################
    ########## Statistic Function ###########
    #########################################



    #BETA - Beta of 5
    beta = ta.BETA(high, low, timeperiod=5)

    #CORREL - Pearson's Correlation Coefficient (r)
    correl = ta.CORREL(high, low, timeperiod=30)

    #LINEARREG - Linear Regression
    linearreg = ta.LINEARREG(close, timeperiod=14)

    #LINEARREG_ANGLE - Linear Regression Angle
    linearreg_angle = ta.LINEARREG_ANGLE(close, timeperiod=14)

    #LINEARREG_INTERCEPT - Linear Regression Intercept
    linearreg_intercept = ta.LINEARREG_INTERCEPT(close, timeperiod=14)

    #LINEARREG_SLOPE - Linear Regression Slope
    linearreg_slope = ta.LINEARREG_SLOPE(close, timeperiod=14)

    #STDDEV - Standard Deviation
    stdev = ta.STDDEV(close, timeperiod=5, nbdev=1)

    #TSF - Time Series Forecast
    tsf = ta.TSF(close, timeperiod=14)

    #VAR - Variance 
    var = ta.VAR(close, timeperiod=5, nbdev=1)



    df_save = pd.DataFrame(data ={
        'date': np.array(df['date']),
        'beta': beta,
        'correl':correl,
        'linearreg':linearreg,
        'linearreg':linearreg_angle,
        'linearreg_intercept':linearreg_intercept,
        'linearreg_slope':linearreg_slope,
        'stdev':stdev,
        'tsf':tsf,
        'var':var

    })

    df_save.to_csv(current_dir+"/"+base_dir+"/"+out_dir+'/'+stock_symbol+"/"+out_dir+'_ta_statistic_functions_'+stock_symbol+'.csv',index=False)

if __name__ == '__main__':
    main()