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clawock

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kcnyu/clawock

AI 辩论决策、代码落账的港美股券商插件:每笔交易须经多代理辩论,模型无法触碰结算代码

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$ dsh plugin add clawock

安装

dsh 没有统一的安装命令——把该插件 README 中的配置行加入你的 profile/patch 配置,然后重启即可。

了解安装方式

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Python

语言

MIT

许可证

2026-05-16

创建于

2026-11-04

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README

clawock

AI argues. Code settles. The losses stay on the page.

PyPI npm Tests Live Data Coverage License

Live dashboard  ·  Daily briefs  ·  简体中文

clawock — portable investment decision workflows for any external AI agent, proven on a live HK and US desk

clawock dashboard cycling through its tabs

US book and Hong Kong book, side by side: each book's return, the basis that return is divided by, and its daily P&L curve in its own currency; the combined return sits underneath on a mixed basis

169 1149 192 48 5 0
days live on a real HK + US account decisions on the public ledger episodes settled by code data modules across 8 layers agent harnesses, one contract scores the model wrote for itself

Numbers and charts refresh weekly; the live dashboard updates through the trading day.

What you get

clawock helps your AI agent analyze your Hong Kong or US holdings and write a trading plan: buy, sell or wait, the case on both sides, and the prices that would change the call. You place the orders. Code later checks each call against market prices and saves the result for the agent to read next time.

clawock on one page: eight source layers arrive through fallback routes; Python reconciles the book and certifies one context pack; Claude Code, Codex, OpenClaw, DeepSeek Harness or your own CLI argues a bull and a bear case over it; code reviews concentration and enforces the configured risk boundaries, records what happens when one breaks and enforces evidence requirements; the plan reaches you and you place the orders; code settles each call as a win, a loss or ungradeable; the record returns around two loops, one into the next judgment through history and earlier-date calibration, one into the evidence rules through a bounded proposal and a named review; on the author’s host patrol rounds, a filing gate, background agents and required CI maintain the desk’s own code, watched from DeepSeek Harness

That loop is what RSI, Recursive Self-Improvement, means here. Saved evidence and results give the agent something to review before its next decision. The author's daily jobs add connected data feeds, a scheduler and accumulated history; a new installation brings its own, or starts from the single-run example.

[!IMPORTANT] This is not a promise of returns. The calls that told the author to act have not been shown to beat a coin flip.

One real call runs through this page: Goldwind (02208, Hong Kong) on the morning of 2026-10-08. The plan said keep holding and watch, add only above 8.97. The ledger records loss because the price fell by the next session's close; that scores the holding judgment, not a purchase at 8.97.

The plan's indicators came from plugins. A strategy may search for better settings of its own, but adopting them requires a person's review. Try it in five minutes.

Everything that computes is a plugin; what a result may do is not

在 GitHub 阅读完整 README →

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