Free Friday #15 – Downloading Custom Data for Build Alpha using Python

The first few lines are simple to import statements pulling public code that we can reuse including the popular pandas library.

import pandas as pd
from datetime import datetime
from pandas_datareader import data

I then define a function that downloads the data using the built-in DataReader function of the pandas_datareader library. I also adjust the open, high, low and close prices by the split ratio at every bar. This ensures we have a consistent time series if a stock has undergone a split, for example. **Please note other checks could be recommended like verifying high > open and high > close and high > low, but I have left these up to Yahoo in this post**. I then end the function returning a pandas data frame that contains our downloaded data. This get_data function will be valuable later in the code.

def get_data(symbol, start_date, end_date):

dat = data.DataReader(symbol, "yahoo", start_date, end_date)
dat['Ratio'] = dat['Adj Close'] / dat['Close']
dat['Open']  = dat['Open']  * dat['Ratio']
dat['High']  = dat['High']  * dat['Ratio']
dat['Low']   = dat['Low']   * dat['Ratio']
dat['Close'] = dat['Close'] * dat['Ratio']
return dat

I then go ahead and put all 30 dow tickers in a Python list named DJIA. I also go ahead and create our start and end dates in which we desire to download data.
DJIA=["AAPL","AXP","BA","CAT","CSCO","CVX","KO","DD","XOM","GE","GS","HD","IBM","INTC","JNJ","JPM","MCD","MMM","MRK","MSFT",
"NKE","PFE","PG","TRV","UNH","UTX","V","VZ","WMT","DIS"]
start = datetime(2007,1,1)
end   = datetime.today()


Finally, and the guts of this code, I loop through all 30 of our tickers calling the get_data function on each one of them. After downloading the first one, AAPL in our case, I open a file named AAPL.csv and then loop through the downloaded price series retrieved from our get_data function. I then write each bar to the file appropriately named AAPL.csv. I then close the AAPL.csv file before downloading the second symbol, AXP in our case. This process is repeated for each and every symbol. The result is 30 seconds to download 30 stocks worth of data! Each symbol’s data is saved in a file named Symbol.csv.

for ticker in DJIA:

DF = get_data(ticker,start,end)
fh = open("%s.csv" % ticker,'w+')
for i,date in enumerate(DF.index):
fh.write("%s,%.2f,%.2f,%.2f,%.2f,%dn" % (date.strftime('%Y%m%d'),DF['Open'][i],DF['High'][i],DF['Low'][i],DF['Close'][i],DF['Volume'][i]))
fh.close()

Now to the second part. Using this data in BuildAlpha is as simple as clicking on settings and searching for your desired file. I’ve attached a photo below that shows how the trader/money manager can now run tests on the newly downloaded AAPL data using the symbol “User Defined 1”. Pictures below for clarity.