Insights
The Quantly blog
Plain-language guides on metrics, strategy design, and platform research - written for investors who want systematic edge without writing code.
Learn · Sep 16, 2026
What is volatility?
Volatility measures how much returns swing around their average. Learn standard deviation, annualizing daily vol, and how volatility connects to Sharpe, drawdowns, and risk limits.
Learn · Sep 15, 2026
What is the Calmar ratio?
The Calmar ratio divides annualized return by maximum drawdown. Learn when it helps compare strategies, how it relates to Sharpe, and pitfalls from short or lucky samples.
Learn · Sep 15, 2026
What is the Sortino ratio?
The Sortino ratio is like Sharpe but penalizes downside volatility only. Learn the formula, when to use it instead of Sharpe, and how to interpret it on a backtest.
Learn · Sep 14, 2026
CAGR vs total return
Total return is how much you grew over a period; CAGR is the steady yearly rate that would compound to the same ending value. Learn when each metric misleads and how to use both in backtests.
Learn · Sep 13, 2026
Win rate and profit factor
Win rate is the share of winning trades; profit factor is gross profits divided by gross losses. Learn what each measures, typical ranges by style, and why high win rate can still lose money.
Learn · Sep 12, 2026
What is momentum investing?
Momentum investing buys recent winners and avoids laggards, often on a schedule. Learn time-series vs cross-sectional momentum, holding periods, and why trends can reverse.
Learn · Sep 11, 2026
What is mean reversion?
Mean reversion strategies bet that prices or spreads return toward an average. Learn common signals, where the idea works, and why strong trends break reversion rules.
Learn · Sep 10, 2026
How to choose a backtest start date
Your backtest start date shapes CAGR, drawdown, and whether results are believable. Learn data availability, survivorship, regime coverage, and practical rules for picking a window.
Learn · Sep 9, 2026
Walk-forward testing explained
Walk-forward testing trains rules on one historical segment and validates on the next, rolling forward in time. Learn why it reduces overfitting and how to apply it without heavy math.
