kelly-criterion

kelly-criterion is a skill for Claude Code from agiprolabs/claude-trading-skills. It costs 22 tokens per session (2,789 once invoked), scanned A, original, MIT.

A mathematical method for choosing what fraction of available money to risk on repeated bets or trades, based on the chance of winning and the expected payoff.

In plain words
What is it for?
Use it to estimate position sizes for trading or other repeated-risk decisions, including fractional sizing and checks for whether a positive expected advantage exists.
Why use it?
It provides a sizing rule that balances long-term growth against the danger of risking too much, while allowing smaller fractional versions for uncertain estimates.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the trading-skills plugin — 68 skills shipped together

not rated 356repo +10 9d ago A scan Socket: passSnyk: passSkillSpector: pass 22 tokens original MIT

Good fit Use it to estimate position sizes for trading or other repeated-risk decisions, including fractional sizing and checks for whether a positive expected advantage exists.

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Install with agentmods
npx agentmods add skills/agiprolabs/claude-trading-skills/kelly-criterion
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add agiprolabs/claude-trading-skills --skill kelly-criterion
Clone the repo
git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills

Made for: Claude Code.

Or install trading-skills, the plugin that ships this one along with the rest of its 68 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for kelly-criterion

README.md
[![agentmods](https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/kelly-criterion/github.svg)](https://agentmods.dev/skills/agiprolabs/claude-trading-skills/kelly-criterion)
Your own site
<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/kelly-criterion"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/kelly-criterion/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for kelly-criterion

Your own site · 80×15
<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/kelly-criterion"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/kelly-criterion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,789 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 21 Mar 2026
  • Snyk pass 21 Mar 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00022 $0.02789
Opus 5 $0.00011 $0.01394
Sonnet 5 $0.00004 $0.00558
Haiku 4.5 $0.00002 $0.00279

Measured 13d ago against content hash df7552b7d1b4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

kelly-criterion scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/kelly_calculator.py, scripts/kelly_from_trades.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/kelly-criterion/SKILL.md · 266 lines

How it starts

The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Kelly Criterion — Optimal Bet Sizing

The Kelly criterion is the mathematically optimal bet size that maximizes long-term geometric growth of capital. Developed by John Kelly at Bell Labs in 1956, it answers a precise question: given a known edge, what fraction of your bankroll should you risk to maximize the compounding rate?

Core insight: Betting too small leaves growth on the table. Betting too large increases ruin risk and actually reduces long-term growth. Kelly finds the exact optimum between these extremes.

Practical insight: You should almost never use full Kelly. Estimation error in your edge means full Kelly will overbets in practice. Use fractional Kelly (0.25x to 0.5x) for real trading.


The Kelly Formula

For a binary outcome (win or lose):

f* = (p * b - q) / b

Where:

  • f* = optimal fraction of bankroll to bet
  • p = probability of winning
  • q = probability of losing (1 - p)
  • b = payoff ratio (average win / average loss)

Equivalent forms:

f* = p - q / b
f* = p - (1 - p) / b
f* = (p * b - (1 - p)) / b

Edge = p * b - q = expected value per unit risked. Kelly only makes sense when edge > 0. If edge is zero or negative, the optimal bet is zero — do not trade.

Quick Reference

Win Rate Payoff 1:1 Payoff 1.5:1 Payoff 2:1 Payoff 3:1
40% -20% -6.7% 10% 20%
45% -10% 3.3% 15% 25%
50% 0% 16.7% 25% 33.3%
55% 10% 18.3% 27.5% 35%
60% 20% 26.7% 35% 40%

Values are full Kelly fraction. In practice, use 0.25x to 0.5x of these numbers.


Why Use Fractional Kelly

Full Kelly assumes you know p and b exactly. You never do. Here is why fractional Kelly is essential:

1. Estimation Error

Your win rate estimate from 100 trades has a standard error of roughly ±5%. If your true win rate is 55% but you estimate 60%, full Kelly will overbets by ~50%, which reduces long-term growth below what half Kelly would achieve.

Read the full file on GitHub · 266 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 13d ago First seen · 266 lines · 22 tokens per session scan A df7552b7d1b4

Subscribe to this mod's changes

kelly-criterion is a skill published in the GitHub repository agiprolabs/claude-trading-skills (356 stars, last pushed 9d ago), licensed MIT. It adds 22 tokens to every session and 2,789 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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