# Metrics: how to tell a *good* strategy from a lucky one

Author: Jiří Fabšic · Jul 7, 2026 · Category: Methodology
Canonical URL: https://www.binaryfintech.com/en/blog/performance-metrics/

> Net profit, profit factor, max drawdown, Calmar, Sharpe, Sortino, PROM: what performance metrics really say, when they lie, why we annualize over 365 days and why nothing gets judged below 30 trades.

## TL;DR

- **One number is not enough.** Gross profit says nothing about the risk that paid for it, nor about the luck that helped it. A strategy is judged by a set of metrics: return, risk and their ratio.
- **The most honest metrics are risk-weighted and penalized:** Calmar (return/max drawdown), Sortino (punishes only downside volatility) and PROM — a pessimistic return that deliberately discounts the result for statistical luck.
- **Without a sample, every metric is noise:** below ~30 trades we do not judge at all. And a metric you optimize for becomes a target — hence the [result landscape](https://www.binaryfintech.com/en/blog/genetic-optimization/), walk-forward and Monte Carlo, not a single maximum.

“How much does it make?” is the most common — and worst-posed — question about a trading strategy. The right version is: **how much does it make, what does that cost in risk, and how much can I trust it?** Three families of metrics answer those three questions. Here are the ones we work with daily — including when each of them lies.

## Return: how much — and how

- **Net profit (%)** — the final result over a period. Necessary, but by itself the least informative: it says nothing about the path the equity took to get there.
- **Profit factor** — gross profits divided by gross losses. Below 1 the strategy loses; fragile systems tend to sit at 1.1–1.3; values above ~2.5 on a large sample are usually a reason for suspicion rather than joy ([overfitting](https://www.binaryfintech.com/en/blog/overfitting/)).
- **Expectancy** — the average result per trade. Useful for comparing strategies with different trading frequencies.

## Risk: what it cost

- **Maximum drawdown** — the deepest equity fall from peak to trough. For live deployment the single most important number: this is the pain you will actually have to sit through.
- **Time under water** — how long it took to climb out of the hole. Two systems with the same −20 % are two different experiences when one recovers in a month and the other in two years.
- **Drawdown distribution** — one backtest shows one max drawdown; [Monte Carlo](https://www.binaryfintech.com/en/blog/monte-carlo-simulation/) shows which drawdowns are *typical* for the strategy and which are possible. Percentiles instead of one number.
- **Risk of ruin** — the probability of the account falling below the point of no return. Managed by position sizing — see [Kelly and sizing](https://www.binaryfintech.com/en/blog/position-sizing-kelly/) — and monitored against guardrails continuously.

## Ratios: return per unit of risk

The real comparison of strategies happens here — return by itself is a meaningless number until you know how much risk produced it:

- **Calmar** — annual return divided by maximum drawdown. In plain words: *how many times the worst fall you will live through pays for itself.* Our favourite first filter for systematic strategies.
- **Sharpe** — return divided by total volatility. The classic, with one flaw: it punishes upside swings too, so it under-rates strategies with occasional large wins (typically trend following).
- **Sortino** — the same, but punishing only *downside* volatility. For asymmetric strategies the more honest sibling of Sharpe.

A detail that matters: we **annualize the ratio metrics over 365 days**, not the textbook 252 exchange days. The markets we trade do not sleep on weekends — and a metric computed on the wrong calendar quietly lies by tens of percent.

## Penalized metrics: deliberate pessimism

A backtest always carries some luck — it sees only one history, and [optimization gladly claims it](https://www.binaryfintech.com/en/blog/overfitting/). That is why our favourite metrics **deduct the luck in advance**:

- **PROM** (pessimistic return on margin, Pardo) — the return recomputed with pessimistically adjusted win and loss counts: under-weight the winners, over-weight the losers, and only then compute the return. A strategy that still looks good after this deliberate handicap is telling you something.
- **Composite metrics** — combinations of captured market movement, consistency of results and drawdown in one score. They punish strategies that earned everything in one lucky period.

*Figure: Return says how much, risk says at what cost, the ratio ties them together. Penalties deduct the luck — and without a sufficient sample none of it is worth reading.*

## Sample quality: when to trust the metrics at all

- **Trade count.** Below ~30 trades every metric is chance; reliability grows with the square root of the sample. A beautiful Calmar on twelve trades is not a result, it is an anecdote.
- **Market exposure** — the share of time in the market. Without it returns cannot be compared honestly: 20 % a year at half the time in the market is more per unit of exposure than 30 % at full time. Details in [the sizing article](https://www.binaryfintech.com/en/blog/position-sizing-kelly/).
- **Stability over time.** Metrics computed over the whole period mask a strategy that earned everything in year one and stagnated for three. That is why we read them by [windows](https://www.binaryfintech.com/en/blog/walk-forward-analysis/): consistency across periods weighs more than the grand total.

## A metric as a target: beware the tunnel

In [genetic optimization](https://www.binaryfintech.com/en/blog/genetic-optimization/) the chosen metric becomes fitness — the number evolution breeds by. And here the old truth about measurement applies: **when a metric becomes a target, it stops being a good metric.** Optimizing for net profit breeds gamblers with deep drawdowns; optimizing for Sharpe breeds timid systems afraid to earn. The defence is always the same: penalized metrics, reading the whole result landscape instead of one maximum — and before anything gets near production, [walk-forward](https://www.binaryfintech.com/en/blog/walk-forward-analysis/) and [Monte Carlo](https://www.binaryfintech.com/en/blog/monte-carlo-simulation/), which hold a mirror up to the metrics on data the optimization has never seen.

## Summary: the metrics in one place

| Metric | Family | What it says | Trap |
|---|---|---|---|
| Net profit % | return | final result over a period | nothing about risk or the path |
| Profit factor | return | gross profits / gross losses | >2.5 on a large sample = suspicion |
| Expectancy | return | average per trade | masks the spread of outcomes |
| Max drawdown | risk | deepest fall from a peak | one run = one number, not a distribution |
| Time under water | risk | how long recovery takes | often not reported at all |
| Risk of ruin | risk | chance of falling past the point of no return | depends on sizing, not just the strategy |
| Calmar | ratio | annual return / max drawdown | cyclical — depends on the period |
| Sharpe | ratio | return / total volatility | punishes gains too (trend following) |
| Sortino | ratio | return / downside volatility | sensitive to few negative days |
| PROM | penalty | return discounted for statistical luck | conservative — undervalues good strategies |
| Composite score | penalty | captured move + consistency + DD | the recipe must be fixed upfront, not tuned |
| Market exposure | sample | share of time in the market | without it returns cannot be compared |
| Trade count | sample | sample size (min ~30) | below 30 everything is an anecdote |

**Reading:** Robert Pardo: [The Evaluation and Optimization of Trading Strategies](https://www.wiley.com/en-us/The+Evaluation+and+Optimization+of+Trading+Strategies%2C+2nd+Edition-p-9780470128015) (Wiley) — chapters on performance evaluation and PROM. Related: [Genetic optimization](https://www.binaryfintech.com/en/blog/genetic-optimization/) · [Monte Carlo](https://www.binaryfintech.com/en/blog/monte-carlo-simulation/) · [Kelly and sizing](https://www.binaryfintech.com/en/blog/position-sizing-kelly/).