bet-sizing
Bet Sizing guides you through position sizing frameworks including the Kelly criterion for maximizing long-term wealth growth, fractional Kelly approaches for practical risk management, and risk budgeting methods. Learn to scale positions by volatility, set concentration limits, and weight bets by conviction strength.
Bet Sizing helps you calculate optimal position sizes using Kelly criterion and fractional Kelly methods.
AI-generated summary based on this skill's SKILL.md
Install
JoelLewis/finance_skills/bet-sizing · repository language: Python
git clone https://github.com/JoelLewis/finance_skills
cp -r finance_skills/plugins/wealth-management/skills/bet-sizing ~/.claude/skills/bet-sizingFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How much should bet-sizing help me invest in each stock?
Bet Sizing guides you through position sizing frameworks to determine optimal allocation per holding. The skill covers Kelly criterion for maximizing long-term wealth growth, fractional Kelly approaches for practical risk management, and risk budgeting methods across portfolio positions. You'll learn to scale positions by volatility, set concentration limits, and weight bets by conviction strength to match your investment thesis.
What's the maximum position size for my portfolio using bet-sizing?
Bet Sizing teaches methods to set maximum position size limits based on liquidity constraints and risk tolerance. The skill helps you establish portfolio concentration limits and VaR-based position limits. By understanding your risk budget and volatility profile, you can determine appropriate caps that prevent overexposure while maintaining diversification aligned with your investment strategy.
How does bet-sizing calculate optimal bet size with Kelly formula?
Bet Sizing covers the Kelly criterion for calculating optimal position sizes to maximize long-term wealth growth. The skill explains both full Kelly and fractional Kelly approaches—fractional Kelly (like half Kelly) reduces bet size for practical risk management. You'll learn how to apply the formula using your win probability and payoff ratio to determine mathematically optimal position sizing.
Can bet-sizing adjust position sizes based on volatility changes?
Yes, Bet Sizing includes volatility-adjusted position sizing methods. The skill teaches you to scale positions proportionally to volatility changes, reducing exposure when volatility rises and increasing it when volatility falls. This dynamic approach helps maintain consistent risk levels across your portfolio as market conditions evolve.
How does bet-sizing weight positions by conviction strength?
Bet Sizing guides you through conviction weighting frameworks to size positions proportional to your investment conviction strength. Rather than equal-weighting all holdings, the skill teaches you to allocate more capital to high-conviction ideas and less to lower-conviction positions. This approach aligns your portfolio construction with the strength of your investment thesis.
What position sizing methods does bet-sizing cover?
Bet Sizing covers multiple position sizing frameworks including Kelly criterion, fractional Kelly, risk budgeting, volatility-adjusted sizing, and conviction weighting. The skill helps you allocate risk budget across portfolio positions, set concentration limits, and determine optimal holding counts. You'll learn practical approaches for scaling bets and managing position risk exposure across your entire portfolio.
SKILL.md
rendered from the published skill — quoted content, verbatim
Bet Sizing
Core Concepts
Kelly Criterion (Discrete)
For a binary bet with payoff odds b, win probability p, and loss probability q = 1-p:
f = (bp - q) / b
where f* is the optimal fraction of wealth to wager. The Kelly criterion maximizes the expected logarithm of wealth (geometric growth rate) over repeated bets.
Properties: - f = 0 when edge = 0 (no bet when there is no advantage) - f < 0 when negative edge (the formula tells you to bet the other side) - f > 0 only when bp > q (positive expected value)
Note: the reference script's discrete_kelly clamps negative Kelly fractions to 0 (no bet) rather than returning a negative value — it does not recommend taking the
(truncated - see the full file via the links below)
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File tree — 2 files
plugins/wealth-management/skills/bet-sizing/SKILL.md
plugins/wealth-management/skills/bet-sizing/scripts/bet_sizing.py