Stock Market Multi-Agent is an advanced open-source Python framework designed to streamline automated trading through coordinated AI agents. Each agent specializes in a specific function: Data Acquisition agents fetch and clean real-time market feeds, Signal Generation agents apply machine learning models for predictive insights, Backtesting agents rigorously evaluate strategies on historical datasets, Portfolio Management agents optimize asset allocation, Execution agents interface with brokerage APIs to place orders, and Risk Management agents enforce safeguards. The config-driven architecture allows plug-and-play modules, supporting customization of algorithms, data sources, and risk parameters. Suitable for research, live trading, and development, it accelerates quantitative strategy deployment and operational scalability.

AgentVest provides a collection of AI-driven agents that collaborate to manage investment portfolios. The DataCollector Agent ingests real-time market data from multiple sources, StrategyGenerator leverages GPT models to propose trading strategies, RiskManager evaluates strategy robustness under various scenarios, and TradeExecutor interfaces with brokerage APIs to perform trades. The framework includes memory management, tool integration, and backtesting modules, allowing developers to build, test, and deploy autonomous investment workflows.

Prosperse provides a comprehensive suite of tools for automated stock screening, real-time charting, and strategy backtesting. Utilizing advanced AI algorithms, it continuously scans the market based on user-defined conditions such as price movements and trading volumes. This allows investors to identify breakout stocks, visualize market trends, and test various investment strategies under different market conditions, making it an essential tool for anyone looking to optimize their investment portfolio.

FinAgents provides a comprehensive toolkit for designing, configuring, and executing autonomous AI agents tailored to financial tasks. By leveraging large language models and real-time market data APIs, it automates strategy backtesting, portfolio rebalancing, risk evaluation, and performance reporting. The framework offers a modular architecture with pluggable data connectors, model adapters, execution engines, and reporting modules, allowing users to mix and match components. FinAgents also includes sample agent templates, logging utilities, and deployment scripts to accelerate development and ensure reproducibility in live or simulated environments.

Financial Stock Analysis Agent leverages AI and machine learning to automate every step of stock research. It connects to financial APIs to fetch real-time price, volume, and historical data. Then it computes technical indicators (e.g., RSI, MACD, moving averages) and analyzes company fundamentals like earnings, revenue growth, and financial ratios. The agent visualizes key trends, identifies potential buy or sell signals, and evaluates risk metrics such as volatility and drawdown. Users can query the agent for customized insights, backtest strategies, and receive detailed reports in natural language or plotted charts, accelerating investment research and decision-making.