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

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

Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting

Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

This is the official code and supplementary materials for our AAAI-2024 paper: MASTER: Market-Guided Stock Transformer for Stock Price Forecasting. MASTER is a stock transformer for stock price forecasting, which models the momentary and cross-time stock correlation and guide feature selection with market information.

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

Daily Stock Forecasts using Machine Learning & Python

Official code implementation of AAAI 2024 paper "StockMixer: A Simple yet Strong MLP-based Architecture for Stock Price Forecasting".

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.

Python-based stock analysis tool that combines traditional technical analysis with AI prediction capabilities. Providing comprehensive stock analysis and forecasting using K-line charts, technical indicators, financial data, and news data. With CMD/WEB/MCP supported.

The source code and data of the paper "HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information".

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).

No description available

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。

Python3 project applying Gaussian process regression for forecasting stock trends

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

Workshop to demonstrate how to apply NN based algorithms to stock market data and forecast price movements.

A IBM Developer code pattern for Watson Studio: forecast the stock market with Python Notebooks, SPSS Modeler, Data Refinery, and other Watson Studio tools.

LOBCAST is a Python-based open-source framework for stock market trend forecasting using Limit Order Book (LOB) data. 🤖📈

The official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on the qlib package.

[WWW'2024] "FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model"

Hidden Markov Model (HMM) based stock forecasting

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.

Simple stock & cryptocurrency price forecasting console application, using PHP Machine Learning library (https://github.com/php-ai/php-ml)

Implemented a system that analyses previous stock data of various companies, processes Time-Series data and aims to forecast the trends of stock in near future. Prediction is done using Supervised Learning Methods. (SVM ,Neural network)

Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

Transformer and MultiTransformer layers for stock volatility forecasting purposes

This is a project on "Stock-Market-Analysis-And-Forecasting-Using-Deep-Learning" using Pytorch, python, deep learning, gru, plotly

Paper-Reproduce: (ESWA) Forecasting the realized volatility of stock price index: A hybrid model integrating CEEMDAN and LSTM

A project using deep learning to forecast stock prices and covariance. Uses multiple threads to gather data and optimize portfolio using numerous APIs.