prediction-market-strategy

Name: prediction-market-strategy
Author: agiprolabs

57installs53forks

How to install prediction-market-strategy?

agiprolabs/claude-trading-skills/prediction-market-strategy

$npx -y skills add agiprolabs/claude-trading-skills --skill prediction-market-strategy

Installs into the current project.

Summary

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.

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SKILL.md

1	# Prediction Market Strategy
2
3	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.
4
5	## Core Thesis
6
7	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).
8
9	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.
10
11	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.
12
13	Forecast skill ≠ trading edge. A good weather or event forecast is largely redundant with the market price at decision time. Markets aggregate information efficiently enough that even a measurably better model produces near-zero net edge after fees unless it finds systematic mispricings (which are behavioral, not informational). The exception is official-label ML in lightly-traded markets — but that is bounded by fill-rate and capacity, not forecast accuracy.
14
15	---
16
17	## Strategy Catalog
18
19	Strategies evaluated on Kalshi/Polymarket weather and event markets. "Real" means it survived correct settlement + fees in testing; others are flagged so you don't re-chase them.
20
21	\| Strategy \| Verdict \| Mechanism \| Key Catch \|
22	\|----------\|---------\|-----------\|-----------\|
23	\| Favorite–longshot fade, maker-side \| ✅ Durable \| Rest maker NO bids on ~$0.05–$0.20 brackets; behavioral tail overpricing \| Rare longshot hit; measure fill-rate forward \|
24	\| Bracket YES-only (forecast-driven) \| ✅ Works, fee-sensitive \| Buy YES when calibrated model says bracket is materially underpriced \| Requires net_edge > θ gate; not raw win-rate \|
25	\| Market-making / liquidity provision \| ⚠️ Structurally favored, infra-heavy \| Two-sided quotes, capture spread + maker rebates \| Inventory risk, adverse selection, queue priority \|
26	\| Overround / dutching arbitrage \| ⚠️ Real in theory, marginal in practice \| Sum-to->$1.00 across brackets; buy the underpriced residual \| Legs must fill simultaneously; Kalshi fills are sequential \|
27	\| Cross-venue arb (Kalshi ↔ Polymarket) \| ❌ Blocked for most \| Simultaneous position in equivalent contracts on two venues \| Transfer time/cost destroys edge; geo-lock \|
28	\| Latency / news front-running (crypto/index hourlies) \| ❌ HFT game \| React to public feeds before market reprices \| Sub-100ms requirement; co-location; not retail \|
29	\| Near-certainty intraday repricing \| ⚠️ Information/latency edge \| Markets slow to reprice near-certain contracts; capture the residual \| Requires real-time feed + automation \|
30	\| Copy-the-sharps \| ❌ Survivorship illusion \| Mirror apparent winning accounts \| No reliable signal on public data; past winners regress \|
31
32	Bottom line: Two strategies survive correct accounting — the behavioral tail fade (maker-side) and the forecast-driven YES entry past the θ gate. Everything else is either HFT-scale, infrastructure-heavy, or dissolves under correct settlement + fees.
33
34	---
35
36	## Fee-Aware Sizing & Edge Gates
37
38	All selection is on fee-adjusted net edge. Raw win-rate, return on notional, and % correct are not selection metrics.
39
40	### Net Edge Formula
41
42	```python
43	net_edge = p_model - ask - kalshi_fee(ask)
44	# Kalshi taker fee: ceil(0.07 * price * (1 - price) * 100) / 100 per contract
45	# Polymarket fee: 0 (no explicit taker fee; spread is the cost)
46	```
47
48	Select a trade only when `net_edge > θ`.
49
50	### Expected-Edge Gate (θ)
51
52	\| Account size \| θ (Kalshi) \| Rationale \|
53	\|---\|---\|---\|
54	\| < $2,000 \| 15% \| Small account; fee drag is proportionally higher \|
55	\| ≥ $2,000 \| 20% \| Standard gate covering fee + execution uncertainty \|
56	\| Polymarket \| ~5% \| No explicit taker fee; spread and gas are

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prediction-market-strategy

57installs53forks

View on GitHub

How to install prediction-market-strategy?

agiprolabs/claude-trading-skills/prediction-market-strategy

$npx -y skills add agiprolabs/claude-trading-skills --skill prediction-market-strategy

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Prediction Market Strategy
2
3	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.
4
5	## Core Thesis
6
7	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).
8
9	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.
10
11	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.
12
13	Forecast skill ≠ trading edge. A good weather or event forecast is largely redundant with the market price at decision time. Markets aggregate information efficiently enough that even a measurably better model produces near-zero net edge after fees unless it finds systematic mispricings (which are behavioral, not informational). The exception is official-label ML in lightly-traded markets — but that is bounded by fill-rate and capacity, not forecast accuracy.
14
15	---
16
17	## Strategy Catalog
18
19	Strategies evaluated on Kalshi/Polymarket weather and event markets. "Real" means it survived correct settlement + fees in testing; others are flagged so you don't re-chase them.
20
21	\| Strategy \| Verdict \| Mechanism \| Key Catch \|
22	\|----------\|---------\|-----------\|-----------\|
23	\| Favorite–longshot fade, maker-side \| ✅ Durable \| Rest maker NO bids on ~$0.05–$0.20 brackets; behavioral tail overpricing \| Rare longshot hit; measure fill-rate forward \|
24	\| Bracket YES-only (forecast-driven) \| ✅ Works, fee-sensitive \| Buy YES when calibrated model says bracket is materially underpriced \| Requires net_edge > θ gate; not raw win-rate \|
25	\| Market-making / liquidity provision \| ⚠️ Structurally favored, infra-heavy \| Two-sided quotes, capture spread + maker rebates \| Inventory risk, adverse selection, queue priority \|
26	\| Overround / dutching arbitrage \| ⚠️ Real in theory, marginal in practice \| Sum-to->$1.00 across brackets; buy the underpriced residual \| Legs must fill simultaneously; Kalshi fills are sequential \|
27	\| Cross-venue arb (Kalshi ↔ Polymarket) \| ❌ Blocked for most \| Simultaneous position in equivalent contracts on two venues \| Transfer time/cost destroys edge; geo-lock \|
28	\| Latency / news front-running (crypto/index hourlies) \| ❌ HFT game \| React to public feeds before market reprices \| Sub-100ms requirement; co-location; not retail \|
29	\| Near-certainty intraday repricing \| ⚠️ Information/latency edge \| Markets slow to reprice near-certain contracts; capture the residual \| Requires real-time feed + automation \|
30	\| Copy-the-sharps \| ❌ Survivorship illusion \| Mirror apparent winning accounts \| No reliable signal on public data; past winners regress \|
31
32	Bottom line: Two strategies survive correct accounting — the behavioral tail fade (maker-side) and the forecast-driven YES entry past the θ gate. Everything else is either HFT-scale, infrastructure-heavy, or dissolves under correct settlement + fees.
33
34	---
35
36	## Fee-Aware Sizing & Edge Gates
37
38	All selection is on fee-adjusted net edge. Raw win-rate, return on notional, and % correct are not selection metrics.
39
40	### Net Edge Formula
41
42	```python
43	net_edge = p_model - ask - kalshi_fee(ask)
44	# Kalshi taker fee: ceil(0.07 * price * (1 - price) * 100) / 100 per contract
45	# Polymarket fee: 0 (no explicit taker fee; spread is the cost)
46	```
47
48	Select a trade only when `net_edge > θ`.
49
50	### Expected-Edge Gate (θ)
51
52	\| Account size \| θ (Kalshi) \| Rationale \|
53	\|---\|---\|---\|
54	\| < $2,000 \| 15% \| Small account; fee drag is proportionally higher \|
55	\| ≥ $2,000 \| 20% \| Standard gate covering fee + execution uncertainty \|
56	\| Polymarket \| ~5% \| No explicit taker fee; spread and gas are

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