# Metrics
> Every performance metric TextToQuant reports, what it means, and how to read it honestly.
Source: https://docs.texttoquant.com/reference/metrics
---
Every backtest returns the same set of metrics so you can compare strategies on equal footing. This
page is the definitive reference: what each number measures, and, just as important, how to read it
without fooling yourself.
Metrics fall into four groups: **headline** performance, **risk adjusted** return, **trade quality**,
and the **overfitting aware** statistics that make a result trustworthy.
## Headline metrics
These six appear on every results card. They answer "did it make money, and how?"
| Metric | What it measures | How to read it |
| --- | --- | --- |
| Total return | Net profit or loss over the test window, as a percentage of starting capital | The top line result, but never judge it alone; a big return with a huge drawdown is fragile |
| Win rate | Share of trades that closed profitable | High win rate ≠ profitable. A 40% win rate can beat a 70% one if winners are bigger |
| Profit factor | Gross profit ÷ gross loss | Above `1.0` is profitable; `1.5`+ is healthy; be suspicious of very high values on few trades |
| Sharpe ratio | Return per unit of total volatility | `>1` is good, `>2` is strong, but see the overfitting section before trusting it |
| Max drawdown | Largest peak to trough equity drop | The pain metric. Ask yourself: could you sit through this loss live? |
| Total trades | Number of closed trades | Sample size. Under ~30 trades, treat every other metric as noisy |
A strategy is a trade off between return, risk, and reliability. Total return tells you the reward,
max drawdown tells you the risk, and total trades tells you how much to trust either.
### Return over time & exposure
Two runs of different lengths, or with very different time in the market, aren't directly comparable
on raw return. These normalise for that.
| Metric | Meaning |
| --- | --- |
| Annualized return (CAGR) | Total return expressed as a yearly rate, so a 6-month and a 3-year run compare on equal footing |
| Exposure / time in market | Share of bars you actually held a position. A strategy in the market 5% of the time carries very different risk from one always in |
| Avg / median bars held | Typical holding time per trade |
## Risk adjusted return
Raw return ignores how much risk you took to earn it. These metrics divide reward by risk, so a calm
strategy and a wild one become comparable.
| Metric | Divides return by | Use it when |
| --- | --- | --- |
| Sharpe ratio | Total volatility (up and down) | The default risk adjusted score |
| Sortino ratio | Downside volatility only | You don't want to be penalised for big *winning* months |
| Calmar ratio | Max drawdown | You care most about surviving the worst stretch |
Because Sharpe penalises upside and downside equally, a strategy with occasional large gains can look
worse than it is. That's when Sortino tells the truer story. Calmar is the one to watch if your real
constraint is *"how deep a hole can I tolerate?"*
### More risk-adjusted & sizing scores
| Metric | Meaning |
| --- | --- |
| Omega ratio | Probability-weighted gains ÷ losses around a threshold, captures return skew the Sharpe misses |
| Information ratio | Return *above a passive buy-&-hold*, divided by the volatility of that excess. Differs from Sharpe, which compares to a flat zero |
| SQN (System Quality Number) | Trade expectancy scaled by √trades (Van Tharp), one number for how tradable the system is |
| Kelly fraction | The bet size the edge implies. A ceiling to respect, not a target, full Kelly is famously wild |
## Trade quality
Headline numbers hide *how* the money was made. These read the trade ledger directly.
| Metric | Meaning |
| --- | --- |
| Expectancy (R) | Average profit per trade, expressed in units of risk (R). Positive expectancy is the whole game |
| Average win / loss | Mean profit of winners vs mean loss of losers |
| Payoff ratio | Average win ÷ average loss, how much bigger winners are than losers |
| R multiple | Each trade's result as a multiple of the risk taken to enter it |
| MAE / MFE | Average worst drawdown (adverse) and best unrealised gain (favourable) reached *inside* a trade, how much heat you sat through before the exit |
| Success rate | Share of trades that closed on one of your exit rules (stop, target, signal) rather than being force-closed at a horizon or the test's end |
A profitable strategy needs `win rate × payoff` to clear `1`. A low win rate is fine if the payoff
is high (trend following), and a low payoff is fine if the win rate is high (mean reversion).
**Win rate** asks whether a trade made money. **Success rate** asks whether it exited the way you
designed (on a stop, target or signal) versus being closed because it ran out of time or the test
ended. A trade can be a win that never hit your target, or a loss that cleanly hit its stop. Read
the two together: a high win rate with a low success rate means your exits aren't doing the work.
## Long vs short
The ledger is also split by direction, so you can tell a genuinely two-sided edge from one that only
works one way.
| Metric | Meaning |
| --- | --- |
| Long / short win rate | Win rate computed separately for long and short trades |
| Long / short profit factor, expectancy, avg R | The same trade-quality read, per direction |
A strategy that's only profitable long in a bull market is a very different bet from one that works
both ways, this is where you catch it before it costs you.
## Overfitting aware statistics
A raw Sharpe is inflated by how many configurations you tried. Once a parameter search records the
trials, these statistics appear on the metric cards, an honest read no other natural language tool
ships.
| Metric | Corrects for | How to read it |
| --- | --- | --- |
| Probabilistic Sharpe (PSR) | Short samples & fat tails | Higher = more confident the Sharpe beats 0 |
| Deflated Sharpe (DSR) | How many configs you tried | ≥95% = survives the search; a big drop vs PSR = search luck |
| Overfit Probability (PBO) | In sample best failing out of sample (grid / joint sweep only) | Low is good; ≥50% = likely overfit |
| Haircut Sharpe | Bonferroni correction for T trials | The Sharpe you can still claim after the search |
| Min. backtest length | Sample too short for the search | Warns when history can't support that many trials |
Your query compiles deterministically to a fixed strategy spec, then a fixed engine scores it. The
AI never sees, ranks, or tunes the numbers, so it can't steer toward a good looking result. Same
query ⇒ same spec fingerprint ⇒ same test. Signals use only closed bar data (no look ahead). The
results header shows a Reproducible chip and, when a hold out split exists, an always on
out of sample verdict.
## How grading uses these
The letter grade you see on each run is not a single metric. It's a blend across four pillars, so a
strategy can't earn an A by maximising return while ignoring risk.
| Pillar | Driven mainly by |
| --- | --- |
| Profit | Total return, profit factor, expectancy |
| Risk | Max drawdown, Sharpe / Sortino |
| Consistency | Win rate, equity curve smoothness, robustness checks |
| Edge | Sample size and the overfitting aware statistics above |
Use the grade to triage which runs deserve a closer look, then validate the survivors with
out of sample and Monte Carlo before you trust an edge.
Ready to see them live? Run a strategy and read its report end to end.