User Manual
User_manual
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Assessment Submission Form
Student Name
Satish Mohan; Navin Mutha; Gautam Gupta (GROUP 10)
Student Number
18200575; 18202954; 18201797
Assessment Title
Group Assignment 1: STOCK MARKET ANALYSIS by the Team Data Whisperers
Module Code
MIS41110
Module Title
Programming for Analytics
Module Coordinator
Miguel Nicolau
Date Submitted
November 23rd 2018
Office use only
Date Received
A SIGNED COPY OF THIS FORM MUST ACCOMPANY ALL SUBMISSIONS FOR ASSESSMENT.
STUDENTS SHOULD KEEP A COPY OF ALL WORK SUBMITTED.
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formally presented work (including essays, examinations, projects, laboratory reports or
presentations). The penalties associated with plagiarism are designed to impose sanctions that reflect
the seriousness of the University’s commitment to academic integrity. Ensure that you have read the
University’s Briefing for Students on Academic Integrity and Plagiarism and the UCD Plagiarism
Statement, Plagiarism Policy and Procedures, (http://www.ucd.ie/registrar/)
Declaration of Authorship
I declare that all material in this assessment is my own work except where there is clear
acknowledgement and appropriate reference to the work of others.
Signed: NAVIN MUTHA
SATISH MOHAN
GAUTAM GUPTA
Date: 23rd November 2018
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Table of Contents
STOCK MARKET ANALYSIS ....................................................................................................................... 3
FLOW CHART ........................................................................................................................................... 3
UNIFIED MODELING LANGUAGE ............................................................................................................. 5
USER-MANUAL ........................................................................................................................................ 6
ERROR HANDLING and EXCEPTION HANDLING ...................................................................................... 9
BIBLIOGRAPHY ...................................................................................................................................... 10
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STOCK MARKET ANALYSIS
In today’s world most of the individuals spend lots of money in pursuing higher studies in pursuit of
better jobs and placement. Unfortunately, they are reluctant to spend even 10% of the amount they
have earned in investing. It is very important for an individual to understand investing as it helps
securing the future over a longer period and requires patience. The purpose is to make the investor
make a sound decision and evaluate the organizations performance based on certain financial
parameters (Julia writer, 2017).
When it comes to investing in stocks, analyst should be capable enough of conducting a thorough
technical analysis of the stock before investing. This includes simple to advanced analysis of numbers
to provide a descriptive and predictive model based on price movements in the past for a stock.
A Stock Market Analysis model is built using Python Programming language to help investors analyse
any stock which they want and make further financial decisions of buying, holding or selling stocks
(Julia writer, 2017).
FLOW CHART
Before understanding any model, it is imperative to understand the flow of the code and identify
how exactly it works. It helps interpreting the flow of the model along with different processes and
decisions as required and describe it in a chronological manner.
The below chart is the basic graphic about how the initialisation and describing the main
functionalities of the model.
Figure 1: Flow Chart – Initialisation
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The Below 2 figures now describes the Other Functionalities which have been incorporated in the
model for analysis purposes.
1. Default Mode: 52 Week Range
Figure 2: 52 Week Range
2. Custom Range: Variable Date Range
Figure 3: Variable Data Range
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UNIFIED MODELING LANGUAGE
UML provides an overview of the flow control for interactions where similar information to sequence
diagram, but it is focussed primarily on object relationships. The sequence of messages is shown
through a numbering scheme. The following diagram corresponds to a communication diagram
between the actor/user and the model of the code which isn’t immediately visible but communicates
set of messages between adjacent objects.
Figure 4: UML - Sequence Diagram
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USER-MANUAL
A user-manual is a technical communication document intended to give assistance to people using
the above mode. Below is a list of steps which will be helpful for the same.
1. There are 5 Python files; “home_page.py”, “descriptive_analytics.py”, “predictive_analytics”,
“candlestick_timeseries”, “twitter”. Download the above-mentioned files for executing Stock
Market analysis.
2. It is important to import certain Python Packages for successful execution of the code. Below
is a list of those packages: a. pandas_datareader
b. pandas
c. numpy
d. datetime
e. calendar
f. pandas_market_calendars
g. descriptive_analytics
h. predictive_anlaytics
i. candlestick_timeseries
j. twitter
k. warnings
l. sys
m. PIL
n. image
o. scikit
p. matplotlib
q. statsmodels.tsa.stattools
r. statsmodels.tsa.arima_model
s. ARIMA
3. Execute the “home_page.py” file.
4. Enter the Company Ticker for which you want to do an analysis.
5. A general summary will be shown describing the organization’s Current price, Market
Capitalisation, IPO year etc.
6. Another Menu option opens asking from 3 different options.
a. 52 Week Range: Showcasing Past 1 Year Data by Default
b. Custom Date Range: User can manually specify the Date for which details are
required.
c. Go Back to Previous Menu
7. If option 6.a is chosen, it further throws certain Menu options which are required for analysis.
They are: a. Stock Analysis for the {company}.
b. Stock Prediction for {company}.
c. Visualisations for {company}
d. Twitter Sentiment Analysis for {company}
e. Home Menu to change to go back to Step 6.
8. If option 7.a. is chosen – it describes the analytics associated with Descriptive Statistics.
a. Basic Statistics analysis for {company}.
b. Advanced Statistical Analysis for {company}.
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c. Comparison of Stocks between different companies.
d. Main Menu
9. 8.a. route selection (Basic Statistics analysis) will display Mean, Median, Range, 1st and 3rd
Quartile, Standard Deviation, Coefficient of Variation for Various Menu options as listed
below: a. Open Price
b. Close Price
c. High Price
d. Low Price
e. Adjusted Close Price
f. Volume
g. Previous Menu
For a-f, all the details will be shown as mentioned above. Option g. selection will be going back
to Step 8 for further analysis as required by the user.
10. 8.b. route selection (Advanced Statistical Analysis) will further display a menu option, which
will plot charts and graphs for all provided options
a. Simple Moving Average
b. Exponential Weighted Moving Average
c. Moving Average Convergence Divergence
d. Go Back to Previous Menu
For a-c, options additional details such as window size will be requested for further graph
plotting. Option d. selection will be going back to Step 8 for further analysis as required by the
user.
11. 8.c. route selection (Compare Stocks) helps in comparison between different stocks based on
their Statistical and Graphical Views as required. Maximum Stocks which can be compared are
3. We can enter different stocks ticker symbols and can compare based on analysis as
required. In comparison of stocks, Statistical will give basic descriptive mathematical
differences such as Mean, Std deviation, Min and Max values, quartiles etc., whereas
Graphical methods compares the Simple Moving Averages and Raw time Series options for
the chosen stocks.
12. In the same route, there is a previous menu option which will redirect back to Step 8 for
further analysis.
13. All the options are explored in Step 8. Clicking on 8.d., will traverse back to Step 7 for other
options.
14. 7.b. is the Predictive Analysis model for the Stock Analysis. There are two options to be
selected under the same. They are: a. Prediction using Linear Regression
b. Prediction using ARIMA Model
c. Main Menu
15. If option 14.a, is chosen Linear Regression is done where different functionalities are
incorporated such as prediction can be made for any past date as well based on the training
data set chosen and displaying the Mean Square Value, R Square and RMS value as well.
Graphical representation is also present for visualisation purposes.
16. If option 14.b, is chosen ARIMA Model for advanced predictive statistics is also used. It works
on the concept of Auto Regressive Moving average with graphs provided for the same. It gives
advanced variables such as coefficient of error, standard deviation, Z and P value as well.
17. If option 14.c is chosen it goes back to previous menu i.e. Step 7.
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18. 7.c. is the Visualisation models for Date Range selected. There are 4 further options which are
provided below for 7.c.
a. Time Series and Trend Lines
b. Candle Stick View
c. Histogram for High and Low
d. Previous Menu
19. 18.a, if chosen will ask for 2 other options which are: a. Time Series
b. Trend Lines
c. Previous Menu
Options a-b will display Time Series and Trend Lines for the Date range through a graphical
representation. If option ‘c’ is chosen, it will go back to Step 18
20. If 18.b. is chosen, it will plot a Candlestick for the data based on their prices and display the
values as well.
21. If 18.c, is chosen it will plot Histogram for Open and Low Prices. It will return to main menu
after this display at Step 18 by choosing 18.d.
22. All the options have been explored for Step 7.a, 7.b. & 7. c.. Now if option 7.d. is chosen Twitter
Sentiment Analysis comes into picture.
23. Twitter Analysis helps in finding the polarity of the company based on the sentiment analysis.
An input needs to be provided to check the sentiment based on the limit (n) of tweets which
user enters for the company. Based on this limit of tweets it will give either a positive or
negative response for the recent trend which can help in decision making process of buying,
selling or holding a stock.
24. Last option in Step 7 i.e. 7.e. will roll back to Step 6.
25. The other option in Step 6 in Custom Date Range where a user can input the desired date
range i.e. a Start and an end Date.
26. For Custom Date Range the Steps for execution are the same from Steps 7 to 25 apart from
the variable Date range provided. The performances and outputs are similar in nature.
27. Step 7.c will exit from the code altogether.
28. Exit from Code.
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ERROR HANDLING and EXCEPTION HANDLING
This provides an extensive list in the model where we have applied and handled the errors and
exceptions using different conditions under different functions.
a.
b.
c.
d.
e.
f.
g.
h.
i.
j.
k.
l.
def getCompanyDetails (Company Ticker):
This function extracts the Ticker Symbol for all the companies listed in NASDAQ website and
cross verifies with the ticker symbol entered by the user. If an unknown symbol is added, it
will throw an error and return to back to the home screen for entering the correct symbol
def main () (Company Ticker):
This function handles the ticker symbol if entered in lower case. It automatically converts the
alphabets into Upper case before hitting the companies listed in NASDAQ.
“__main__”:
While executing the code for first time, if a user wants to understand the flow of the model,
they can press Y and view that else exit from that for analysis.
def validate_date_format (date_string):
The Date format needs to be YYYY-MM-DD format. If added in any other format it will throw
and error and ask the user to enter in specified format.
def dateValidation:
In the case for Custom Data Range, where we need to add the start date, it validates the date
entered is a valid date
def endDateValidation:
In the case for Custom Data Range, an end date needs to be inputted by the user. It validates
whether the end date is valid format and not less than the start date. It can be greater than
equal to the start date i.e. dateValidation.
def validate_date_range:
It validates the Date Range specified is not entered as a future Date and not a valid case.
def validateWorkingDays:
Need to check for the days when Start and End Date can either be both Saturday and Sunday.
In that case a validation check is placed where it will throw an error in that case. Also, if a date
is entered 6 months apart but both are weekends, in that case it will not throw an error. Only
consecutive weekends will throw an error and ask the user to input the new dates.
def_simple_moving_avg:
In descriptive statistics while displaying Simple Moving Average, integer check has been
placed on the Windows size and a match window of 100 is taken into consideration for a
longer date range i.e. beyond 250.
def_exponential_Weighted_MA:
In descriptive statistics while displaying Exponential Weighted Moving Average, integer check
has been placed on the Windows size and a match window of 100 is taken into consideration
for a longer date range i.e. beyond 250.
def comparestocks: This will compare the stocks for multiple tickers entered and validate the
ticker symbol in each case.
In all the cases where Menu options are provided, if a non-provided option is entered, it will
throw an error showcasing as invalid option, please chose from the valid options.
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BIBLIOGRAPHY
Julia writer, 2017. Benzinga. [Online]
Available at: https://www.benzinga.com/general/17/06/9602734/why-is-stock-chart-technicalanalysis-important
[Accessed 13 June 2017].
Contribution of Each Individual Member ( Equal Contribution as “The Data Whisperers”:
1. Navin Mutha: - Twitter Analysis, Visualization, Main Menu, Validation
2. Satish Mohan: - Descriptive Analytics, Predictive Analytics, Validation
3. Gautam Gupta: - Report Writing, UML Flow Chart, Code Integration and Testing, Validation
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