Metadata-Version: 2.4
Name: rl-pricing
Version: 0.5.0
Summary: A reproducibility lab for trading AI: causal backtests, a live forward-test track and a pre-open decision ledger for RL, LLM and forecasting policies.
Home-page: https://github.com/BichengWang/RL-pricing
Author: Bicheng Wang
License-Expression: MIT
Project-URL: Source, https://github.com/BichengWang/RL-pricing
Project-URL: Issues, https://github.com/BichengWang/RL-pricing/issues
Project-URL: Leaderboard, https://github.com/BichengWang/RL-pricing/blob/main/leaderboard/LEADERBOARD.md
Project-URL: Paper, https://arxiv.org/abs/2609.31870
Keywords: reinforcement learning,portfolio management,backtesting,benchmark,leaderboard
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.24
Requires-Dist: pandas>=2.0
Requires-Dist: stockstats>=0.6.2
Requires-Dist: yfinance>=0.2.40
Requires-Dist: matplotlib>=3.7
Requires-Dist: gymnasium>=0.29.1
Requires-Dist: stable-baselines3>=2.3.2
Requires-Dist: torch>=2.0
Provides-Extra: test
Requires-Dist: pytest>=7.0; extra == "test"
Provides-Extra: lint
Requires-Dist: ruff>=0.5; extra == "lint"
Dynamic: author
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# RL-pricing

A reproducibility lab for trading AI. RL-pricing re-tests published claims
about deep RL agents, LLM trading agents and forecasters under one
pre-registered protocol. This package holds the lab's instruments:

- `rl-pricing bench`: a causal backtesting benchmark. Policies decide at the
  close and fill at the next open with fees; learned policies run over
  several seeds; frozen policies are scored on a live forward-test track; and
  a decision ledger records LLM agents' trades before each US market open.
- `rl-pricing --mode train|backtest|ensemble|lstm`: the share-trading toolkit
  that trains and backtests Stable-Baselines3 agents (A2C, PPO, DDPG, TD3,
  SAC), a rolling ensemble and an LSTM forecaster on daily equity data.

```bash
pip install rl-pricing
rl-pricing bench tasks             # list the benchmark tasks
rl-pricing bench data us-sectors   # download a task's data and print its hash
```

The import package is `rl`, so this distribution can't share an environment
with the unrelated `rl` (GNU Readline bindings) distribution. Leaderboard
submissions, results and reports live in the
[repository](https://github.com/BichengWang/RL-pricing): clone it to run the
submissions, or pass `--submissions-root` to use your own.

- [Verdicts, findings and leaderboard](https://github.com/BichengWang/RL-pricing#readme)
- [Paper: Wang & Zhang, arXiv:2609.31870](https://arxiv.org/abs/2609.31870)

The training toolkit's environment, preprocessing and training code are
adapted from [FinRL](https://github.com/AI4Finance-Foundation/FinRL), which is
MIT-licensed.
