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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
An open source reinforcement learning framework for training, evaluating, and deploying robust trading agents.
| Date | Stars |
|---|---|
| 2026-07-31 | 6605 |
| 2026-08-01 | 6607 |
| 2026-08-02 | 6608 |
| 2026-08-03 | 6609 |
| 2026-08-04 | 6611 |
| 2026-08-05 | 6614 |
| 2026-08-06 | 6614 |
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<p align="center"> <img src="docs/source/_static/logo.jpg" width="200" alt="TensorTrade Logo"> </p> # TensorTrade **Train RL agents to trade. Can they beat Buy-and-Hold?** [](https://github.com/tensortrade-org/tensortrade/actions/workflows/tests.yml) [](https://tensortrade.org) [](http://www.apache.org/licenses/LICENSE-2.0) [](https://discord.gg/ZZ7BGWh) [](https://www.python.org/downloads/release/python-3120/) TensorTrade is an open-source Python framework for building, training, and evaluating reinforcement learning agents for algorithmic trading. The framework provides composable components for environments, action schemes, reward functions, and data feeds that can be combined to create custom trading systems. ## Quick Start ```bash # Requires Python 3.12+ python3.12 -m venv tensortrade-env && source tensortrade-env/bin/activate pip install -e . # For training with Ray/RLlib (recommended) pip install -r examples/requirements.txt # Run training python examples/training/train_simple.py ``` ## Documentation & Tutorials 📚 **[Tutorial Index](docs/tutorials/index.md)** — Start here for the complete learning curriculum. ### Foundations - [The Three Pillars](docs/tutorials/01-foundations/01-three-pillars.md) — RL + Trading + Data concepts - [Architecture](docs/tutorials/01-foundations/02-architecture.md) — How components work together - [Your First Run](docs/tutorials/01-foundations/03-your-first-run.md) — Run and understand output ### Domain Knowledge - [Trading for RL Practitioners](docs/tutorials/02-domains/track-a-trading-for-rl/01-trading-basics.md) - [RL for Traders](docs/tutorials/02-domains/track-b-rl-for-traders/01-rl-fundamentals.md) - [Common Failures](docs/tutorials/02-domains/track-b-rl-for-traders/02-common-failures.md) — Critical pitfalls to avoid - [Full Introduction](docs/tutorials/02-domains/track-c-full-intro/README.md) — New to both domains ### Core Components - [Action Schemes](docs/tutorials/03-components/01-action-schemes.md) — BSH and order execution - [Reward Schemes](docs/tutorials/03-components/02-reward-schemes.md) — Why PBR works - [Observers & Feeds](docs/tutorials/03-components/03-observers-feeds.md) — Feature engineering ### Training - [First Training](docs/tutorials/04-training/01-first-training.md) — Train with Ray RLlib - [Ray RLlib Deep Dive](docs/tutorials/04-training/02-ray-rllib.md) — Configuration options - [Optuna Optimization](docs/tutorials/04-training/03-optuna.md) — Hyperparameter tuning ### Advanced Topics - [Overfitting](docs/tutorials/05-advanced/01-overfitting.md) — Detection and prevention - [Commission Analysis](docs/tutorials/05-advanced/02-commission.md) — Key research findings - [Walk-Forward Validation](docs/tutorials/05-advanced/03-walk-forward.md) — Proper evaluation ### Additional Resources - [Experiments Log](docs/EXPERIMENTS.md) — Full research documentation - [Environment Setup](docs/ENVIRONMENT_SETUP.md) — Detailed installation guide - [API Reference](https://www.tensortrade.org/en/latest/) --- ## Research Findings We conducted extensive experiments training PPO agents on BTC/USD. Key results: | Configuration | Test P&L | vs Buy-and-Hold | |---------------|----------|-----------------| | Agent (0% commission) | +$239 | +$594 | | Agent (0.1% commission) | -$650 | -$295 | | Buy-and-Hold | -$355 | — | The agent demonstrates directional prediction capability at zero commission. The primary challenge is trading frequency—commission costs curren
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ae1bcf48a8a91908, desc:reinforcement learning