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Stock Market Prediction
- 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. 1
- 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. 2
- 8. Flow Chart 4
- 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: 6
- 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 6 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. 7
- 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. 8
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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. 10
- 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 11
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- 21. Bar Chart 11
- 22. Milestones, Deliverables, Critical Path Milestones - T1, T2, T4, T5, T6, T11 Deliverables – T3, T16, T9 Critical Path – T4 < T5 < T8 < T9 < T10 : 110 12
- 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 13
- 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. 13
- 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. 14
- 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 15
- 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) 16 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
- 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. 17 17
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- 33. UML 18
- 36. 21 TEST PLAN
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- 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. 25
- 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 26
- 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 26
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- 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 29
- 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. 30
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- 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. 32
- 51. The Algorithm 32
- 53. Thank you BY SALMAN SHEZAD