# prediction-market-strategy

A strategy, position-sizing and backtesting layer for binary prediction markets such as Kalshi, Polymarket and ForecastEx. Useful when trade selection has to run on net edge instead of win rate.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 311 installs · ★ 443
- Author: agiprolabs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/prediction-market-strategy/SKILL.md
- Updated: 2026-09-03
- HTML: https://affpapa.org/en/nevibecode/skills/prediction-market-strategy

## What it does

Binary contracts are priced as probabilities here: a quote of $0.18 stands for an 18% chance of resolving YES. The skill stays neutral about venue and market type. API calls belong to `kalshi-api` and `polymarket-api`, contract semantics and settlement to `kalshi-weather-markets` and `kalshi-crypto-index-markets`; what remains is selection, sizing and validation.

Selection runs on fee-adjusted net edge: `net_edge = p_model - ask - kalshi_fee(ask)`. A trade qualifies only above the θ gate — 15% under a $2,000 account, 20% at or above it, roughly 5% on Polymarket, where no explicit taker fee applies. Stake comes from fractional Kelly: `f_star = edge / (1 - entry_price)`, with quarter-Kelly and a 1.5% bankroll cap per contract as the defaults. The functions sit in `scripts/sizing.py`, which you can run for a worked example.

Slippage counts as part of selection. Walk the actual NO/YES ladder at full size to get a depth-weighted entry price. Should the walk push net_edge back under θ, or should slippage eat more than half the edge, the trade is skipped. Penny levels of 1–2 cents that fail to persist between order book snapshots get dropped from the ladder first. Taking liquidity makes sense only when a resting order is unlikely to fill in time.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill prediction-market-strategy -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/prediction-market-strategy/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/prediction-market-strategy/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill prediction-market-strategy -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/prediction-market-strategy/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill prediction-market-strategy -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/prediction-market-strategy/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install agiprolabs/claude-trading-skills prediction-market-strategy
```

2. **Manually: put the files in place**
- File: `.github/skills/prediction-market-strategy/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill prediction-market-strategy -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/agiprolabs/claude-trading-skills --path skills/prediction-market-strategy
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/prediction-market-strategy/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill prediction-market-strategy -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/prediction-market-strategy/SKILL.md

## When to use it
- pick trades by net edge rather than share of correct calls
- size a position with fractional Kelly under a per-contract cap
- fold the Kalshi fee into the entry price before posting an order
- check slippage across the order book ladder at full size
- strip phantom 1–2 cent levels before computing entry price

## Example prompts
- Compute net edge for a Kalshi contract at $0.12 with my model probability of 0.25
- What stake does quarter-Kelly give me on a $5,000 bankroll with edge 0.18?
- Walk the NO ladder at full size and tell me whether the trade clears the θ gate

## Source text · SKILL.md · MIT

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BEGIN THIRD-PARTY FILE ca1da31a11a550d1
```text
---
name: prediction-market-strategy
description: Venue- and market-type-agnostic strategy, sizing, and backtesting layer for binary prediction markets (Kalshi, Polymarket, ForecastEx). Covers the durable edge thesis, fee-aware selection, fractional-Kelly sizing, and leak-free validation methodology.
---

# Prediction Market Strategy

Binary prediction markets price contracts as probabilities. This skill covers the *strategy, sizing, and validation* layer that applies across all venues and market types. API mechanics live in `kalshi-api` / `polymarket-api`; contract semantics and settlement live in `kalshi-weather-markets` / `kalshi-crypto-index-markets`. This is the strategy/sizing/validation layer that applies across all of them.

## Core Thesis

**Price = implied probability.** A contract priced at $0.18 claims an 18% chance of resolving YES. Brackets in a series sum to just above $1.00 — the overround is the house margin (roughly 5–8% for weather markets on Kalshi).

**Takers systematically lose; makers systematically win.** Across 300k+ Kalshi contracts, the average pre-fee return is ≈ −20%, concentrated in takers (market-order users) and in longshot buyers. Makers (resting limit orders) earn positive returns. On Polymarket (588M+ trades), the top ~1% of accounts capture ~76.5% of profit, predominantly by resting limit orders. This is the foundational result.

**Favorite–longshot bias is the durable mechanism.** Cheap longshots are systematically overpriced: a $0.05 contract historically wins ~2%; sub-$0.10 contracts lose ~60% of stake to buyers. Favorites are fairly- to slightly-underpriced. The repeatable expression is **selling the overpriced longshot tail, maker-side** — resting NO bids on brackets priced ~$0.05–$0.20, diversified across many events to survive the rare hit. This is structural/behavioral, not a forecasting edge.
```
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