Processing Stocks Data in Python for the Analysis

Preface: In this video series Matt shows how to use the EODHD Financial APIs for the Stock Analysis with Python.

Don’t forget to explore other 2 Parts to learn more:
Part 1 Obtaining Stocks Data for the Analysis in Python
Part 2 Processing Stocks Data in Python for the Analysis
Part 3 Filtering and visualizing the Stocks Data Analysis results in Python

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import datetime as dt
from eod import EodHistoricalData
import json
import os
import pandas as pd
import requests

DEFAULT_DATE = dt.date.today() - dt.timedelta(396)
TODAY =dt.date.today()              

def get_closing_prices(folder= 'data_files', adj_close= False):
    """
    returns file with closing prices for selected securities
    """
    files = [file for file in os.listdir(folder) if not file.startswith('0')]

    closes = pd.DataFrame()

    for file in files:
        if adj_close:
            df = pd.DataFrame(pd.read_csv(f"{folder}/{file}",
                            index_col = 'date')['adjusted_close'])
            df.rename(columns={'adjusted_close': file[:-4]}, inplace=True)
        else:
            df = pd.DataFrame(pd.read_csv(f"{folder}/{file}",
                            index_col = 'date')['close'])
            df.rename(columns={'close': file[:-4]}, inplace=True)

        if closes.empty:
            closes = df
        else:
            closes = pd.concat([closes, df], axis = 1)
    closes.to_csv(f"{folder}/0-closes.csv")
    return closes                                                                        

def main():
    key = open('api_token.txt').read()
    get_closing_prices(folder='energy')

if __name__ == '__main__':
    main()

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import datetime as dt
from eod import EodHistoricalData
import json
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import requests

DEFAULT_DATE = dt.date.today() - dt.timedelta(396)
TODAY =dt.date.today()

def returns_from_closes(folder, filename):
    """
    returns instantaneous returns for selected securities
    """
    try:
        data = pd.read_csv(f"{folder}/{filename}", index_col=['date'])
        
    except Exception as e:
        print(f"There was a problem: {e}")            
    return np.log(data).diff().dropna()

def get_corr(data):
    """
    returns correl from securities
    """
    return data.corr()

def plot_closes(closes, relative=False):
    """
    plot absolute or relative closes for securities
    """
    if closes.endswith('.csv'):
        closes = pd.read_csv(closes, index_col=['date'])
    else:
        closes = pd.read_excel(closes, index_col=['date'])
    if relative:
        relative_change = closes / closes.iloc[0] - 1
        relative_change.plot()
        plt.axhline(0, c='r', ls='--')
        plt.grid(axis='y')
        plt.show()
    else:
        closes.plot()
        plt.grid(axis='y')
        plt.show()            

def main():
    key = open('api_token.txt').read()
    print(returns_from_closes('energy', '0-closes.csv'))
    print(get_corr(returns_from_closes('energy','0-closes.csv')))
    plot_closes(closes='mine/0-closes.csv',relative=True)

if __name__ == '__main__':
    main()

def get_return_data(*tickers, date=DEFAULT_DATE, adj_close=False, key):
    """
    saves closes and returns out to excel file named returns
    """
    client = EodHistoricalData(key)
    temp = pd.DataFrame()

    for ticker in tickers:
        try:
            if adj_close:
                temp[ticker] = pd.DataFrame(client.get_prices_eod(ticker,
                from_=date))['adjusted_close']
            else:
                temp[ticker] = pd.DataFrame(client.get_prices_eod(ticker,
                from_=date))['close']
        except Exception as e:
            print(f"{ticker} had a problem: {e}")
    data = temp
    data_instanteous = np.log(data).diff().dropna()
    data_pct = data.pct_change()

    with pd.ExcelWriter('returns.xlsx', datetime_format='yyyy-mm-dd') as writer:
        data.to_excel(writer, sheet_name='closes')
        data_instanteous.to_excel(writer, sheet_name='returns')
        data_pct.to_excel(writer, sheet_name='pct change')

    print(f"Data retrieved and saved to returns.xlsx in {os.getcwd()}")
    return data, data_instanteous, data_pct
    
def main():
    key = open('api_token.txt').read()
    tickers = "AAPL AMZN GOOG NVDA".split()
    returns = get_return_data(*tickers, key=key)
    print(returns[0])
    
if __name__ == '__main__':
    main()

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