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

Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

Use unsupervised and supervised learning to predict stocks

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

Stock Price Prediction using Machine Learning Techniques

Strategies to Gekko trading bot with backtests results and some useful tools.

📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.

Stock Trading Bot using Deep Q-Learning

Portfolio optimization with deep learning.

Courses, Articles and many more which can help beginners or professionals.

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

This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).

Introducing neural networks to predict stock prices

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.

Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.

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

Backtrader-powered backtesting framework for algorithmic trading, featuring 20+ strategies, multi-market support, CLI tools, and an integrated MCP server for professional traders.

Stock price prediction with LSTMs in TensorFlow

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

多因子指数增强策略/多因子全流程实现

Programs for stock prediction and evaluation

Simple to use interfaces for basic technical analysis of stocks.

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

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

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).

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