发布分发手册¶

本页是可复制的元数据与发布草稿。仅在公开仓库、安装包、数据集、文档站与容器链接全部上线后再执行外发。所有稿件中的链接已就位：

仓库：https://github.com/HephaestLab/TraderHarness
文档站：https://hephaestlab.github.io/TraderHarness/
数据集：https://huggingface.co/datasets/ANTICH/traderharness-ashare-5y
PyPI：https://pypi.org/project/traderharness/

外发前硬门槛¶

不要只看 GitHub Release 是否存在；标签发布工作流中的 PyPI、容器和 Release job 必须全部成功。用一个没有项目源码的干净环境验证公开安装路径：

python -m pip index versions traderharness
python -m pip install --no-cache-dir "traderharness[llm,data,ui]"
traderharness --version
curl --fail https://hephaestlab.github.io/TraderHarness/robots.txt
curl --fail https://hephaestlab.github.io/TraderHarness/llms.txt
curl --fail https://hephaestlab.github.io/TraderHarness/llms-full.txt

任一命令失败时，不发布含 pip install 或“开箱即用”表述的外发稿。PyPI 首发使用 GitHub Actions Trusted Publisher 时，先在 PyPI 建立 pending publisher，并确保 owner、repository、workflow filename 与 GitHub environment 四项完全匹配。

TraderHarness 的 pending publisher 必须使用以下精确值：

PyPI project name: traderharness
Owner: HephaestLab
Repository: TraderHarness
Workflow name: release.yml
Environment name: pypi

搜索与生成式发现发布清单¶

站内技术项由文档部署流水线自动检查：唯一标题和描述、canonical、双语 hreflang、JSON-LD、站点地图、robots.txt、llms.txt / llms-full.txt，以及中文和英文核心指南的公开可访问性。部署完成后，IndexNow 会通知支持该协议的搜索引擎。

首次发布或域名迁移后仍需由站点所有者完成以下一次性操作：

在 Google Search Console 和 Bing Webmaster Tools 验证站点所有权并提交 https://hephaestlab.github.io/TraderHarness/sitemap.xml。
对中文指南 https://hephaestlab.github.io/TraderHarness/zh/guides/agent-stock-backtesting/ 使用 URL 检查工具请求抓取；英文指南同样操作。
如果后续建立可稳定访问的中国大陆镜像，再向百度搜索资源平台提交镜像站点；不要让两个站点在没有 canonical / hreflang 约束的情况下复制同一批页面。
每周记录品牌词、Agent 炒股如何回测、LLM trading agent backtesting 的展现、点击、收录状态和 AI 引用样例。至少观察 6–8 周，不用单日排名判断改版成败。
外部内容使用“问题—方法—可复现实验—局限—项目链接”的独立文章结构，不复制 README；优先争取真实讨论、复现和引用，而不是批量制造低质量外链。

GitHub 仓库元数据（建议配置）¶

描述

Leakage-resistant backtesting and deterministic replay for LLM trading agents, with A-share data and SFT trajectory export.

Topics

llm-agents, autonomous-agents, quantitative-trading, algorithmic-trading,
backtesting, trading-bot, a-share, china-stocks, benchmark, deepseek,
multi-agent, agentic-ai, synthetic-data, sft, rlhf, financial-ai,
market-simulation, fastapi, react, python

社交预览图使用 docs/assets/social-preview.png（需在仓库 Settings 手动上传，上传前目视确认 GitHub 的裁切效果）。

Show HN¶

Title

Show HN: TraderHarness – contamination-resistant backtesting for autonomous trading agents

Draft

Historical LLM trading evaluations are easy to leak: a model can recognize the date, company, or a full intraday price path. TraderHarness is an open-source A-share market environment that treats those as systems boundaries. It preloads five years of full-market data, progressively reveals 5-minute bars, anonymizes dates and companies, routes every fill through one matching path, and records full trajectories with fail-closed replay. It supports isolated agent comparisons and TradingAgents-style committees where only one executor can trade. A no-key replay and local FastAPI/React research console are included. I would especially value feedback on the masking threat model and trajectory format.

Reddit r/algotrading¶

Title

Open-source environment for auditing LLM trading agents on point-in-time A-share data

Draft

I built TraderHarness to separate the agent architecture from the historical market contract. The environment owns the portfolio and simulated clock, masks future data at each tool boundary, reveals open/close 5-minute windows progressively, and exports complete model/tool/fill trajectories. Date and company identities can be deterministically anonymized to reduce memorized-answer contamination. It is research infrastructure rather than a strategy claim; I am looking for review of execution fairness, metrics, and data integrity.

Reddit r/LocalLLaMA¶

Title

TraderHarness: turn LLM trading runs into SFT training data (full-fidelity trajectories, fingerprinted replay)

Draft

Every run in TraderHarness persists each LLM call with the complete message list, full tool schema, assistant content and reasoning, every tool call and its arguments, and phase metadata. traderharness export sft emits OpenAI-style JSONL gated by entity/date leakage checks, and replay cassettes reproduce runs deterministically with no API key. The bundled environment is a masked A-share market (five years of full-market daily + 5-minute data) with one controlled order path, so the generated trajectories come from a fair, auditable setting. Dataset: https://huggingface.co/datasets/ANTICH/traderharness-ashare-5y

X / Twitter 线程（英文）¶

1/ Historical LLM trading evals are quietly broken: the model may recognize the date, the company, even the exact price path it's being tested on. We built TraderHarness to fix that at the environment level. 🧵

2/ TraderHarness preloads 5 years of full-market A-share data, then masks every outlet: point-in-time filtering, relative dates (D+0/D-1), deterministic company pseudonyms. 5-min bars are revealed progressively — no peeking, then picking a better earlier fill.

3/ Every order goes through one path: TradingBus.place_order(). Same clock, isolated portfolios — so multi-agent comparisons are fair, and replay cassettes reproduce runs bit-for-bit with zero API keys.

4/ Bonus: every run doubles as a data synthesizer. Full-fidelity trajectories support reinforcement learning, behavior analysis, and evaluation; an optional converter emits OpenAI-style SFT JSONL. Every shared artifact is gated by leakage audits. Dataset: https://huggingface.co/datasets/ANTICH/traderharness-ashare-5y

5/ Open source (Apache-2.0), local-first, with a pixel-office research console. Repo: https://github.com/HephaestLab/TraderHarness — feedback on the masking threat model very welcome.

知乎（回答体，适配"如何评价 LLM 炒股/交易 Agent"类问题）¶

先说结论：目前大多数"LLM 交易"实验的评测结论是不可信的，因为模型很可能记得它被测试的那段历史。

一个通用大模型在 2024 年的 A 股上"回测"，它看到 600519 和 3 月的日期，就可能直接调用训练语料里的记忆——这不是交易能力，是背诵。行业里有 TradingAgents 这样的角色框架，但"把 Agent 放进历史里考试"这件事一直没有规范化的考场。

我们开源了 TraderHarness，把抗污染做成环境不变量：五年全市场真实数据预加载后，每个数据出口都过模拟时钟过滤（时点掩码）；日期变成 D+0/D-1 相对偏移；公司变成确定性伪身份（600519 → 公司-600731）；分钟线随窗口渐进揭示，决策时不可见的价格不可成交；所有订单只走一条 TradingBus 路径。

同时它也是一个数据合成器：每次模型调用的完整消息、工具 schema、推理过程全部落盘成轨迹，可用于强化学习、行为分析和评测，也可按需转换为 SFT JSONL。数据集已发 Hugging Face。

方法指南：https://hephaestlab.github.io/TraderHarness/zh/guides/agent-stock-backtesting/ 仓库：https://github.com/HephaestLab/TraderHarness（Apache-2.0，本地运行，带免密回放和像素办公室研究台）。欢迎挑刺，尤其是掩码威胁模型。

掘金 / V2EX（分享体）¶

标题

开源 TraderHarness：给 LLM 交易 Agent 一个不容易偷看答案的 A 股回测环境

正文

大多数交易 Agent Demo 把注意力放在 prompt 和角色，却没有把历史时点、分钟级可见性、公司/日期记忆污染、公平成交和完整证据链做成环境不变量。TraderHarness 用五年 A 股全市场真实数据，在每个数据出口执行时序遮罩，对日期与公司做确定性匿名化，所有订单只走 TradingBus，并保存可 replay、可审计、可导出轨迹的完整记录。同时支持独立账户的多 Agent 赛马，以及多个只读顾问 + 单一 Trader 的委员会。仓库带免 Key 回放和本地研究工作台。

GitHub: https://github.com/HephaestLab/TraderHarness Agent 炒股回测指南: https://hephaestlab.github.io/TraderHarness/zh/guides/agent-stock-backtesting/ 数据集: https://huggingface.co/datasets/ANTICH/traderharness-ashare-5y

Product Hunt¶

Tagline（60 字符内）

Contamination-resistant backtesting for LLM trading agents

Description（260 字符内）

TraderHarness is an open-source A-share market environment for evaluating LLM trading agents without data leakage: point-in-time masking, entity/date anonymization, progressive 5-minute execution, fingerprinted replay, and full-fidelity trajectories for RL and evaluation.

First comment

Hi Product Hunt! I built TraderHarness after noticing that most LLM trading evaluations can be quietly invalidated by memorization — the model recognizes the dates and companies it's tested on. TraderHarness treats that as a systems boundary: masked egress, relative dates, deterministic pseudonyms, one order path, and auditable trajectories. It's local-first research infrastructure (Apache-2.0), not a trading bot. I'd love your feedback on the masking threat model and the trajectory format. Docs: https://hephaestlab.github.io/TraderHarness/

awesome-list 投稿模板¶

每个列表一条小而实的 PR，链接到与列表范围匹配的文档页，不做绩效宣称：

awesome-llm-agents → | [TraderHarness](https://github.com/HephaestLab/TraderHarness) | Contamination-resistant A-share backtesting environment for LLM trading agents: point-in-time masking, entity/date anonymization, fingerprinted replay, trajectory export. |
awesome-ai-agents → 同上
awesome-quant → | [TraderHarness](https://github.com/HephaestLab/TraderHarness) | LLM-native A-share backtesting environment with strict point-in-time masking and deterministic replay. |
awesome-systematic-trading → 同上（链接到核心架构）
awesome-financial-ai → | [TraderHarness](https://github.com/HephaestLab/TraderHarness) | Evaluate LLM trading agents on masked five-year A-share data; records auditable trajectories for reinforcement learning and evaluation. |

HelloGitHub 投稿草稿（暂缓投递，先攒背书）¶

投递方式：在 HelloGitHub 仓库开 issue，选"项目推荐"模板。 投递时机：建议等 2-3 个 awesome-list PR 合并、star 数破百后再投，过稿率更高。

项目标题

TraderHarness：给 LLM 交易 Agent 一个无法偷看答案的 A 股回测考场

项目地址

https://github.com/HephaestLab/TraderHarness

类别

Python / 人工智能 / 量化金融

项目描述

LLM 正在走进真实交易，但"回测 LLM 交易 Agent"一直没有规范化的考场：模型可能认得考题（日期、公司、行情），记忆污染会让评测结论悄悄失效。TraderHarness 是一套抗污染的 A 股回测环境——五年全市场真实数据、每个数据出口严格时点掩码、日期与公司确定性匿名化、分钟级渐进撮合、全保真轨迹可指纹回放可审计；同时它也是一个 LLM 交易专项数据合成器，每次运行自动产出可用于强化学习、行为分析和评测的轨迹数据。

亮点

抗污染是环境不变量而非提示词约定：时点掩码 + 日期相对化（D+0/D-1）+ 公司伪身份（600519 → 公司-600731）
五年 A 股全市场真实数据：2.8 亿条 5 分钟 K 线、公告、新闻、基本面、估值、分红、沪深 300
公平撮合：分钟线随窗口渐进揭示，决策时不可见的价格不可成交，所有订单只走一条 TradingBus 路径
多 Agent 赛马 + 单执行者委员会，本地像素办公室研究台可实时观战、逐笔复盘
免密回放演示开箱即跑；完整轨迹支持强化学习、行为分析和评测，并可按需转换为 OpenAI 风格 SFT JSONL

截图

使用仓库 docs/assets/ 下的 GIF 与截图（首页 GIF + 2×2 实拍矩阵）：

https://github.com/HephaestLab/TraderHarness/blob/main/docs/assets/traderharness-demo.gif

https://github.com/HephaestLab/TraderHarness/blob/main/docs/assets/office-live.png

https://github.com/HephaestLab/TraderHarness/blob/main/docs/assets/trade-review.png

示例代码

pip install "traderharness[llm,data,ui]"
traderharness data download --full
traderharness demo        # 免密回放，开箱即看
traderharness ui          # 打开本地研究台 http://127.0.0.1:8000