/prediction-market-monitor

name: prediction-market-monitor
description: Pull Kalshi prediction market prices for Fed decisions, CPI, GDP, NFP, and other macro / market events. Report implied probability per outcome, aggregate cross-strike distribution when the series is a laddered strike set (like KXFED-27APR-T4.25, T4.00, T3.75...), expected value, modal outcome, and open interest. Prediction markets now clear enough volume post-2024 to reflect a real market-implied policy path, often diverging from surveyed economist consensus. Uses Kalshi's public read-only API; no authentication required.

prediction-market-monitor

You pass a Kalshi series or shortcut. The skill pulls open markets, groups by event, and reports implied probabilities. When the event is a laddered strike-type (Fed funds level, CPI reading), it derives the cross-strike probability distribution from adjacent-threshold differences and reports the modal outcome and expected value.

Motivated by 2025-26 growth of prediction markets as leading indicators post-2024 election validation. Kalshi Fed-decision contracts now trade meaningful volume; contract prices often reflect policy expectations before consensus surveys catch up.

When to invoke

• "What's the Kalshi-implied Fed decision at the next meeting?"
• Comparing market-implied CPI to Bloomberg / Reuters consensus
• Cross-referencing macro-print bets against your positioning
• The user says "Kalshi", "prediction market", "implied Fed", "fed

funds futures alternative"

What you need

• Internet access (Kalshi's public API; no Massive key needed)

Optional:

• `--series` (shortcut like `fed`, `cpi`, `nfp`, `gdp`, or a raw

Kalshi series ticker like `KXFED`, `KXCPI`)

• `--keyword` (client-side filter on title / ticker)
• `--event-ticker` (pin a specific event, e.g. `KXFED-27APR`)
• `--max-events` (default 5)

What you get back

**Layer 1: JSON**. Per event: `event_ticker`, `title`, `close_time`, `markets` (each with `implied_probability`, bid, ask, last, volume, open interest, floor_strike). When laddered: `implied_distribution.buckets` with `p_in_bucket` and `cumulative_p_above_lower`, plus `modal_bucket` and `expected_value`.

**Layer 2: rendered note**. Per event: title + close time, modal outcome + expected value line, bucket distribution with ASCII bars.

How it works

1. Query Kalshi `/trade-api/v2/markets` with the series filter. Paginate up to 3 pages (600 markets max). 2. Group markets by `event_ticker`. A single event ("KXFED-27APR") typically contains 15-20 laddered strikes. 3. For each event, sort by `floor_strike` and derive the implied distribution: `P(rate in [lower, upper))` = `P(above lower)` - `P(above upper)`. 4. Report modal outcome (highest-probability bucket) and expected value (probability-weighted midpoint).

Series shortcuts

• `fed`: KXFED (fed funds level after meeting)
• `fed_decision`: KXFEDDECISION (rate change at meeting)
• `cpi`: KXCPI (m/m)
• `core_cpi`: KXCORECPI
• `cpi_yoy`: KXCPIYOY
• `nfp`: KXNFP
• `unemployment`: KXUNEMP
• `gdp`: KXGDP
• `jobless_claims`: KXICSA
• `recession`: KXRECESSIONYEAR
• `spx_close`: KXSPX
• `btc_close`: KXBTCD

Foundations used

• None. Kalshi public API only.

Endpoints used

• `GET https://api.elections.kalshi.com/trade-api/v2/markets`

Public read-only, no auth.

Doesn't handle (yet)

• **Polymarket integration.** Kalshi only. Polymarket has more

political / cultural markets; Kalshi has more macro. Adding a `--source polymarket` toggle would be a clean extension.

• **Time series of implied probability.** Snapshot only. A rolling

history would show when the market moved.

• **Cross-reference to survey consensus.** Would need a data

partnership with Bloomberg / Reuters or an FOMC dot-plot lookup.

• **CFTC-regulated futures cross-check.** SOFR/Fed funds futures

implied path from CME data would be a nice comparator.

These are clean PR extensions.