kelly-criterion

Optimal position sizing is calculated by analyzing your strategy's win rate and payoff ratio to compute the Kelly fraction, which maximizes long-term geometric growth. This Skill applies fractional Kelly variants to recommend conservative allocations balancing growth and ruin risk.

Fractional Kelly applies a fraction (e.g., 0.25x, 0.5x) of the full Kelly criterion recommendation to reduce volatility and ruin risk. It provides conservative bet sizing suitable for real-world trading by accounting for edge stability and sample size confidence intervals.

Estimating trading edge from historical P&L involves calculating your strategy's win rate and average payoff ratio. This Skill analyzes historical performance data to compute confidence intervals and edge stability, ensuring position sizing recommendations account for statistical variance.

Yes, the Kelly criterion can be applied to betting scenarios with small sample sizes by using conservative fractional Kelly sizing. This Skill adjusts recommendations based on sample size and confidence intervals to prevent overestimating edge and risking ruin.

Yes, numpy supports the mathematical operations required for Kelly criterion position sizing, including statistical analysis of historical P&L data. This Skill leverages numpy to compute optimal allocation fractions, confidence intervals, and edge stability metrics.

Full Kelly criterion sizing assumes perfect edge estimation, which is unrealistic and leads to high volatility and ruin risk. This Skill mitigates this by applying fractional Kelly sizing and adjusting recommendations based on sample size, edge stability, and statistical confidence intervals.