In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic.

1. Stock Price Prediction
BY SALMAN SHEZAD

2. BRIEF DESCRIPTION
 In Stock Market Prediction, the aim is to predict the future
value of the financial stocks of a company. The recent trend
in stock market prediction technologies is the use of
machine learning which makes predictions based on the
values of current stock market indices by training on their
previous values. Machine learning itself employs different
models to make prediction easier and authentic.
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3. Technical
Objective
 The technical objectives will be
implemented in R. The system must
be able to access a list of historical
prices. It must calculate the
estimated price of stock based on
the historical data. It must also
provide an instantaneous
visualization of the market index.
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4. Block
diagram
With
Relations
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5. Use case
diagram
4

6. Use Case Index
1
4

7. USE Case
Description
4

8. Flow
Chart
4

9. Interfaces/
Attributes 5
5

10. Requirement
Analysis
 After the extensive analysis of the problems in
the system. we are familiarized with the
requirement that the current system needs. The
requirement that the system needs is
categorized into the functional and non-
functional requirements. These requirements are
listed below:
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11. Functional Requirements
 Stock Id = use to know which id stock belongs to
 Stock name = helps to track which stock I am
monitoring
 Stock value = need to know what value is it at
beginning and ending
 Admin = person who going to do all the work
 Date = helps to monitor stock day by day
 General index = to keep track for everything
 Monitor = helps to view all the stocks graph
 Printer = to print out results
 Data = code we will be using to find out the
stock price
 Max value = maximum value for stock
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 Min value = minimum value for stock when its start
 Stock price = a constant price which we need to
start with
 Stock quantity = amount of stock we will be
investing.
 Closing price = the value of stock at end of the day
one
 Market price = comparison for stocks in regular
market
 Actual value = compare the new value with actual
day value
 Result = finally the result for knowing the stocks
(predictions)

12. Non-Functional Requirements
1. Reliability: The reliability of the product will be dependent on the accuracy of the data-date
of purchase, how much stock was purchased, high and low value range as well as opening
and closing figures. Also the stock data used in the training would determine the reliability
of the software.
2. Security: The user will only be able to access the website using his login details and will not
be able to access the computations happening at the back end.
3. Maintainability: The maintenance of the product would require training of the software by
recent data so that their commendations are up to date. The database must be updated
with recent values.
4. Portability: The website is completely portable and the recommendations completely
trustworthy as the data is dynamically updated.
5. Interoperability: The interoperability of the website is very high because it synchronize all
the database with the warp server.
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13. Select a process model, justify
Machine learning has significant applications in the stock price
prediction. In this machine learning project, we will be talking about
predicting the returns on stocks. We will develop this project with
following steps:
 First, we will learn how to predict stock price using the LSTM
neural network.
 Then we will build a dashboard using Plotly dash for stock
analysis. Then,
 The system will be able to access a list of historical prices and It
will calculate the estimated price of stock based on the historical
data.
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14. Modify Project
Management
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15. 6
9

16. 6
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17. 6
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18. Security/safety
concerns
Stock market prediction has always had a certain
appeal for everyone. In fundamental and
technical approaches, stock market price
movements are believed to derive from a
security’s relative data. Information from quarterly
reports or breaking news stories can dramatically
affect the share price of a security. Stock market is
where prices are determined randomly and
outperforming the market is infeasible. Market
timing is critical and opportunities can be found
through the careful averaging of historical price
and volume moments and comparing them
against current prices. The further reason that
price movements are not totally random, however,
technical analysis is considered to be more of an
art from rather than a science and is subject to
interpretation.
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19. Task Duration Dependencies
T1 30 n/a
T2 20 T1
T3 15 T2
T4 15 n/a
T5 15 T4
T6 n/a
T7 10 T4
T8 10 T5
T9 30 T8
T10 30 T9
T11 5 T4
12 5 T11
T13 5 T12
T14 15 T13
T15 30 T13
T16 5 T13
Activity
Table
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20. 11

21. Bar Chart
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22. Milestones, Deliverables, Critical Path
 Milestones - T1, T2, T4, T5, T6, T11
 Deliverables – T3, T16, T9
 Critical Path – T4 < T5 < T8 < T9 < T10 : 110
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23. CMM
Description
 CMM stands for the Capability Maturity Model.
CMM developed by adapting the Total Quality
Management to use for Software Development
and changed the name to Capability Maturity
Model. The CMM for software is a structure that
emphasizes on processes for software
development. It was developed by observing
best practices in software organization as well as
non-software organization. Therefore, the
collective process experience and expectations
of many companies are reflected the CMM for
software. The CMM for the software can be used
both to evaluate the software process of an
organization and to plan process improvements
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24. CMM
Levels
Level 1: $ 10,000
Very Low
Level 2: $ 20,000 Low
Level 3: $ 30,000
Normal
Level 4: $ 40,000
High
Level 5: $ 50,000
Very High.
My Project is of Level 1:
$ 10,000
I choose level 1 because
the system itself is not
complex, especially on the
level of Linux, however it’s
not a simple system either.
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25. Case Tool
Utilization
 Programming Tools: Managing process concerned with defining goals for company’s
future direction and determining on the missions and resources to achieve those
targets.
 Editing tools: Need to fix and edit website for better use.
 Documentation tools: The information that describes the product to its users. It
consists of technical manuals of product and online information.
 Change management tools: It is on us to deal with the change from the perspective
of an organization and individual is a systematic approach.
 Configuration management tools: Ensuring the proper accounting of a configuration
items and of the interrelationship between in and operation environment. It is
governance and system engineering process, and its software and capabilities are
necessary to deliver services for an organization.
 Testing Tools: finding how it works and if error occurs it should be fixed.
 Re-engineering tools: Involves reserving a program’s machine code back into the
source code. To duplicate or enhance the object to see how it works.
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26. Software duration estimate
Nominal effect = 3.2 x (KDSI)1.05 person-month person-months
The constants 3.2 and 1.05 are the values that best fitted the
data on the organic mode
products used by Boehm to develop intermediate
COCOMO.
3.2 x (10) 1.05 = 35 person-months
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27. Software Cost Estimate
 Cost estimate of $10,000 during the
requirements workflow
 Likely actual cost is in the range
($0.25k, $4k)
 Cost estimate of $10,000 at the end
of the requirements workflow
 Likely actual cost is in the range
($0.5k, $2k)
 Cost estimate of $10,000 at the end
of the analysis workflow (earliest
appropriate time)
 Likely actual cost is in the range
($9k, $10k)
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Level 1: $ 10,000
Very Low
Assembler Version Ada Version
Source code size
Development costs
KDSI per person-month
Cost per source statement
Function points per person-
month
Cost per function point
7KDSI
$1,000
0.2
$10
0.2
$635
2 KDSI
$5000
0.211
$15
1.8
$500

28. Overall
Class
Diagram
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29. Layer
Attributes
Private long serialVersionUID = 2L
Operations
Public Layer ( )
Public Layer( intneuronsNum, NeuronPropertiesneuronProperties )
Public void setParentNetwork(NeuralNetwork parent)
Public NeuralNetworkgetParentNetwork( )
Public iterator<Neuron>getNeuronsIterator( )
Public Neuron[0..*] getNeurons( ) Public void addNeuron( Neuron neuron)
Public void addNeuron( intidx, Neuron neuron)
Public void setNeuron( intidx, Neuron neuron)
Public void removeNeuron( Neuron neuron)
Public void removeNeuronAt( intidx )
Public Neuron getNeuronAt( intidx )
Public intindexOf( Neuron neuron )
Public intgetNeuronCount ( )
Public void calculate( ) Public void reset( )
Public void randomizeWeights( )
Fig 1.UML Class Diagram for the Layer.
NeuralNetwok
Attributes
Private long serialVersionUID = 3L
Private thread learningThread
Operations
Public NeuralNetwork
Public void addLayer( Layer layer)
Public void addLayer( intidx, Layer layer)
Public void removeLayer( Layer layer)
Public void removeLayer( intidx)
Public Iterator<Layer>getLayersIterator( )
Public Layer[0..*] getLayers( )
Public Layer getLayerAt( intidx)
Public intindexOf(Layer layer)
Public intgetLayersCount( )
Public void setInput( Double inputVector[0..*])
Public void setinput( )
Public Double[0..*] getOutput( )
Public Double[0..*] getOutputAsArray( )
Public void calculate( )
Public void reset( )
Public void run( )
Public void learn( TrainingSettrainingSetToLearn )
Public void learnNewThread( TrainingSettrainingSetToLearn )
Public void learnNewThread( TrainingSettrainingSetToLearn,
LearningRulelearningRule )
Public void learnInSameThread( TrainingSettrainingSetToLearn )
Public void
learnSameThread(TrainingSettrainingSetToLearn, LearningRulelearningRule)
Public void stopLearning( )
Public void randomizeWeights( )
Public NeuralNetworkTypegetNetworkType( )
Public void setNetworkType( NeuralNetworkType type)
Public Neuron[0..*] getInputNeurons( )
Public void setInputNeurons( Neuron inputNeurons[0..*] )
Public Neuron[0..*] getoutputNeurons( )
Fig 2.UML Class Diagram for the NeuralNetwork.
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30. 17

31. 17
17

32. 17

33. UML
18

34. 19
Sequence
Diagram

35. Stub/Drivers
in Testing
120

36. 21
TEST PLAN

37. 23
maintenance
plan

38. 24

39. Verification and Validation
 Verification
 Verification is the process of checking that
a software achieves its goal without any
bugs. It is the process to ensure whether
the product that is developed is right or
not. It verifies whether the developed
product fulfills the requirements that we
have.
 Verification is Static Testing.
 Validation:
 Validation is the process of checking
whether the software product is up to the
mark or in other words product has high
level requirements. It is the process of
checking the validation of product i.e. it
checks what we are developing is the
right product. it is validation of actual and
expected product.
 Validation is the Dynamic Testing.
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40. DECOMMISSION PLAN
 Develop a new ,up to date software system
 Plan for seamless transfer to the new SW
system
 Testing
 Training
 preparation Plan
 Load the system on back-up main frame
 Transfer to primary on a quiet Sunday
 Fix any problem
 try for a seamless transfer again
Items to be considered
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41. DECOMMISSION PLAN
SCAN: SCANS THE
DATA SEND TO THE
DATABASE.
SEARCH: FINDS THE WORDS
DEFINITION/TRANSLATION OF
THE DATA REQUIREMENT.
SELECT: CHOOSE THE
RIGHT
DEFINITION/TRANSLATI
ON OF THE REQUIRED
DATA.
SEND TRANSFER THAT
REQUIRED BACK TO
WHERE IT IS REQUIRED.
RECEIVE: TRANSFER
INPUT DATA FROM THE
INPUT DEVICE
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42. 27

43. 27

44. MASSIVE
PARALLELISM,
NEURAL
NETWORK,
SYSTOLIC
ARRAYS,
QUANTUM
COMPUTING
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45. 28

46. TELECOMM,HUB,
SWITCH,CORNING,ROU
TER,FIREWALL,5G
 Telecom - Telecommunication is the
transmission of information by various types
of technologies over wire, radio, optical or
other electromagnetic systems.
 Hub - a physical layer networking device
which is used to connect multiple devices in
a network
 Switch -A switch is a device in a computer
network that connects other devices together
 Corning - A company that focuses on
providing cellular coverage and land
deployment to businesses
 Router - a networking device that forwards
data packets between computer networks
 Firewall - a network security system that
monitors and controls incoming and
outgoing network traffic based on
predetermined security rules
 5G - the fifth-generation technology
standard for broadband cellular networks
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47. CYBER
SECURITY,ATTACKS,DEFENSE.
 Cyber Security: the protection of internet-connected systems such as
hardware, software and data from cyber-threats. The practice is used by
individuals and enterprises to protect against unauthorized access to data
centers and other computerized systems.
 Cloud Security: cloud computing security or, more simply, cloud security
refers to a broad set of policies, technologies, applications, and controls
utilized to protect virtualized IP, data, applications, services, and the
associated infrastructure of cloud computing
 Digital Forensics: Digital forensics is the modem day version of forensic
science and deals with the recovery and investigation of material found in
digital devices. It is most often used in cybercrime situations, including but
not limited to attribution. identifying leaks within an organization.
 Firewalls: A firewall is a system designed to prevent unauthorized access to
or from a private network. You can implement a firewall in either hardware
or software form, or a combination of both. Firewalls prevent unauthorized
internet users from accessing private networks connected to the internet,
especially intranets.
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48. 31
Programming
languages

49. 31

50. The Algorithm
 I thought of a different
way of creating a bot to
decide to buy/sell a
stock today given the
stock’s history. In essence
you just predict the
opening value of the
stock for the next day,
and if it is beyond a
threshold amount you
buy the stock. If it is
below another threshold
amount, sell the stock.
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51. The
Algorithm
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52. Activity log
4 columns
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53. Thank you 
BY SALMAN SHEZAD