What is QuantRL-Lab CLAUDE.md?

A repository guide for a Python research project using uv for dependencies, pytest for tests, and pre-commit for code checks.

Setting up the environment, installing optional machine-learning or notebook tools, running selected tests, measuring coverage, and checking code quality.

It shows developers which dependency groups and test commands to use, including the difference between local unit tests and integration tests that need API keys.

How whanyu1212/QuantRL-Lab configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to whanyu1212/QuantRL-Lab. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/whanyu1212/QuantRL-Lab/main/CLAUDE.md

Clone the repo

git clone --depth 1 https://github.com/whanyu1212/QuantRL-Lab

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/instructions/whanyu1212/quantrl-lab/claude-md/github.svg)](https://agentmods.dev/instructions/whanyu1212/quantrl-lab/claude-md)

Your own site

<a href="https://agentmods.dev/instructions/whanyu1212/quantrl-lab/claude-md"><img src="https://agentmods.dev/badge/instructions/whanyu1212/quantrl-lab/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/instructions/whanyu1212/quantrl-lab/claude-md"><img src="https://agentmods.dev/badge/instructions/whanyu1212/quantrl-lab/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 2,870 This file is loaded in full into every session.

When invoked 2,870 The same file — it is already loaded in full.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.02870	$0.02870
Opus 5.5	$0.01148	$0.01148
Sonnet 5.5	$0.00574	$0.00574
Haiku 4.5	$0.00287	$0.00287

Measured 2mo ago against content hash fce87a7f5817, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

QuantRL-Lab CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

CLAUDE.md · 263 lines

How it starts

The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Essential Commands

Package Management

This project uses uv (not Poetry). The lock file is uv.lock.

uv sync                                    # Core deps only
uv sync --extra dev --extra notebooks      # With dev/notebook extras
uv sync --all-extras                       # All optional features
source .venv/bin/activate

Optional dependency groups: dev, notebooks, ml (torch/transformers/litellm), tuning, viz, storage, full.

Testing

uv run pytest -m "not integration"         # Unit tests only (CI/CD - no API keys needed)
uv run pytest -m integration               # Integration tests (requires .env with API keys)
uv run pytest tests/data/test_indicators.py  # Specific test file
uv run pytest -k "test_portfolio"          # Tests matching pattern
uv run pytest --cov=quantrl_lab            # With coverage

Code Quality

Pre-commit hooks run automatically on git commit. Run manually with:

pre-commit install                         # One-time setup
pre-commit run --all-files
pre-commit run --files path/to/file.py    # Before committing changes

Hook configuration (.pre-commit-config.yaml):

Black: --line-length=120 --skip-string-normalization
isort: --profile black
flake8: --max-line-length=120
docformatter: Google-style, --wrap-summaries=72

Always run pre-commit run --files <changed_files> after significant changes. Common failures: unused imports (flake8), line length violations, missing/malformed docstrings.

Documentation

uv run mkdocs serve    # Local preview
uv run mkdocs build    # Build (always run before committing API changes)

When renaming modules or changing public APIs, update docs/api-reference/ to match actual module paths and run mkdocs build to verify.

Standalone guide tabs (top-level nav, each a self-contained reference):

docs/DATA_SOURCES.md — data loaders, capability matrix, usage per source
docs/data-processing.md — DataProcessor and DataPipeline with all 7 pipeline steps
docs/environments.md — action/observation/reward spaces, all reward strategies, full env example
docs/experiments.md — BacktestRunner, ExperimentJob, JobGenerator, AgentExplainer, OptunaRunner

Read the full file on GitHub · 263 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 263 lines · 2,870 tokens per session scan A fce87a7f5817

Subscribe to this mod's changes

QuantRL-Lab CLAUDE.md is an instructions file published in the GitHub repository whanyu1212/QuantRL-Lab (54 stars, last pushed 6mo ago), licensed MIT. It adds 2,870 tokens to every session, about $0.0115 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

PyTorch Scientific algorithmic-trading deep-reinforcement-learning fintech rl

QuantRL-Lab CLAUDE.md next to 6 instructions from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
QuantRL-Lab CLAUDE.mdwhanyu1212/QuantRL-Lab	Instructions file	54	6mo ago	MIT	A	2,870	Claude Code
vscode buildNext.instructions.mdmicrosoft/vscode	Instructions file	195k	today	MIT	A	9,719	GitHub Copilot
vscode oss-third-party-notices.instructions.mdmicrosoft/vscode	Instructions file	195k	today	MIT	A	5,516	GitHub Copilot
next.js AGENTS.mdvercel/next.js	Instructions file	143k	today	MIT	A	7,461	Codex, OpenCode
langchain AGENTS.mdlangchain-ai/langchain	Instructions file	149k	today	MIT	A	4,680	Codex, OpenCode
vscode copilot-instructions.mdmicrosoft/vscode	Instructions file	195k	today	MIT	A	3,093	GitHub Copilot
vscode model-prompts.instructions.mdmicrosoft/vscode	Instructions file	195k	today	MIT	A	2,022	GitHub Copilot

QuantRL-Lab: Instructions file for Claude Code