globalpercent

> 📦 项目主页：https://github.com/simonlin1212/globalpercent — 更新、反馈、支持作者 > > 作者：Simon 林 · X [@linsizhen](https://x.com/linsizhen) · 邮箱：simonlin0423@gmail.com # GlobalPercent — a global-macro-probability panel for your research system This skill packages everything needed to build a panel that reads **prediction-market probabilities** from **Polymarket + Kalshi** (both public, read-only, no account) and presents them as a **macro sentiment thermometer** grouped by module. It is NOT a single API call. The value is in the *system*: classification, dedup, multi-source merge, snapshot caching, async refresh, translation, and a dozen non-obvious gotchas that each cost real debugging time. All of that is captured here so you don't rediscover it. ## The one idea A prediction market's **price IS a probability**. A contract trading at $0.62 means the market collectively bets 62% the event happens. Reading those numbers is free, needs no wallet or account — only *trading* does. So you can pull a global, money-backed sentiment read on Fed decisions, geopolitics, AI milestones, etc. for zero cost. **Use it as a thermometer, not a trading tool.** 70–84% of prediction-market traders lose money; the edge belongs to HFT market-makers, not "AI that predicts well." Frame the panel as *"what is the market's mood?"*, never as a buy/sell signal. ## When to use this skill - Building an "event probability" / "sentiment" / "macro mood" dashboard panel - Integrating Polymarket or Kalshi probability data into any app - Anyone asks for prediction-market odds, election/Fed/geopolitics probabilities as data ## What you're building (architecture in one breath) ``` Polymarket Gamma API ─┐ ├─► per-source fetchers shape every market into ONE common schema Kalshi events API ────┘ (question, prob_yes, change_24h, volume_24h, source, …) │ shared taxonomy classifies each into a module (货币政策/宏观/地缘/政治/股指大宗/AI + reference group) │ aggregator: merge → group by module → cap floods → translate titles → pin a disk SNAPSHOT │ /pulse/overview (instant from snapshot; refresh = async rebuild) │ React panel: module sections + source badges + trend chart ``` ## Build workflow Follow in order. Read the two reference files first — they hold the details this overview compresses. 1. **Read `reference/apis.md`** — exact endpoints, field names, and every API gotcha (Kalshi's `*_dollars` field rename, no volume-sort, broken `category` filter, etc.). *Verify the endpoints live before coding — these APIs change.* 2. **Read `reference/architecture.md`** — the design decisions and the hard-won gotchas (async refresh, translation rate-limiting, empty-pull-never-overwrite, multi-leg events). 3. **Port the backend** from `code/backend/` into your stack: - `market_taxonomy.py` — module list + keyword classifier (source-agnostic). Tune keywords. - `polymarket_signals.py` — Polymarket Gamma/CLOB fetch + shape + snapshot. - `kalshi_signals.py` — Kalshi events fetch + shape + snapshot (note the retry/empty-guard). - `polymarket_translate.py` — optional title translation (swap in your LLM client). - `market_pulse.py` — the aggregator + async-refresh snapshot model. This is the core. - `api_routes.py` — FastAPI routes (`/pulse/overview`, `/polymarket/history`). 4. **Port the frontend** from `code/frontend/`: - `EventProbabilityPanel.tsx` — module sections, source badges, EN-primary/CN-secondary titles, multi-leg "档位 (market line)" chips, collapsed reference group, async-refresh poll. - `ProbabilityTrend.tsx` — echarts line chart (swap for your chart lib if needed). 5. **Wire the proxy/route** so the panel page and its API share a path prefix without the browser navigation getting swallowed (see architecture.md → "frontend plumbing"). 6. **Smoke test**: hit `/pulse/overview?refresh=true` once to build the first snapshot (can take minutes — Kalshi is slow), then confirm normal loads are instant and modules are populated and capped. ## Porting notes (the repo-specific bits to swap) The reference code came from a working FastAPI + React dashboard. Three things are environment-specific — find and replace them: | In the code | What it is | Swap for | |---|---|---| | `from src.config.paths import get_data_dir` | where snapshots/cache live (has a `~/.vibe-trading` fallback) | your app's data dir | | `from src.providers.llm import build_llm` (in `polymarket_translate.py`) | the LLM used to translate titles to Chinese | your LLM client, or drop translation entirely (English-only) | | `ProbabilityTrend`, Tailwind classes, lucide icons, `@/` alias | UI stack | your component/design system | Everything else (the API logic, taxonomy, aggregator, snapshot/async model, gotcha handling) is portable as-is. The modules import each other by bare name (e.g. `import market_taxonomy`) — keep them in the same package or adjust imports. ## Scope / customization - **Sources**: Polymarket + Kalshi are the two free, no-auth, money-backed venues worth using. Manifold (play-money) is open too but noisy; Metaculus/PredictIt are Cloudflare-blocked from servers. Adding/removing a source = add/remove a `*_signals.py`. - **Modules & filtering**: the taxonomy is a keyword map — retune `CORE_MODULES`, `REFERENCE_MODULES`, keyword lists, and `MODULE_CAPS` for your audience. The default folds sports/world-cup/crypto into a collapsed "reference" group. - **Translation**: optional. If the audience reads English, delete the translate step. Read `reference/apis.md` and `reference/architecture.md` next. --- > 📦 https://github.com/simonlin1212/globalpercent — Star ⭐ 是最好的支持