DEPARTMENT OF INFORMATION SCIENCE AND ENGINEERING A PROJECT REPORT ON ON “REAL TIME STOCK MARKET PREDICTION USING BACK PROPAGATION ALGORITHM” Submitted in the partial fulfillment of the requirements in the 8th semester of BACHELOR OF ENGINEERING IN INFORMATION SCIENCE AND ENGINEERING By Girish B K 1NH15IS037 Under the guidance of Mrs. VANDANA Sr.Assistant Professor, Dept. of ISE, NHCE DEPARTMENT OF INFORMATION SCIENCE AND ENGINEERING CERTIFICATE Certified that the project work entitled “Real Time Stock Market Prediction Using Back propagation Algorithm ”, carried out by Mr. GIRISH B K,1NH15IS037, a bonafide student of NEW HORIZON COLLEGE OF ENGINEERING, Bengaluru, in partial fulfillment for the award of Bachelor of Engineering in Information Science and Engineering of the Visveshwaraiah Technological University, Belgaum during the year 2018-19.It is certified that all corrections/suggestions indicated for Internal Assessment have been incorporated in the Report deposited in the departmental library. The project report has been approved as it satisfies the academic requirements in respect of Project work prescribed for the said Degree. ……………………. ……………………. ……………………. Prof. Vandana Dr. R J Anandhi Dr. Manjunatha Internal Guide HOD, Dept. of ISE Principal,NHCE External Viva Name of the Examiners Signature with Date 1. __________________________ ________________________ 2. __________________________ _________________________ DEPARTMENT OF INFORMATION SCIENCE AND ENGINEERING DECLARATION I hereby declare that I have followed the guidelines provided by the Institution in preparing the project report and presented report of project titled “Real Time Stock Market Prediction Using Backpropagation Algorithm”, and is uniquely prepared by me after the completion of the project work. I also confirm that the report is only prepared for my academic requirement and the results embodied in this report have not been submitted to any other University or Institution for the award of any degree. Signature of the Student Name: GIRISH BK USN: 1NH15IS037 ABSTRACT Stock market index forecasting is one of the most challenging tasks for one who wants to invest in stock market. This challenge is because of the uncertainty and volatility of the stock prices in the market. Due to advancement in technology and globalization of business and share markets it is important to forecast the stock prices more quickly and accurately. Machine Learning techniques are most accepted due to the capability of identifying stock trend from massive amounts of data that capture the underlying stock price movement. Multi Linear Regression architecture with back propagation algorithm has the capability to predict with greater accuracy. A feed-forward neural network using multiple back propagation algorithm has been used to forecast next day’s OHLC data. This model has used the pre- processed dataset of Open price (O), High price (H), Low price (L), Close price (C), Volume Traded (V) and Turnover (T) for the period of 1 year. The root mean square error (RMSE) is chosen as indicators of performance of the network. i ACKNOWLEDGEMENT Any achievement, be it scholastic or otherwise does not depend solely on the individual efforts but on the guidance, encouragement and cooperation of intellectuals, elders and friends. A number of personalities, in their own capacities have helped me in carrying out this project. I would like to take an opportunity to thank them all. First and foremost I thank the management, Dr. Mohan Manghnani, Chairman, New Horizon Educational Institutions for providing us the necessary state of art infrastructure to do Project. I would like to thank Dr.Manjunatha, Principal, New Horizon College of Engineering, Bengaluru, for his valuable suggestions and expert advice. I would like to thank Dr. R J Anandhi, Professor and Head of the Department, Information Science and Engineering, New Horizon College of Engineering, Bengaluru, for constant encouragement and support extended towards completing my Project. I deeply express my sincere gratitude to my guide Mrs.Vandana, Sr. Assistant Professor, Department of ISE, New Horizon College of Engineering, Bengaluru, for her able guidance, regular source of encouragement and assistance throughout my project period. Last, but not the least, I would like to thank my peers and friends who provided me with valuable suggestions to improve my project. Girish B K (1NH15IS037) ii TABLE OF CONTENTS Abstract i Acknowledgement ii Table of Contents iii List of Figures iv 1. Preamble 1 1.1 Introduction 1 1.2 Relevance of the Project 2 1.3 Purpose 3 1.4 Scope of the Project 3 1.5 Problem Statement and Definition 4 1.6 Objective of study 4 1.7 Existing System 4 1.8 Proposed system 5 2. Literature Survey 6 3. System Requirements and specifications 8 3.1 General Description of the system 8 3.1.1 Overview of Functional requirements 8 3.1.2 Overview of Data requirements 9 3.2 Technical Requirements of the System 9 3.2.1 Hardware Requirements 10 3.2.2 Software Requirements 10 3.3 Language specification 11 3.3.1 Python 12 3.3.2 Machine Learning Features 13 3. 3. 3 Logistic Regression 14 4. System Design and Analysis 15 4.1 Preliminary Design 15 4.2 System Architecture 15 4.3 Data Flow Diagram 16 4.3.1 DFD for Data Extraction 16 4.3.2 DFD for Classification of Data 16 4.3 Use Case Diagram 17 5. Implementation 18 5.1 Different modules of the project 26 5.2 Flow Chart Of The Proposed System 28 6. Experimental Results 29 6.1 Outcomes of the Proposed System 33 7. Testing 34 7.1 Testing and Validations 34 7.2 Testing Levels 34 7.2.1 Functional Testing 35 7.2.2 Non-Functional Testing 36 7.3 White Box Testing 37 7.4 Different Stages of Testing 38 7.4.1 Unit Testing 38 7.4.2 Integration Testing 39 7.4.3 System Testing 42 7.4.4 Acceptance Testing 43 8. Conclusion and Future Enhancement 44 8.1 Conclusion 45 Reference 45 iii LIST OF FIGURES Figure no Figure Name Page no 1 Preliminary Design 15 2 System Architecture 15 3 Data Flow Diagram 15 4 DFD for Data Extraction 16 5 DFD for Classification of 16 Data 6 Use Case Diagram 17 7 Training Data 27 8 Flow Chart Of The Proposed 28 System 9 Data distribution 31 10 Non-Functional Testing 36 11 Unit Testing 39 iv Stock Market Prediction CHAPTER 1 PREAMBLE 1.1 INTRODUCTION: A share market is a market in which shares of publicly held companies or people are traded to raise money. The fluctuation of prices of shares depends upon the demand and supplies of shares. Only the registered Companies are allowed to carry out trading. Stock market forecasting is the process of trying to predict the future stock value of a company. The successful prediction of a stock's prices would return significant profit. Forecasting stock market index is an important financial problem that is receiving increasing diligence. A number of neural network models have been proposed for attaining accurate prediction results. Economies of a country are strongly linked and heavily influenced by the performance of the stock market. In India stock prices are basically influenced by sentimental factors and event driven activities. The investors at a large are still at a nascent stage and lack required knowledge and experience before investing in stock market. Most of them just do it for the sake of trying their luck. A few who own good resources and are serious about it often tend to invest based on good fundamentals, technical indicators and news channel based advises. We often see a sudden spurt or spike in stock prices as a result of these. There are many techniques in the literature and applications to predict short term movements based on different stochastic models. We have proposed here a model which will not require any previous experience of trading for investing into the stock market. The model suggests trend of stock index based on historical prices. Dept of ISE, NHCE Page 1 Stock Market Prediction 1.2 Relevance of the Project Predicting how the stock market will perform is one of the most difficult things to do. There are so many factors involved in the prediction – physical factors vs. physhological, rational and irrational behaviour, etc. All these aspects combine to make share prices volatile and very difficult to predict with a high degree of accuracy. Using features like the latest announcements about an organization, their quarterly revenue results, etc., machine learning techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions. In this article, we will work with historical data about the stock prices of a publicly listed company. We will implement a mix of machine learning algorithms to predict the future stock price of this company, starting with simple algorithms like averaging and linear regression, and then move on to advanced techniques like RSME. The core idea behind this article is to showcase how these algorithms are implemented. I will briefly describe the technique and provide relevant links to brush up on the concepts as and when necessary. In case you’re a newcomer to the world of time series, I suggest going through the following articles first: 1.3 PROBLEM STATEMENT AND EXPLANATION From the given data create a Machine Learning model that can predict Stock market movement (Closing price) most accurately. We’ll dive into the implementation part of this article soon, but first it’s important to establish what we’re aiming to solve. Broadly, stock market analysis is divided into two parts – Fundamental Analysis and Technical Analysis. • Fundamental Analysis involves analyzing the company’s future profitability on the basis of its current business environment and financial performance.