Fr.C.
Rodrigues Institute of Technology,Vashi
Mini-Project Progress
S.E. (Computer) Sem - IV
2023-24
STOCK - X
Group members : [Group no. : 16 B]
Aditya Ohol 1022221
Om Shinde 1022253
Revant Shinde 1022254
Prajakt Patil 1022232
Presentation Outline
Abstract
Introduction
Literature Survey
Existing System
Proposed System
Scope
Hardware and Software required
Implementation
Reference
Conclusion
References
ABSTRACT
Stock market trading has been a subject of interest to investors, academicians,
and researchers. Shares which entitles the owner to a proportion of the
corporation’s assets and profits equal to how much stock they own.
Analysis and management of the of stock market data is a challenging task
nowadays, a large number of learning algorithms are developed to study market
behavior's and enhance the prediction accuracy; its global optimization ability
with continuous data has been exploited in financial domains.
So, to overcome the problem of data management this web app will help you
provide user friendly stock management..
The app mainly deals with tracking of daily profit, loss and real time stock
values. It also provides daily financial news which will help user to stay updated
in today’s rapidly growing commercial world.
INTRODUCTION
Background
In today's fast-paced and interconnected global economy, the world of stock
trading and investment has become more accessible than ever before.
As the global economy intertwines with technological advancements, the stock
market has become an integral part of investment strategies for individuals
and institutions alike. Investors, both seasoned and novice, seek reliable tools
and insights to navigate the complex and dynamic financial markets.
Understanding the volatile nature of stocks and their potential impact on
financial portfolios, there is a pressing need for a comprehensive platform that
empowers users with the necessary tools and information to make informed
decisions.
INTRODUCTION
Motivation
With the evolution of technology and the abundance of financial data, the
motivation to create StockX arose from the desire to bridge the gap between
investors and the intricacies of the stock market.
We recognized the challenges faced by traders, investors, and finance enthusiasts
in gaining access to accurate and up-to-date financial information.
The passion to facilitate educated decision-making in the world of finance is
what drives us to develop StockX as the go-to resource for stock analysis,
prediction, portfolio management, and financial news.
INTRODUCTION
Aim & Objective
The primary aim of StockX is to revolutionize the way individuals perceive and
interact with the stock market. Our objective is to provide a one-stop platform
that amalgamates cutting-edge technologies and expert financial analysis,
equipping users with the tools needed to make well-informed investment
choices.
We strive to empower investors, from beginners to seasoned professionals, with
the ability to predict market trends, analyze stocks with precision, monitor
portfolio performance, and stay updated with the latest finance news - all
within a user-friendly and intuitive interface.
INTRODUCTION
FEATURES:
Stock Analysis.
Predictor.
Portfolio Performance Tracker.
Finance News.
LITERATURE SURVEY
Stock Market Prediction Using Machine Learning Techniques
Naadun Sirimevan; I.G. U. H. Mamalgaha; Chandira Jayasekara; Y. S.
Mayuran; Chandimal Jayawarden
Idea mentioned in the paper:
Predicting the stock market with machine learning involves using
historical data, news sentiment, and various models to guess if stock prices
will go up or down. It's a complex and uncertain task, as the market is
influenced by many factors. Careful risk management and understanding
are crucial because there are no guarantees, and losses can happen.
Technology/tool used:
https://ieeexplore.ieee.org/document/9103381
LITERATURE SURVEY
Stock Market Forecasting using Machine Learning: Today and Tomorrow
Sukhman Singh; Tarun Kumar Madan; Jitendra Kumar; Ashutosh Kumar Singh
Idea mentioned in the paper:
The paper likely covers using computers for stock market predictions,
discussing techniques, data sources, sentiment analysis, and model evaluation.
It may address challenges, ethics, regulations, practical uses, risk management,
and future trends in machine learning for stock forecasting, providing insights
into this evolving field.
Technology/tool used
https://sci-hub.se/10.1109/icicict46008.2019.8993160
LITERATURE SURVEY
A LSTM-Method for Bitcoin Price Prediction: A Case Study Yahoo
Finance Stock Market
Ferdiansyah Ferdiansyah; Siti Hajar Othman; Raja Zahilah Raja Md
Radzi; Deris Stiawan
Idea mentioned in the paper
The research paper "A LSTM-Method for Bitcoin Price Prediction: A Case Study
Yahoo Finance Stock Market" likely presents a Long Short-Term Memory
(LSTM) neural network method for predicting Bitcoin prices using Yahoo
Finance data. It may discuss LSTM's ability to capture temporal patterns and
historical data for accurate price predictions, with potential insights into Bitcoin
market dynamics.
Technology/tool used
https://sci-hub.se/10.1109/icecos47637.2019.8984499
LITERATURE SURVEY
Stock Market Prediction using Machine Learning Algorithms: A
Classification Study
Meghna Misra; Ajay Prakash Yadav; Harkiran Kaur
The paper's main idea is to develop a deep learning system for predicting
stock prices of NASDAQ[National Association of Securities Dealers
Automated Quotations].-listed companies, with the aim of helping investors
maximize their profits. The system utilizes deep neural networks, including an
autoencoder for noise reduction, and incorporates advanced features and time
series data engineering. It excels at making accurate multi-step-ahead
predictions using limited historical stock data, and the predictions are utilized
not just for forecasting but also for making investment decisions aimed at
maximizing profits
Technology/tool used
https://sci-hub.se/10.1109/icrieece44171.2018.9009178
LITERATURE SURVEY
Table of Comparison/summary Idea presented
Paper Title Author Gap Identified
Naadun Sirimevan; I.G. U.
H. Mamalgaha; Chandira 1.Limited Testing Data,
Stock Market Prediction Using Machine learning predicts
Jayasekara; Y. S. 2.Time vs. Accuracy
Machine Learning Techniques stock markets using social
Mayuran; Chandimal 3.Neglect of External Factors
Jayawarden media, improving accuracy.
Stock Market Forecasting using Sukhman Singh; Tarun 1. Role of Sentiment Data
Machine Learning: Today and Kumar Madan; Jitendra Stock market prediction's
2. Dynamic Nature of Markets
Tomorrow Kumar; Ashutosh Kumar significance, machine learning's
3. Hybrid/Fusion Models
Singh role, and model challenges.
Ferdiansyah 1. Limited Testing Data
A LSTM-Method for Bitcoin Price Bitcoin's investment
Prediction: A Case Study Yahoo Ferdiansyah; Siti Hajar potential, need for automated
2. Comparison of Regression
Finance Stock Market Othman; Raja Zahilah Raja Techniques
prediction tools, LSTM-
Md Radzi; Deris Stiawan 3. Dimensionality Reduction
based forecasting.
Limited Testing Data
Stock Market Prediction using Meghna Misra; Ajay Boost stock predictions with
Comparison of Regression
Machine Learning Algorithms: A Prakash Yadav; Harkiran data-driven machine learning
Techniques
Classification Study Kaur for informed investor
Dimensionality Reduction
decisions.
EXISTING SYSTEM
Title of the system of 1ˢᵗ system
Bloomberg Terminal
Idea :
It is a powerful and specialized platform designed primarily for financial professionals,
including traders, analysts, portfolio managers, and other professionals working in the
finance and investment industry. The main idea behind the Bloomberg Terminal is to provide
users with access to a wide range of financial data, news, analytics, and trading tools in real-
time.
Drawbacks
1. Cost: One of the most significant drawbacks of the Bloomberg Terminal is its high cost.
2. Overwhelming Amount of Information: While the terminal provides an extensive amount of data
and news, this abundance of information can sometimes be overwhelming.
3. Limited Portability: The Bloomberg Terminal is typically installed on desktop computers, which
limits its portability. Users cannot access its full functionality on mobile devices or from remote
locations without special arrangements.
EXISTING SYSTEM
Title of the system of 2ⁿᵈ system
TradingView
Idea :
The primary idea behind TradingView is to offer a user-friendly and feature-rich
platform for technical analysis and trading in various financial markets, including
stocks, cryptocurrencies and more.
Drawbacks :
1. Limited Historical Data : The amount of historical price data available on
TradingView may be limited compared to more specialized data providers or platforms.
2. Delayed Data: While TradingView provides real-time data for many markets, some
exchanges or instruments may have a slight delay in data updates.
3. Chart Scaling: Users have reported occasional issues with chart scaling and price
discrepancies, especially when viewing data over extended timeframes. This can be
frustrating for traders who require precise charting accuracy.
EXISTING SYSTEM
Title of the system of 3ʳᵈ system
E*TRADE
Idea :
The primary idea behind ETRADE is to offer a user-friendly and accessible online
trading platform that empowers individuals to take control of their own
investments and make informed financial decisions.
Drawbacks
1. Complexity : E*TRADE's platform can be complex, especially for beginners. It
offers a wide range of tools and features, which may be overwhelming for new
investors.
2. Account Minimums : Some account types on E*TRADE may have minimum
balance requirements, which could be a barrier for users with limited funds.
3. Fees and Commissions: While E*TRADE has moved towards commission-free
trading for stocks and ETFs, it may still charge fees for certain services, such as
options trading, mutual funds, and broker-assisted trades.
EXISTING SYSTEM
Table of Comparison/summary
Technology Stack Features Supported
Existing System Gap Identified
used
- Comprehensive Stock
Analysis - Real-time market
Proprietary software - Financial High cost and limited
Bloomberg Terminal data sources
data - Predictive Analytics -
accessibility for individuals
Portfolio Management -
Financial News and Analysis
- Stock Analysis and Charting-
- Web technologies (HTML, Technical Indicators - Social
Limited advanced analytics No
TradingView JavaScript)- Data analytics Collaboration - Real-time
direct portfolio management
tools market data - Customizable
alerts
Frontend: HTML, CSS,
JavaScript. Backend: Java, Trading platform, research
Needs to enhance user
.NET, Python. Databases: tools, mobile apps, educational
E*TRADE Oracle, SQL Server, resources, account
experience, improve analytics,
strengthen cybersecurity.
PostgreSQL. APIs and Security management.
used.
PROPOSED SYSTEM
Stock Analysis and Prediction:
Users can search for specific stocks by name or ticker symbol.
Detailed stock profiles display historical data, technical indicators, and fundamental metrics.
Prediction of stock prices using machine learning model
Portfolio performance tracking:
Users can add, edit, or remove stocks from their portfolio.
Real-time tracking of portfolio performance, including gains/losses, diversification, and risk
assessment.
Automated portfolio rebalancing suggestions based on user-defined goals and risk
tolerance.
Third-Party Integrations:
Integration with financial data providers and APIs for real-time market data.
Finance News Feed:
A curated feed of finance news articles and market updates.
Users can filter news by relevance to their portfolio holdings.
SCOPE
While this project marks a significant milestone, it also opens the door to several avenues of
future development and expansion:
1. Stock Analysis and Prediction:
StockX allows users to access real-time and historical data for various stocks, providing
in-depth insights into stock performance.
The system offers predictive analytics to assist users in making informed decisions about
future stock price movements.
2. Portfolio Performance Tracking:
Users can create and manage portfolios, tracking the performance of their investments
over time.
3.Finance News Feed:
StockX provides a curated feed of finance news articles and market updates.
HARDWARE AND SOFTWARE
REQUIRED
Software:
A strong reliable network connection
Front-End Development-React.js, python
Back-End Development-node.js,python,mongodb
DESIGN
Flowchart :
DESIGN
Block Diagram :
IMPLEMENTATION
CONCLUSION
In the culmination of this project, we have successfully created a stock portfolio management and
prediction system that combines the principles of predictive analytics, modern portfolio
management, and a user-friendly interface. By harnessing a versatile technology stack, including
Node.js, MongoDB, Python, HTML, CSS, and React.js, we've delivered a dynamic toolset for
investors to navigate the intricate landscape of financial markets. The project aims to empower
users, from novices to seasoned investors, with data-driven insights that enhance their investment
decision-making process. The implementation of predictive models, coupled with a holistic approach
to portfolio management, sets the stage for a new era of financial decision-making, offering the
potential for optimized returns and risk mitigation.
this project not only delivers a valuable tool for investors but also paves the way for exciting
possibilities in the world of stock portfolio management and prediction. The future scope is filled
with potential for innovation and refinement, aligning the project with the ever-evolving landscape of
the financial industry.
REFERENCES
Stock Market Prediction Using Machine Learning Techniques
Stock Market Forecasting using Machine Learning: Today and Tomorrow
A Case Study Yahoo Finance Stock Market
Stock Market Prediction using Machine Learning Algorithms: A Classification Study
Thank You!
