Python primer for Trading

[UberMachine]

Now for some analysis

Let's calculate the daily returns on close_price

df['Close'].pct_change(1)

You could calculate the percent change for any number of days on any colums by changing the arguments to the pct_change function.
So you need to calculate the 5 day returns on Adj Close then

df['Adj Close'].pct_change(5)

But you need to get back in the original dataframe. So

df['DailyReturn'] = df['Close'].pct_change(1)
df

You would get the following output

Instead of percentage values, you need the difference value, then

 df['Close'].diff(1)

 

[UberMachine]

Moving averages calculation

Let's calculate the 7 day moving average

df['MA'] = df['Close'].rolling(7).mean()

You can also calculate standard deviation, median and others on any column for any period
To calculate the median, use

df['Close'].rolling(5).median()

Try calculating 10 day moving standard deviation on close

Spoiler: 10 day std

df['Close'].rolling(10).std()

And also, 5 day maximum for close prices

Spoiler: 5 day max

df['Close'].rolling(5).max()

 

[UberMachine]

So some plotting

# Required to display plots
import seaborn as sns
sns.set()

or

%pylab inline

The first one gives you nicer graphs

Plotting a single column

df['Close'].plot()

Plotting multiple columns

cols = ['MA7', 'Close']
df[cols].plot()

You can generate box plot and histograms quite easily

df['Close'].plot('box')
df['Close'].plot('hist')

 

[UberMachine]

A quick look at the statistics

df['Close'].describe()

Statistics for all columns

df.describe()

 

[UberMachine]

And finally, after doing the hard work.
You need to export it for future use

output_filename = 'my_output.csv'
df.to_csv(output_filename)

This will generate a file myoutput.csv in the same folder from which you ran the notebook

But you need the output in Excel, then

output_excel= 'my_output.xlsx'
df.to_excel(output_excel)

That's it to keep you get started in Python

 

[MSN1979]

Done all that Successfully saved file

 

[MSN1979]

 

[MSN1979]

all complete I think

 

[MSN1979]

ignore this message

 

[UberMachine]

all complete I think