globalpercent

📦 项目主页：https://github.com/simonlin1212/globalpercent — 更新、反馈、支持作者

作者：Simon 林 · 抖音「Simon林」· 公众号「硅基世纪」

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.

1. Read `reference/architecture.md` — the design decisions and the hard-won gotchas

(async refresh, translation rate-limiting, empty-pull-never-overwrite, multi-leg events).

1. 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).

1. 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).

1. 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").

1. 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:

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.

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