kelly-criterion
Kelly Criterion computes the mathematically optimal fraction of your bankroll to risk on each trade, balancing growth against ruin risk. The skill implements the Kelly formula with practical guidance on fractional sizing (0.25x–0.5x recommended), edge estimation from trade history, and portfolio-level adjustments for correlated positions.
Kelly Criterion calculates the optimal bet size that maximizes long-term capital growth given your win rate and payoff ratio.
AI-generated summary based on this skill's SKILL.md
Install
agiprolabs/claude-trading-skills/kelly-criterion · repository language: Python
git clone https://github.com/agiprolabs/claude-trading-skills
cp -r claude-trading-skills/skills/kelly-criterion ~/.claude/skills/kelly-criterionFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What is kelly criterion bet sizing and how does it work?
Kelly Criterion computes the mathematically optimal fraction of your bankroll to risk on each trade, balancing growth against ruin risk. The skill implements the Kelly formula with practical guidance on fractional sizing (0.25x–0.5x recommended), edge estimation from trade history, and portfolio-level adjustments for correlated positions.
How do I calculate optimal position size using Kelly?
Kelly Criterion calculates optimal bet size by combining your win rate, average win size, and average loss size into a single formula. The skill helps you estimate edge and win rate from trading data, then applies the Kelly formula to determine what fraction of your bankroll to risk per trade. Fractional Kelly (0.25x–0.5x) is recommended for practical trading to reduce drawdown risk.
What is fractional Kelly and why use 0.25x or 0.5x?
Fractional Kelly reduces full Kelly sizing by a conservative multiplier—typically 0.25x or 0.5x—to protect against estimation error and real-world volatility. Kelly Criterion's full formula assumes perfect edge knowledge; fractional Kelly for practical trading applications acknowledges that your edge estimate may be wrong, smoothing growth curves and lowering catastrophic ruin risk while still outperforming fixed-size betting.
How do I estimate edge and win rate from my trading data?
Kelly Criterion guides you to extract three metrics from your trade history: win rate (winning trades ÷ total trades), average win size (mean profit on winners), and average loss size (mean loss on losers). These feed directly into the Kelly formula to compute your true edge. The skill helps validate that your edge is positive and statistically meaningful before sizing positions.
Can Kelly Criterion manage multiple correlated positions?
Yes. Kelly Criterion addresses managing multiple correlated positions with Kelly sizing by adjusting allocations across your portfolio. When positions move together, naive Kelly sizing on each independently can over-leverage your total exposure. The skill provides portfolio-level adjustments to account for correlation, ensuring your combined risk stays within acceptable bounds.
When should I use fractional Kelly for meme token trades?
Apply conservative Kelly to high-uncertainty meme token trades by using fractional Kelly at 0.25x or lower. Meme tokens have unpredictable volatility and edge estimation is unreliable; fractional Kelly reduces position size dramatically, cutting ruin risk while preserving long-term growth. Full Kelly is dangerous here; conservative fractional sizing is the practical approach.
SKILL.md
rendered from the published skill — quoted content, verbatim
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 =
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File tree — 5 files
skills/kelly-criterion/SKILL.md
skills/kelly-criterion/references/kelly_derivation.md
skills/kelly-criterion/references/practical_kelly.md
skills/kelly-criterion/scripts/kelly_calculator.py
skills/kelly-criterion/scripts/kelly_from_trades.py