Arrow: Australian Stock Analysis in Python
  • Arrow: Australian Stock Analysis in Python
  • Arrow: Australian Stock Analysis in Python
  • Arrow: Australian Stock Analysis in Python
  • Arrow: Australian Stock Analysis in Python
  • Arrow: Australian Stock Analysis in Python
Originally published: 04/06/2019 07:25
Last version published: 30/08/2019 07:00
Publication number: ELQ-40624-3
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Arrow: Australian Stock Analysis in Python

Australian stock analysis and prediction using Python programming and Data Science

Description
The aim of the Arrow series is to help finance professionals with no or minimal programming background to expedite financial analysis tasks with less time and efforts using Data Science.

The Objective of this template is to help you analyze and pick stocks that best suits your Risk and Return preferences in few minutes. It is always a challenge to select stocks that are high in returns because generally, they are high in risk as well. The python script will make an attempt using Data Science visualization technique to compare various stock that offers the same return but with less risk.

The traditional way of establishing analysis is the spreadsheet which requires repetition of the same tasks and bit labor-intensive and time-consuming. The python template aims to speed up the process with better precision.

The analysis involves :
 Selecting multiple scripts at a time for analysis
 Few key ratio analysis such as PE, Dividend Yield, Beta
 Analysis of all stocks in an index risk and relationship in one single scatter plot.
 Plotting historical price movement
 Plotting Normalised Adjusted prices to see price growth in a given time period for various stocks in a single graph.
 Plotting prediction using Brownian motion for various stocks.

Bonus Outputs:
Excel is the world’s most widely used tool by Finance professionals for performing various analysis, considering the fact If you interested in further analysis;
 The script will save all plots and graphs in PNG format and,
 All raw data received from Yahoo Finance and calculated Data tables will be saved in CSV format.
 You can also save the entire book into PDF as a report.

So What you need to run the script:
1. Understanding of Finance and all Models used in the script.
2. Ideally Microsoft Azure Notebook account, which is Free at the moment (https://notebooks.azure.com/). Please refer to the following YouTube video for steps on how to create one: https://www.youtube.com/watch?v=r4B_zs3Lb-Y
3. Log in to Azure Notebook
4. Upload this script in a newly created project

### DISCLAIMER:
Quality of analysis depends upon the quality of data, models, and libraries. The template is based on yahoo finance data, python libraries written by third parties, and financial models. Together they do not guarantee 100% desired and/or actual outcomes. User should not consider it as a stand-alone decision-making tool to make monetary and non-monetary decisions.
The author of the template will not accept any liabilities whatsoever for any unexpected outcomes or losses.

NS: Please allow 5 - 10 minutes, depending upon the number of stocks and length of time, to obtain data from the source, make calculations and plot graphs.

This Best Practice includes
1 Python Script and 1 png file

Rizwan Ahmed Surhio offers you this Best Practice for free!

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Further information

To provide a next-generation solution for stock analysis

1. Availability of free Microsoft Azure account.
2. Availability of Python 3.6 version, Libraries and Modules used in the script.
3. Availability of Data from Yahoo Finance.

If Yahoo finance data service API depreciated.
Change in Python functions, Libraries, and Modules.
Local python version, libraries, modules may not appropriate to run the script.

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