Deep Trading Agent provides a complete pipeline for algorithmic trading: data ingestion, environment simulation compliant with OpenAI Gym, deep RL model training (e.g., DQN, PPO, A2C), performance visualization, backtesting on historical data, and live deployment through broker API connectors. Users can define custom reward metrics, tune hyperparameters, and monitor agent performance in real time. The modular architecture supports stocks, forex, and cryptocurrency markets and allows seamless extension to new asset classes.

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.