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

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

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.

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)

Astock

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

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

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

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 …

Deep Trading using Convolutional Neural Network

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

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.

Python command-line program that leverages the user's Robinhood account to assist in choosing options to perform the wheel strategy. This is done by utilizing a delta-based risk assessment and listing qualifying weekly options in order of potential profit within price range.

Stock market prediction using LSTM

End to End self case study from Data Collection to Optimization for Deployment

This is the the final project of the course: L330 Data Science: principles and practice at the University Of Cambridge. The task for this project is stock market prediction using a diverse set of variables. In particular, given a dataset representing days of trading in the NASDAQ Composite stock market, our aim is to predict the daily movement o…

Stock market prediction of a stock using stacked LSTM