🐗 🐻 Deep Learning based Python Library for Stock Market Prediction and Modelling

Web app to predict closing stock prices in real time using Facebook's Prophet time series algorithm with a multi-variate, single-step time series forecasting strategy.

Astock

Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.

Repository for Going Deeper with Convolutional Neural Network for Stock Market Prediction

Reproduce research from paper "Predicting the direction of stock market prices using random forest"

Stock Prediction System is a ML based website designed using Django's Framework and CSS's BootStrap Framework (NOTE: ALL THE DEPLOYMENTS ARE CURRENTLY DOWN)

Reinforcement Learning for Stock Market Prediction

Code implementation of "SENN: Stock Ensemble-based Neural Network for Stock Market Prediction using Historical Stock Data and Sentiment Analysis"

This repository hosts a stock market prediction model for Tesla and Apple using Liquid Neural Networks. It showcases data-driven forecasting techniques, feature engineering, and machine learning to enhance the accuracy of financial predictions.

In this repository i created many data scince - machine learning projects like(Deep dream,weather prediction,Movie recommender system etc) with code & datasets

A collection of notebooks and different prediction models that can predict the stock prices. Also a comparison of how all these models performed.

Deep Trading using Convolutional Neural Network

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 …

Model news data in short, medium and long term for stock price trend prediction

In this work an application of the Triple-Barrier Method and Meta-Labeling techniques is explored with XGBoost for the creation of a sentiment-based trading signal on the S&P 500 stock market index. The results confirm that sentiment data have predictive power, but a lot of work is to be carried out prior to implementing a strategy.

Stock Market Prediction on High-Frequency Data Using soft computing based AI models

Developed ML/DL based a web application for stock price prediction based on real-time data.

Stock market prediction of a stock using stacked LSTM

Stock prediction via sentiment analysis of financial news. Winner of VITHack 2020 in FinTech Domain