How to Get Market Data using Yahoo API with Python




Market data is the main resource for conducting quantitative analysis. Yahoo finance provides API for getting market data and is the best source of free data to start market analysis.
In this writing I explain how you can get daily, monthly and even intraday data using Yahoo API in Python.
Fortunately yfinance package in Python provided us a great function we can use to extract market data for each Symbol.
Here I show you how you can create working directory in Python and save the market data in it and make a function that get all SP500 data at the same time. It is pretty straightforward.
Warning: make sure you create an empty folder for your working directory and set it as working directory because for getting the SP500 data we first clean the working directory first and then add the new .csv data in the folder and I am sure you don’t want to delete the sensitive data accidently by executing this code.


import yfinance as yf
import os
import pandas as pd
WorkingDirectory='set your WorkingDirecotory Here'
os.chdir(WorkingDirectory)
# ___________________________ Single Symbol ______________________
def Ticker(symbol):
# You Probably just need the first line of this function to download the data the next lines
# is just preparing the downloaded data and the code for saving it as .csv file.
    df = yf.download(symbol, period='5y').dropna()
    df=df.reset_index()
    del df['Adj Close']
    string_date=[]
    for date in df['Date']:
        string_date.append((str(date)[:10]).replace('-',''))
    df['Date']=string_date
    df.to_csv(WorkingDirectory+'/'+symbol+'.csv', index=False)
# ___________________________ Market Data ______________________
# you can provide a list of Tickers to get their market data
def Market(symbols,remove=True):
    # Warning if remove == True this part removes all files in your WorkingDirectory
    if remove == True:
        folder = WorkingDirectory
        for the_file in os.listdir(folder):
            file_path = os.path.join(folder, the_file)
            try:
                if os.path.isfile(file_path):
                    os.unlink(file_path)
               
            except Exception as e:
                print(e)

    for tiker in symbols:
        Ticker(tiker)
# ___________________________ Intraday _____________________________________   
  
def intraday(symbol,Interval='60m',period='3mo'):
    df_1h = yf.download(symbol,period=period,interval =Interval)
  
   # save data with Ticker name in the working directory
    df_1h.to_csv(WorkingDirectory+'/'+symbol+Interval+'.csv', index=False)


# ____________________________ Done  __________________________________
#_______________________________Execution code_________________________
Ticker('QBAK')
#_________________________________________________________________________
intraday('AET',Interval='60m',period='3mo')
#_________________________________________________________________________
# You can get data of the customized list of stocks here as .csv file 
market=['^GSPC','SYY','PNR','MRK']
Market(market,remove=True)
#_______________________________________________________________________

market=['^GSPC','ALXN','PM','NBL']
Market(market,remove=True)
#_________________________________________________________________________
Market(SP500,remove=True)


Comments

Post a Comment

Popular posts from this blog

Application of GARCH models in R – Part II ( APARCH)

Image
If you read my previous post regards forecasting volatility using GARCH models you won’t have any trouble to forecast volatility by APARCH models. APARCH model is the extended form of GARCH model that allowing to combine two effects in practical use (i.e. asymmetry in the impact of positive and negative lagged returns (i.e. leverage effects) and have flexible power in the volatility calculation). The formula of APARCH is: The APARCH model is one of the most complicated models in use. It allows for leverage effects when gamma is not zero and power effects when delta is not 2. If we impose both values to zero and 2 respectively we just get the GARCH model back. You should note that in many case estimation will fail if the data sample is too short or exhibits structural breaks in volatility like what happened to SP500 index during the 2007-2009 crisis, or it will fail if the data sample size is too short. If we restrict t
Read more

How to scrap Zacks Rank signal in Python

Image
If you trade in the US market or follow market analysis you probably heard about Zacks Rank system provided by Zacks Investment Group. This ranking system is based on fundamental conditions of the company which revise frequently. the Zacks analysts revise their analysis based on most recent information about each company, and at the end Zacks rank each company in five different level. 1- Strong Buy 2- Buy 3- Hold 4- Sell 5- Strong Sell You should be noted that based on Zacks, these are not recommendation for buying or selling the equities, rather these are the analysis of companies fundamental usually based on a benchmark like SP500 index. In other words, for instance if a company has Zacks Rank 3 or Hold, that means its returns is foretasted to be aligned with the SP500 return. For the Zacks Rank 4 or Sell the projection is it under-perform the benchmark and so on. Zacks claim these analysis is the 1 to 3 months projection and can be revised anytime based on the new informati
Read more

How to check if a distribution is normal?

Image
    Many models assumed that the distribution is normal. It is a wise idea to check if your distribution is normal before use it in the model. In this post I want to explain you, some numerical and visual methods you can use to check if a distribution is normal. 1)     Boxplot-Whisker Plot and Histogram: Plotting Boxplot-whisker plot and the histogram of the distribution is a visual way you can use to see if the distribution looks normal. The Boxplot-whisker lets us check the symmetry around the mean and the histogram help us to visualize the overall shape of the distribution. Let’s see it in an example: In this example we create one normal distribution sample and one non-normal distribution sample and use the boxplot-whisker and histogram to visual them. import numpy as np import pandas as pd import scipy.stats as stats import matplotlib.pyplot as plt # Sample A:
Read more