Panda DataReader

eg1.py
import matplotlib.pyplot as plt
plt.plot([1,2,3,4],[1,5,10,20], ‘ro’)
plt.axis([0, 8, 0, 25])
plt.show()

eg2.py

from pandas_datareader import data
import pandas_datareader.data
import numpy as np
import pandas as pd
stock = [‘AAPL’,’GOOG’,’INFY’,’MSFT’]
panel_data = data.DataReader(stock,data_source=”google”,start=’01/01/2010′)
returns = panel_data.pct_change()
mean_return = returns.mean()
return_stdev = returns.std()
annualised_return = round(mean_return * 252,2)
annualised_stdev = round(return_stdev * np.sqrt(252),2)
print(mean_return)
print(return_stdev)

Links and Keywords

Python
Ipython
Jupyter
Numpy
Pandas
Matplotlib
Scikit learn
Newer tools like ggplot and ggvis in the R language, along with
web visuali‐ zation toolkits based on D3js and HTML5 canvas,
often make Matplotlib feel clunky and old-fashioned.

https://chrisalbon.com/python/pandas_with_seaborn.html
https://lectures.quantecon.org/py/pandas.html
http://www.learndatasci.com/python-finance-part-yahoo-finance-api-pandas-matplotlib/

An Introduction to Stock Market Data Analysis with Python (Part 1)

https://www.datacamp.com/community/tutorials/finance-python-trading#gs.JhPFJMg

Pandas Tutorial – Using Matplotlib

a simple trading algorithm and performed backtests via Pandas, Zipline and Quantopian.
Yves Hilpisch’s Python For Finance book
Mastering Pandas for Data Science” by Michael Heydt

 

Python start

Install Anaconda, Spyder (python 3.6) IDE best tool to use
Eg 1:
print('Hello World')
Eg 2 :
message = "Hello Dibs"
print (message)
Eg 3:
message = "Hello dibs ribs"
print (message)
print (message.title())
print (message.upper())
print (message.lower())
print ("Hello " + message)
Eg 4:
b = ['one','two','three','four']
print (b)
print (b[0])
print (b[3])

 

 

 

 

 

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