globalpercent

name

globalpercent

description

GlobalPercent — build a global-macro-probability panel for an investment research system. Merges public probability data from prediction markets (Polymarket + Kalshi), classifies every market into macro modules (monetary policy / macro economy / AI / etc.), and shows the whole market's expected-probability state at a glance as a sentiment/risk overlay (not a trading signal). Use when building a macro-probability or "event probability" dashboard, or wiring Polymarket/Kalshi APIs. Includes verified API details, the full architecture, hard-won gotchas, and adaptable reference code (Python backend + React panel).

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

作者：Simon 林 · X @linsizhen · 邮箱：[e-mail]

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.

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