| 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：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.
- 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.
- Read
reference/architecture.md — the design decisions and the hard-won gotchas
(async refresh, translation rate-limiting, empty-pull-never-overwrite, multi-leg events).
- 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).
- 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).
- 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").
- 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
