Today I continued along with the tutorial by first importing the numpy library.

import numpy as np

Then we assign Adj_Close to daily_close for both Microsoft and Apple.

daily_close = msft_data[['Adj_Close']]

We can’t do this for aapl, we would need a new variable name here. So we’ll only use Microsoft data to simplify things.

daily_return = daily_close.pct_change()

daily_return.fillna(0, inplace=True)

print(daily_return)

So next we have set our daily_return, replaced NA values with 0, and printing out the daily_return. The result is as follows.

(In the tutorial, at this point there is an error. adj_pricesadj_price = msft_data[‘Adj_Close’] should be instead adj_price = msft_data[‘Adj_Close’])

adj_price = msft_data['Adj_Close']

mav = adj_price.rolling(window=50).mean()

A few lines of Python more, and we get to plotting. Follow along.

In the command line first, run pip install matplotlib and brew install python-matplotlib.

pip install matplotlib

brew install python-matplotlib

import matplotlib.pyplot as plt

adj_price.plot()

mav.plot()

By this point we’ve got some nice charting going on from our captured Microsoft data. This is day 4 of creating a Python algorithmic trading advisor. Data Science is wonderfully simplified in modern years. If I can learn this, you can too.

Actually, we’ve encountered some errors using install matplotlib. Current output is shown below. Perhaps I can get some help solving this issue. But at least I learned something new today, getting snake emojis for Python Code! I did this by adding the jemoji gem in the config.yml file of my Jekyll Blog.

Thanks for reading.

Researching Stack Overflow

Good Evening from the Pacific

Chris