:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

In this repository, I have developed the entire server-side principal architecture for real-time stock market prediction with Machine Learning. I have used Tensorflow.js for constructing ml model architecture, and Kafka for real-time data streaming and pipelining.

Stock Market Prediction Using Unsupervised Features

A comprehensive React-based stock market analysis dashboard that enables users to visualize and compare historical market data from multiple stocks, featuring technical indicators, trend analysis, and a customizable prediction model that supports both Yahoo Finance API data and user-imported datasets

Stock Market Analysis and Prediction is the project on technical analysis, visualization and prediction using data provided by Google Finance.

Team Buffalox8 predicts directional movement of stock prices.

Stock Market Prediction & Trading Bot using AI with a Web Interface

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.

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

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This is the code for "Stock Market Prediction" by Siraj Raval on Youtube

This is the project for deep learning in stock market prediction.

Smart Algorithms to predict buying and selling of stocks on the basis of Mutual Funds Analysis, Stock Trends Analysis and Prediction, Portfolio Risk Factor, Stock and Finance Market News Sentiment Analysis and Selling profit ratio. Project developed as a part of NSE-FutureTech-Hackathon 2018, Mumbai. Team : Semicolon

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A deep learning method for event driven stock market prediction. Deep learning is useful for event-driven stock price movement prediction by proposing a novel neural tensor network for learning event embedding, and using a deep convolutional neural network to model the combined influence of long-term events and short-term events on stock price movements

Reinforcement Learning for Stock Market Prediction

Following repo is the solution to Stock Market Prediction using Neural Networks and Sentiment Analysis

CNN for stock market prediction using raw data & candlestick graph.

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

Attempt to predict future stock prices based on Google Trends data.

This repository hosts the code for the SAMBA model, proposed in our IEEE ICASSP paper "Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price Prediction".

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

Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns. My research paper (IEEE Big Data 2018) on this can be found here: https://arxiv.org/pdf/1807.00939.pdf

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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)

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.

Stock Price Prediction using CNN-LSTM