Day Trading Agents provides a comprehensive suite of AI-powered modules that automate the entire day trading workflow. The platform continuously ingests tick-level market data and applies machine learning models to identify entry and exit points. It features backtesting utilities that simulate performance over historical timeframes, risk management engines for dynamic position sizing and drawdown control, and live execution adapters that connect to brokerage APIs such as Interactive Brokers and Alpaca. Custom strategy components can be written in Python, allowing traders to incorporate technical, fundamental, or sentiment-based indicators. With a modular architecture, users can mix and match data preprocessors, predictive models, and execution strategies to fine-tune performance and minimize latency. The system also logs detailed trade metrics for performance analysis and iterative improvement.

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.