StockPredictor

Maintainers

@tejaswi0910 @i-am-SnehaChauhan and @Nitya-Pasrija

Please reach out to the maintainers if you get stuck or wish to report someone.

Abstract:

Stock price prediction is a challenging task in the financial domain, driven by the complex and dynamic nature of markets. Traders, investors, and financial analysts employ a range of techniques to make informed decisions about buying or selling stocks. The goal is to capitalize on potential price changes and market opportunities. For Stock prediction, various time series models play a crucial role in capturing patterns and trends within historical stock price data. This abstract provides an overview of different time series models commonly employed in stock prediction.

AutoRegressive Integrated Moving Average (ARIMA):

ARIMA models are widely utilized for their ability to capture temporal dependencies and seasonality in time series data. By combining autoregressive and moving average components, ARIMA models are effective in forecasting stock price movements.

Generalized Autoregressive Conditional Heteroskedasticity (GARCH):

GARCH models focus on modeling volatility clustering and changes in variance over time. These models are particularly useful for understanding the intricate dynamics of stock market volatility, aiding in risk assessment and management.

Long Short-Term Memory(LSTM):

Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture that is designed to overcome the limitations of traditional RNNs in capturing long-term dependencies in sequential data.

XG-BOOST:

XGBoost, short for eXtreme Gradient Boosting, is a popular and powerful machine learning algorithm that belongs to the class of ensemble learning methods. It has gained widespread popularity and has been successful in various data science competitions, making it a go-to choice for predictive modeling tasks. XGBoost is particularly effective for structured/tabular data and has been used in a variety of applications, including regression, classification, and ranking.

Structure of the Projects 📝

This repository consists of various machine learning projects, and all of the projects must follow a certain template. I wish the contributors will take care of this while contributing to this repository.

Dataset

This folder stores the dataset used in this project. If the Dataset is not being able to upload in this folder due to the large size, then put a README.md file inside the Dataset folder and put the link of the collected dataset in it. That'll work!
Dataset can be scrapped from web sources for real-time analysis of stock price. [Stock_prediction_model_starter_notebook]

Model

This folder would have your project file (that is .ipynb file) be it analysis or prediction.

Dashboard

A finance dashboard is an interactive web application that provides real-time insights into stock prices. It fetches live data from financial markets, processes it, and presents it in a user-friendly format. [Starter_python_file_streamlit.py]

The dashboard may include features such as:

Stock Selection: Allows users to select the stocks they are interested in.
Price Charts: Displays the historical price trends of selected stocks.
Real-Time Price Updates: Shows the current price of the selected stocks, updated in real-time.
Performance Metrics: Provides key metrics such as opening price, closing price, highs, lows, and volume traded.
Predictive Analysis: Uses machine learning algorithms to predict future price trends.

Resources 📝

Hi, so this section will include amazing tutorials on different model you can use in the model and how to work with them :

Creating Dashboard:

Implementation: https://www.youtube.com/watch?v=fdFfpEtv5BU
Streamlit’s simplicity and Python’s powerful data libraries make this dashboard a robust tool for anyone interested in financial markets. It serves as a one-stop solution for tracking, analyzing, and predicting stock price movements. Please note that while such a dashboard can be a helpful tool for understanding market trends, investment decisions should always be made with caution and proper research.
Other JavaScript, React, and Python libraries can also be used

🧮 Workflow

Fork the repository
Clone your forked repository using terminal or gitbash.
Make changes to the cloned repository
Add, Commit and Push
Then in Github, in your cloned repository find the option to make a pull request
CONTRIBUTING TO THIS PROJECT
Take a look at the Existing Issues of your project and find one that interests you or create your own Issues!
Tag the repository maintainers or issue creators to assign that issue to you.
Wait for the Issue to be assigned to you after which you can start working on it.
Fork the Repo and create a Branch for any Issue you are working on.
Create a Pull Request which will be promptly reviewed and suggestions would be added to improve it.
Once your PR is approved, your changes will be merged into the project.
Add Screenshots to help us know what this Script is all about.
Repository-specific contribution information is in the respective READMEs of each repo.
Do not abuse and/or use bad language. Ensure you don't insult anyone. Be respectful and inclusive.

Do not abuse and/or use bad language. Ensure you don't insult anyone. Be respectful and inclusive. Please mention your full name on your GitHub handle to be eligible for prizes. You can take up any of the existing issues or create a new one to contribute!

Contribution period ends: 28 January 2024

How to get started?

You can refer to the following resources on Git and Github to get started and contact our Project Mentors via Discord if you have any doubts. Learn how to contribute to GDSC IGDTUW snow script of Code Projects

Watch this video to get started: https://youtu.be/HbSjyU2vf6Y?si=7WgLVkpfhuJqLwti

SnowScriptWinterOfCode / Stock-Prediction

readme