Welcome to the project! If you're interested in contributing, please fill out the contribution form.

🚩 Table of Contents

1. Introduction
2. What are we doing
3. Base Research Papers
4. Technology Stack
   • Programming Language
   • Editor
   • IDE
   • Libraries
   • Dataset
5. Further Reading
6. Screenshots
7. Results
8. Team
9. License
10. Contribution Form

Welcome to the exciting world of stock market prediction using machine learning in the context of the Nepali stock market. In this project, we explore the application of advanced machine learning techniques to analyze and predict trends in the Nepali stock market. The use of machine learning allows us to leverage historical stock data, identify patterns, and make informed predictions, contributing to more informed investment decisions.

Our primary objective is to develop and implement machine learning models that can predict stock prices in the Nepali market. By harnessing the power of data and advanced algorithms, we aim to provide valuable insights to investors, traders, and financial analysts. The project involves exploring various machine learning models, fine-tuning them, and evaluating their performance on real-world Nepali stock data.

• Title: Predicting stock and stock price index movement using Trend 4 Deterministic Data Preparation and machine learning techniques
• Authors: Jigar Patel, Sahil Shah, Priyank Thakkar, K. Kotecha

• Title: A Review of Stock Market Prediction with Artificial Neural Network (ANN)
• Authors: Chang Sim Vui, Gan Kim Soon, Chin Kim On, and Rayner Alfred

• Title: Stock Market Prediction Using Artificial Neural Networks
• Authors: Bing Yang, Hao Jiankun, Zhang Sichang

• Title: Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indices
• Authors: Wensheng Dai, Jui-Yu Wu, Chi-Jie Lu

• Title: Evaluating multiple classifiers for stock price direction prediction
• Authors: Michel Ballings, Dirk Van den Poel, Nathalie Hespeels, Ruben Gryp

• Title: Applying Artificial Neural Networks to prediction of stock price and improvement of the directional prediction index – Case study of PETR4, Petrobras, Brazil
• Authors: Fagner A. de Oliveira, Cristiane N. Nobre, Luis E. Zárate

• Title: Empirical analysis: stock market prediction via extreme learning machine
• Authors: Xiaodong Li, Haoran Xie, Ran Wang, Yi Cai, Jingjing Cao, Feng Wang, Huaqing Min, Xiaotie Deng

... (continue the list with the remaining papers)

Python3.6 is the programming language used in the experiment.

For code editing and creating files, we are using the following editors:

• Visual Studio Code
• Sublime Text 3

For development, training, and deployment of the models, we are using Jupyter Notebook along with Anaconda Integrated Development Environment.

• Numpy
• Pandas
• scikit-learn
• matplotlib
• keras
• tensorflow

• Nabil Bank Limited
• Nic Asia Bank Limited.
• NMB Bank
• Nepsealpha
• Sharesanshar

• Naive Bayes
• Support Vector Machine
• Random Forest
• Artificial Neural Network
• XGBoost
• Long Short Term Memory

Mausam Gurung

This software is licensed under the MIT License

Wanna contribute to this project? Fill out the contribution form by clicking here