> ## Documentation Index > Fetch the complete documentation index at: https://docs.0xinsider.com/llms.txt > Use this file to discover all available pages before exploring further. # Quant Metrics > The curated risk and performance metrics behind a trader's scores, what each field means, and how to read them. `quant_metrics` is a curated set of risk and performance metrics for a trader. It is where the two headline scores live, `smart_score` and `copy_score`, alongside the risk-adjusted return and consistency measures they are built from. The field set is fixed and documented: exactly nine fields, each a number or `null`. It is not a raw database dump, and it does not grow silently as we add internal columns. New internal metrics stay internal until they are curated into this contract. ## How to get it `quant_metrics` is an [expanded field](/concepts/expand). It is omitted from trader responses unless you ask for it: ```bash theme={null} # Single trader curl -H "Authorization: Bearer $OXINSIDER_API_KEY" \ "https://api.0xinsider.com/api/v1/trader/0x204f...?expand=quant_metrics" # Many traders at once curl -H "Authorization: Bearer $OXINSIDER_API_KEY" \ -X POST "https://api.0xinsider.com/api/v1/traders/batch" \ -H "Content-Type: application/json" \ -d '{"traders": ["0x204f...", "swisstony"], "expand": ["quant_metrics"]}' ``` It appears on [`GET /api/v1/trader/{address}`](/api-reference/endpoint/get-trader) and [`POST /api/v1/traders/batch`](/api-reference/endpoint/batch-get-traders). The field is present only when the trader has computed metrics; when present, all nine keys are present. ## The two headline scores Both scores are 0-100 and higher is better. They answer different questions. * `smart_score` measures **skill**: how good is this trader, on a risk-adjusted basis, relative to everyone else? * `copy_score` measures **copyability**: how safely could you follow this trader, after discounting traits that make a strategy hard to replicate? `smart_score` is a weighted blend of the trader's cross-sectional percentiles and a few absolute measures: ``` smart_score = clamp(0, 100, 30 * sharpe_percentile_fraction + 20 * profit_factor_percentile_fraction + 20 * edge_consistency_percentile_fraction + 10 * min(1, return_on_capital / 2) + 10 * equity_smoothness + 10 * (1 - min(1, asset_concentration))) ``` `copy_score` starts from that same base and subtracts penalties for hard-to-replicate behaviour, then clamps to 0-100: | Penalty | When it applies | | ------- | ----------------------------------------------------- | | -20 | Fewer than 50 markets traded (thin track record) | | -15 | Positions are highly concentrated | | -15 | Position sizing exceeds about 2x Kelly (over-betting) | | -10 | Worst single-trade loss exceeds 30% | | -10 | Edge is inconsistent | A trader can have a high `smart_score` and a much lower `copy_score`: genuinely skilled, but running a book that is risky or impractical to mirror. When you are ranking traders to follow or copy, read `copy_score`. When you are ranking pure skill, read `smart_score`. ## The component metrics These are the underlying measures. They are absolute values, not ranks. | Field | Meaning | | ------------------ | -------------------------------------------------------------------------------------------------------------------------- | | `sharpe_30d` | Sharpe ratio over the trailing 30 days (risk-adjusted return; higher is better). Magnitude can be large for small samples. | | `sharpe_7d` | Sharpe ratio over the trailing 7 days. | | `profit_factor` | Gross profit divided by gross loss; greater than 1 is profitable. Capped at 1000 when there are effectively no losses. | | `edge_consistency` | Stability of the trader's edge over time, 0-1 (higher is more consistent). | ## The percentile ranks These place the trader against the whole population, 0-100. They are the inputs the scores lean on, exposed directly so you can see where a number sits in the field. | Field | Meaning | | ------------------------ | ---------------------------------------------------------------- | | `sharpe_percentile` | Percentile rank of the trader's Sharpe ratio versus all traders. | | `pf_percentile` | Percentile rank of profit factor versus all traders. | | `consistency_percentile` | Percentile rank of edge consistency versus all traders. | ## Reading the values * **`null` means insufficient trade history, not zero.** Every field is a number or `null`. A `null` field is one we cannot compute yet for this trader; treating it as `0` will rank a brand-new wallet as the worst possible trader rather than an unknown one. * **Values refresh periodically.** The scores and percentiles are recomputed by the cross-sectional ranking job, not on every request. A trader's percentile moves as the rest of the population moves, even if their own trades do not. * **Scores are 0-100; components are absolute.** `smart_score`, `copy_score`, and the three percentiles are bounded 0-100. `sharpe_30d`/`sharpe_7d` and `profit_factor` are unbounded (profit factor is capped at 1000), so compare them against the percentile fields for context. ## Full response shape ```json theme={null} { "object": "trader", "data": { "id": "trd_0x204f72f35326db932158cba6adff0b9a1da95e14", "username": "swisstony", "grade": "S", "quant_metrics": { "smart_score": 84.2, "copy_score": 74.2, "sharpe_30d": 2.13, "sharpe_7d": 3.44, "profit_factor": 4.1, "edge_consistency": 0.62, "sharpe_percentile": 97.4, "pf_percentile": 88.9, "consistency_percentile": 91.0 } } } ``` ## Related * [Signal scoring](/concepts/signal-scoring) names every score the API exposes and which to reach for. * [Grades](/concepts/grades) explains the headline `S`-`F` grade, the long-run quality verdict that sits above these metrics. * [Expanding responses](/concepts/expand) covers the `expand` parameter and the other expandable fields. * [Trust metadata](/concepts/trust-metadata) exposes per-field provenance, including for `quant_metrics`.