KCNyu/clawock
clawock
Stock investment and portfolio decision workflows for AI agents: Claude Code, Codex, OpenClaw, DeepSeek Harness (dsh plugin) or your own script. Agents argue bull vs bear on HK and US stocks; Python reconciles the book, gates risk and scores each trading call against market prices. Pluggable quant strategies, live real-money track record.
Install
npx @deepseek-ai/dsh plugin --profile web add clawock-dshRestart `dsh web` after install. Bundle APIs can change during the developer preview.
README badge
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From the README
Excerpt from KCNyu/clawock, cleaned of badges and images.
AI argues. Code settles. The losses stay on the page.
Live dashboard · Daily briefs · 简体中文
<a href="https://kcnyu.github.io/clawock/"><img src="https://raw.githubusercontent.com/KCNyu/clawock/refs/heads/master/site/assets/dashboard.gif" alt="clawock dashboard cycling through its tabs" width="820"></a>
<a href="https://kcnyu.github.io/clawock/#drill"></a>
| 162 | 1086 | 180 | 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 |
<sub>Numbers and charts refresh weekly; the live dashboard updates through the trading day.</sub>
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.
<p align="center"><img src="https://raw.githubusercontent.com/KCNyu/clawock/refs/heads/master/site/assets/rsi-loop.svg" width="1016" alt="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"></p>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.
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