Stock Price Prediction using Machine Learning Techniques

Use NLP to predict stock price movement associated with news

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

A comprehensive dataset for stock movement prediction from tweets and historical stock prices.

A stock trading bot that uses machine learning to make price predictions.

OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

Top paper collection for stock price prediction, quantitative trading. Covering top conferences and journals like KDD, WWW, CIKM, AAAI, IJCAI, ACL, EMNLP.

Stock price prediction with LSTMs in TensorFlow

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).

Stock price prediction with recurrent neural network. The data is from the Chinese stock.

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.

Code for stock movement prediction from tweets and historical stock prices.

Use BPNN and LSTM to forecast stock price. 使用BP神经网络和LSTM预测股票价格，注释拉满。

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

Plain Stock Close-Price Prediction via Graves LSTM RNNs

Stock Price Prediction case study using Keras

Pull stock prices from online API and perform predictions using Long Short Term Memory (LSTM) with TensorFlow.js framework

Final Year B.tech Project on Machine Learning Stock Prediction through Deep Learning

Predicting stock price using historical data of a company, using Neural networks (LSTM).

Stock price prediction using LSTM and 1D Convoltional Layer implemented in keras with TF backend on daily closing price of S&P 500 data from Jan 2000 to Aug 2016

Data and Notebook for the Stock Price Prediction Tutorial

Stock price prediction using a Temporal Fusion Transformer

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".

Try to predict stock price with LSTM、GAN and DRL, exploring the features of news and technical indicators，which help improving perfomance of predictions.

Stock Price Prediction

This project is about predicting stock prices with more accuracy using LSTM algorithm. For this project we have fetched real-time data from yfinance library.

Stock Price Prediction Based on Internet Public Opinion Analysis

Code release for "Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction" https://arxiv.org/abs/2309.00073

Improve S&P 500 stock price prediction (random forest and gradient boosting trees) with time series similarity measurements: DTW, SAX, co-integration, Euclidean and Pearson.

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