tensortrade-rl

Train reinforcement learning trading agents with TensorTrade and Ray RLlib.

10|2|Updated Mar 8, 2026

Search Tags:#environment #trading #ray #reinforcement-learning #rlib #tensortrade

Ready to run

npx skills add https://github.com/mahmoud20138/Tradecraft --skill tensortrade-rl

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tensortrade-rl
Source: https://github.com/mahmoud20138/Tradecraft/tree/main/plugins/tradecraft/skills/tensortrade-rl
Command: npx skills add https://github.com/mahmoud20138/Tradecraft --skill tensortrade-rl

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a modular framework to design and train reinforcement learning trading agents using TensorTrade, enabling researchers to prototype strategies quickly.

Core Features & Use Cases

Modular TradingEnv with Observer, ActionScheme, RewardScheme, Portfolio, and Exchange.
Distributed training with Ray RLlib for scalable experiments.
End-to-end workflow from environment design to training and evaluation on financial data.

Quick Start

Install the package, set up your trading environment, and start an RL training run.

Frequently Asked Questions about tensortrade-rl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I train reinforcement learning trading agents using TensorTrade?▼

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.

Can I use Ray RLlib for distributed training of TensorTrade environments?▼

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.

What components do I need to set up a TensorTrade TradingEnv?▼

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.

How does reinforcement learning apply to trading strategy prototyping?▼

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.

Do I need Python 3.12 to run TensorTrade reinforcement learning training?▼

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.

What is the best way to scale reinforcement learning trading experiments?▼

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.