Education
Understanding Sharpe Ratio
A strategy that returns 40% sounds amazing — until you learn it had a 60% drawdown to get there. Sharpe ratio is the one number that tells you if your returns are worth the risk.
What is the Sharpe ratio?
Invented by Nobel laureate William Sharpe in 1966, the Sharpe ratio measures excess return per unit of risk. The formula is simple:
Sharpe = (Strategy Return - Risk-Free Rate) / Strategy Volatility
The risk-free rate is what you’d earn with zero risk (currently ~4.5% in T-bills). Volatility is the standard deviation of your returns — how bumpy the ride is.
How to interpret the number
Returns don't justify the risk. You'd be better in an index fund.
Decent risk-adjusted returns. Most retail strategies land here.
Strong performance. Institutional-grade. Worth deploying capital.
Outstanding. Few strategies sustain this. Renaissance Medallion territory.
Usually means overfitting, look-ahead bias, or a very short sample period.
Why total return is misleading
Consider two strategies over the same period:
Return: +45%
Max drawdown: -52%
Sharpe: 0.6
You would have been down 52% at one point. Most people would have quit.
Return: +28%
Max drawdown: -11%
Sharpe: 1.8
Lower return, but you could sleep at night. And you can leverage it.
Strategy B is objectively better. A Sharpe of 1.8 means you can safely apply 2x leverage and reach 56% returns with only -22% drawdown. Sharpe is the real edge.
Common Sharpe ratio pitfalls
- Short sample periods — A strategy backtested over 3 months can show a Sharpe of 5.0. It means nothing. You need at least 1–2 years of data.
- Annualization errors — Sharpe is typically annualized. Daily Sharpe of 0.1 ≠ annual Sharpe of 0.1. Multiply daily by √252.
- Non-normal returns — Sharpe assumes normally distributed returns. Strategies with fat tails (like short options) can have a high Sharpe that masks blowup risk.
How AlgoThesis reports Sharpe
Every AlgoThesis backtest reports an annualized Sharpe ratio alongside max drawdown, total return, win rate, and alpha vs SPY. When you compare the 3 AI-generated strategies for your thesis, Sharpe should be your primary filter — it tells you which strategy gives the best return for the risk taken.
See your strategy’s Sharpe ratio
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