tensortrade-rl

To train reinforcement learning trading agents with TensorTrade, you build a modular TradingEnv using Observer, ActionScheme, RewardScheme, Portfolio, and Exchange, then orchestrate distributed training runs with Ray RLlib.

Yes, Ray RLlib supports distributed training for TensorTrade environments. The framework integrates Ray RLlib to scale reinforcement learning experiments across multiple compute resources for scalable trading strategy prototyping.

Setting up a TensorTrade TradingEnv requires configuring several modular components: an Observer for market data, an ActionScheme for trades, a RewardScheme for performance, plus a Portfolio and an Exchange.

Reinforcement learning applies to trading strategy prototyping by allowing an agent to learn optimal buy, sell, or hold actions through a RewardScheme based on Portfolio performance within a simulated financial Exchange environment.

Yes, Python 3.12 or higher is required to run TensorTrade reinforcement learning training. You must also install the package locally using pip install -e . along with any specified data and environment dependencies.

The best way to scale reinforcement learning trading experiments is using Ray RLlib for distributed training. It orchestrates TensorTrade environments across clusters, enabling researchers to run scalable financial strategy evaluations.