Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

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

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.

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).

基于神经网络的通用股票预测模型 A general stock prediction model based on neural networks

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

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).

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

Stock analysis/prediction model using machine learning

MambaStock: Selective state space model for stock prediction

Code release for "Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models" https://arxiv.org/abs/2402.03659

Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks

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".

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.

使用机器学习进行股票预测并指导短线(预测未来3日股价)交易。

We used Machine learning techniques to evaluate past data pertaining to the stock market and world affairs of the corresponding time period, in order to make predictions in stock trends. We built a model that will be able to buy and sell stock based on profitable prediction, without any human interactions. The model uses Natural Language Processing (NLP) to make smart “decisions” based on current affairs, article, etc. With NLP and the basic rule of probability, our goal is to increases the accuracy of the stock predictions.

This repository provides tools and workflows for stock analysis using large language models (LLMs). It combines financial data processing with advanced natural language understanding to deliver insights, trends, and predictions in the stock market.

We propose a model to analyze sentiment of online stock forum and use the information to predict stock volatility in the Chinese market. By generating a sentimental dictionary, we analyze the sentimental tendencies of each post as sentiment indicators. Such sentimental information will be fused with market data for prediction based on Recurrent Neural Networks (RNNs). We manually labeled the sentiment of forum post and make the data public available for research. Empirical evidence shows that 8 of the 10 stocks perform better with sentimental indicators.

We implement a quantum-classical hybrid QLSTM model by incorporating quantum variational layers into the classical LSTM in order to improve the efficiency and trainability of LSTM for better stock price prediction.

In Stock Market Prediction, our aim is to build an efficient Machine Learning model to predict the future value of the financial stocks of a company.Our machine learning model will be presented to retail investors with a third-party web app with the help of Streamlit.

Stock Market Prediction Using Neural Network Models (Backpropagation, RNN, RBF) Keras with Tensorflow backend

Stock and Forex market prediction using ML and time-series modelling

RNN - Stock Prediction Model using Attention Multilayer Recurrent Neural Networks with LSTM Cells

S&P 500 stock price prediction using Transformer model

A new stock trading and prediction model based on a MLP neural network utilizing technical analysis indicator values as features (using Apache Spark MLlib)

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

The Stock Price Prediction App is a Streamlit-based web application that provides users with tools to analyze historical stock price data, visualize technical indicators, and make short-term price predictions using different machine learning models.

Calculate technical indicators from historical stock data Create features and targets out of the historical stock data. Prepare features for linear models, xgboost models, and neural network models. Use linear models, decision trees, random forests, and neural networks to predict the future price of stocks in the US markets. Evaluate performance of the models in order to optimize them Get predictions with enough accuracy to make a stock trading strategy profitable.

💰 股票预测 / 量化交易比赛