Simple Stock Investment Recommendation System based on Machine-Learning algorithms for prediction and Twitter Sentiment Analysis.

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 Prediction System is a ML based website designed using Django's Framework and CSS's BootStrap Framework (NOTE: ALL THE DEPLOYMENTS ARE CURRENTLY DOWN)

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)

The project focused on the use of public data to assess the economic situation in the country based on the state of the stock market and national means of payment, in particular - of the national currency. As sources are used: Open data Ministry of Finance of the Russian Federation These Moscow Exchange Google Finance Data Technologies used: Backend: Databases (relational) - Microsoft SQL Server 2014 Databases (multivariate) models DataMining, OLAP-cube - Microsoft Analysis Services 12.0 Веб-сервер - Windows Server 2012 / Internet Information Services Самописный ASP.NET HTTP Restful интерфейс для взаимодействия с Frontend ETL (загрузка и пре-процессинг данных, управление обновлением данных) SQL Server Integration Services 2014 (разработка в Visual Studio 2013, SSDT) Frontend: AngularJS ChartJS Twitter Bootstrap These were chosen so that the detail (granularity) in the set is not less than 1 day. The result has been created and filled with data analytic repository (Kimball model, topology - star), which was used to build a multi-dimensional databases and OLAP-based cubes on it, as well as models of analysis of data on two main algorithms: Microsoft Time Series, Microsoft Neural Network . To ensure interoperability frontend and backend server for backend-server was set up HTTP-Restful interface JSON-issuing documents in the form of finished sets. The project includes two main areas: Intelligent visualization of open data Analysis of open data and the construction of forecasts based on them Intelligent visualization involves the use of MDX-queries to the OLAP-cube, followed by depression (drilldown) in the data, the system allows the user to quickly find the "weak points" of the economy, as part of the data collected. To predict the time a standard mix of algorithms ARTXP / ARIMA, without the use of queries involving cross-prediction (but it is possible to enroll in the system correct data). These algorithms have been tested primarily on foreign exchange rates (US dollar) and the assets of banks included in the special list of Ministry of Finance. In addition, for assets shows the different customization options algorithms - a long-term, short-term and medium-term (balanced) plan. Assessing the impact of oil prices and foreign currency exchange rate for the total market capitalization was conducted on a sample of the data collected: companies with a total market capitalization of 100 to 500 million rubles, present in the market during 2013-2015 Analytical server builds the neural network receiving the input exchange rates, companies, the weighted average share price, total capitalization of the company and the price of oil to requests received models give the opportunity to evaluate the growth rate of \ fall (if at all) the company's capitalization at historical exchange rates and / or the cost of oil. Built a system can expand to include new indicators, which will significantly increase the accuracy of forecasting.

A Stock Price prediction system using LLM and Multi-agent-system

Real-time stock discussion platform with accuracy-based reputation system, time-expiring Q&A, and WebSocket-powered predictions.

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This repository contains an automated trading system using machine learning for stock return prediction, position sizing and generating exit rules. This project partly makes up my master's thesis.

🤖AI Agents for Financial Trading💰: LLM-Driven Stock Prediction & Investment Recommendation System

股市情報系統

Premium Markets is an automated stock markets technical analysis system. It implements a graphical environment for monitoring and tailoring stock markets technical analysis major indicators and oscillators, a portfolio management interface, historical data charting and buy and sell signals. See Premium Markets web portal at http://premiummarkets.elasticbeanstalk.com for more information and a free workable demo of the forecast prediction engine based upon neural networks.

AchillesV1 is an AI-powered trading bot designed to automate stock market investments. In a demo account, this system achieved an 182% return on investment by leveraging deep learning market predictions and sentiment analysis from real-time news. This solution can be expanded to Forex, Indices, and Crypto markets, enabling automated high-frequency

Complete software system for real time stock price prediction.

Advanced ML system for stock market prediction with real-time data and multiple algorithms

A hybrid AI-based stock market prediction system using LSTM, Random Forest, and XGBoost, built for real-world deployment with Optuna-powered tuning, feature-rich engineering, and ensemble prediction logic. Designed to optimize F1 score and accuracy, this system aims to generate reliable buy/sell signals on stocks.

一个根据量化交易系统策略改造的短期股市趋势预测系统,内核与主功能分开设计,方便添加自定义内核.(A short-term stock market trend prediction system transformed according to the quantitative trading system strategy, the core is designed separately from the main function, which is convenient to add a custom core.)

a stock prediction system

This is a Stock Market predictor, an Expert System using Candlestick chart that predicts when to invest and withdraw stocks from the market.

ST system gives stock price prediction based on results from a training phase, it searches through a large space of models and hyper-parameters, and present testing and validation results for any user selected symbol.

This is a research project supervised by professor Richard Sinnott - Chinese stock prediction system based on two models. Model 1 is based on stock indicators with LSTM; Model 2 on stock-affecting news with BERT WWM.

System for stock prediction, analysis and investment.

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Stock Market Prediction system in Python and Java (Web based desktop application)

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An intelligent, LLM-powered inventory management system leveraging Google's Gemini Pro and PandasAI. Features Natural Language to SQL generation, interactive dashboards, automated stock predictions, and seamless Excel integration. Built with Streamlit and Python.

基于 Flask 的股票分析/预测管理系统，提供股票历史行情获取、技术指标可视化、综合评分与分析报告展示等功能。

Machine learning has significant applications in the stock price prediction. In this machine learning project, we will be talking about predicting the returns on stocks.

Machine Learning system combining LSTM neural networks and BERT-based sentiment analysis for stock market prediction. Achieved 0.002 MAE with LSTM and 90% accuracy with NLP model, improving trading returns by 10%.

The aim of the problem is to develop a forecasting model to predict a stock's short-term price movement. The use of such prediction models is widely prevalent in algorithmic trading. Algorithmic trading, sometimes referred to as high-frequency trading in specific circumstances, is the use of automated systems to identify true(money making) signals among massive amounts of data that capture the underlying stock dynamics. These models can be leveraged to develop profitable trading strategies(akin to hedge funds) to help investors/traders achieve better returns. Contestants are expected and encouraged to think of empirical models/heuristics in order to better predict the price evolution of the hypothetical stock