A Python progamm that determines whether a stock has potential to increase its price based on Sentimental Analysis and recent stock patterns/growth.

AI-based Portfolio Management System is designed to help individuals make data-driven investment decisions by leveraging algorithms and statistics.

[MDAI 2024] The official repo for the paper: "Transforming Stock Price Forecasting: Deep Learning Architectures and Strategic Feature Engineering".

TrendSage aims to develop a correlation between stock and cryptocurrency predictions based on the posts or comments on Reddit and the actual market trends.

Template Implementation of an RNN with LSTM units for stock price prediction

A timeseries forecasting project on stock market data using GRU.

A collection of information for quantitative finance, insurance, mathematics and AI

This repository contains the code for the Stock Market Predictor online application, which is aimed to provide users with predictive modeling skills for stock price predictions. The application integrates LSTM (Long Short-Term Memory) models and Streamlit to offer a simple interface for obtaining historical stock data and visualizing patterns.

Stock price prediction project for MCSE thesis.

Stock Price Prediction and Portfolio Return Maximization

Professional Backtesting Engine for crypto, stocks and forex

This repository focuses on predicting Apple Inc.'s stock prices using time series analysis from 2013 to 2018. The project involves data exploration, stationarity analysis, time series decomposition, model development, and generating insights to assist investors.

This project focuses on predicting the future stock prices of Microsoft based on historical data spanning four years. The goal is to forecast the stock prices for the next 90 days

Using LSTM to predict stock price movement for S&P500

Implement AI Trading Strategies with Backtrader

S&P 500 stock price prediction using Transformer model

A machine learning project for classifying stocks into their respective sectors using historical stock price data. Implemented in a Jupyter Notebook, the project includes data fetching, preprocessing, feature extraction, model training, and prediction.

This web app uses LSTM (long short term memory) model to predict the stock prices.

A project featuring exploratory data analysis (EDA) and machine learning applications for S&P 500 stock data, utilizing Python and relevant libraries.