1. API
  2. Screeners API

Screeners API

The whole market,
one request.

Run the platform's preset screens over every liquid name, read the market's volatility regime with sector and ETF heatmaps, find the names whose IV is stretched against SPY, and check one chain for mispriced butterflies and skew.

Get a token, from $55/mo 4 endpoints See a real response
4Endpoints in this family
4Matching MCP tools
60/minPer token, 1,000 an hour

A real response

This is what
comes back.

Captured from the live endpoint on 2026-09-08, lists cut to a few rows so it fits on a page. The shape is exactly what your code receives. Run it on your own ticker from the endpoint page.

GET /api/mcp/data/screen?screen_type=high_iv_rank&limit=5200, application/json
{
  "results": [
    {
      "asset_type": "Stock",
      "avg_opt_volume": 181.0,
      "beta": 0.64,
      "chg_1m": 11.05,
      "chg_1w": 14.47,
      "contango": -30.8,
      "days_to_earnings": 57,
      "hv20d": 34.5,
      "imp_earn_move": 16.18,
      "iv30d": 243.0,
      "iv60d": 177.2,
      "iv_percentile": 100,
      "market_cap": "$1.7B",
      "market_cap_raw": 1703777000.0,
      "next_earnings": "2026-11-04",
      "price": 24.86,
      "sector": "Healthcare",
      "skew_pctile": 78.0,
      "stock_volume": 44616,
      "ticker": "TYRA",
      "vrp": null
    },
    {
      "asset_type": "Stock",
      "avg_opt_volume": 76.0,
      "beta": 1.34,
      "chg_1m": 7.87,
      "chg_1w": -6.13,
      "contango": 5.76,
      "days_to_earnings": 58,
      "hv20d": 36.3,
      "imp_earn_move": 15.12,
      "iv30d": 113.4,
      "iv60d": 115.7,
      "iv_percentile": 100,
      "market_cap": "$2.2B",
      "market_cap_raw": 2196446000.0,
      "next_earnings": "2026-11-05",
      "price": 47.65,
      "sector": "Healthcare",
      "skew_pctile": 46.0,
      "stock_volume": 6826,
      "ticker": "RAPP",
      "vrp": 77.1
    },
    {
      "asset_type": "ETF",
      "avg_opt_volume": 14.0,
      "beta": 0.61,
      "chg_1m": 7.91,
      "chg_1w": 1.71,
      "contango": -1.08,
      "days_to_earnings": null,
      "hv20d": 13.7,
      "imp_earn_move": 0.0,
      "iv30d": 25.0,
      "iv60d": 25.0,
      "iv_percentile": 100,
      "market_cap": "$5.2B",
      "market_cap_raw": 5212689000.0,
      "next_earnings": null,
      "price": 79.29,
      "sector": "Materials",
      "skew_pctile": 57.0,
      "stock_volume": 7381,
      "ticker": "GNR",
      "vrp": 11.4
    }
  ],
  "scan_time": "2026-09-08T07:50:11",
  "screen_description": "Tickers with elevated IV: selling premium opportunities",
  "screen_name": "High IV Rank",
  "screen_type": "high_iv_rank",
  "total_results": 298,
  "total_scanned": 5963,
  "universe_size": 2318
}

Every response is wrapped as {"success": true, "data": {...}}. Errors use the same envelope with an error string and a status code that means what it says.

Call it three ways. Same token, same JSON.

curlBearer token from Account, then API Access
curl -H "Authorization: Bearer avmcp_YOUR_TOKEN" \
     "https://apexvol.com/api/mcp/data/screen?screen_type=high_iv_rank&limit=5"
Pythonrequests, nothing else
import requests

headers = {"Authorization": "Bearer avmcp_YOUR_TOKEN"}
params = {"screen_type": "high_iv_rank", "limit": "5"}
r = requests.get("https://apexvol.com/api/mcp/data/screen", headers=headers, params=params)
r.raise_for_status()
data = r.json()["data"]
print(data["screen_name"])
In Claude, Cursor or ChatGPTthrough the MCP server, no code
You

Screen for the highest IV rank stocks over 5 billion market cap.

The assistant calls screen_market and answers from the JSON above.

More prompts for this family in the prompt library. Set the server up in Claude Desktop, Claude Code, Cursor, ChatGPT, VS Code, Windsurf or Gemini CLI.

The family

4 endpoints,
one prefix.

All under https://apexvol.com/api/mcp/data. Each card opens a page with the parameters and their bounds, every response field, a real captured response and a console that runs it on your ticker.

GET /market-overview What is the volatility regime across the market today? Market-wide vol stats, sector heatmap, index prices, breadth, movers and earnings this week. Premium GET /relative-value-scan Whose IV is stretched versus SPY, and which pairs look rich against cheap? Market-wide IV-to-SPY mean-reversion stretches, or rich-versus-cheap pairs. Pro GET /screen Which stocks have the highest IV rank right now? Preset screens over the whole universe: high IV rank, high VRP, earnings this week, skew, contango and more. Premium GET /vol-arb-scan Are there mispriced butterflies or skew trades on SPY right now? Cross-strike checks: butterfly mispricings, skew trades, calendars and parity violations. Pro
Download as Markdown Copy for your AI

The Markdown twin of this family, sized for a context window.

Field by field

What each
number means.

results[].iv30d, hv20d, vrp
Thirty-day implied, twenty-day realized and the premium between them, all in annualised percentage points.
results[].iv_percentile
Where today's IV sits in its one-year range, 0 to 100.
market_stats.market_regime
The market-wide label from average IV, VRP and the share of names at high IV.
sector_heatmap[]
Per-sector average IV and VRP behind the regime read.
results[].z_score, direction
On the relative-value scan: how far the IV-to-SPY ratio sits from its one-year mean, and which way it should revert.
butterfly_mispricings[].edge
On the arbitrage scan: the theoretical edge in a mispriced butterfly, in dollars per one-lot.

The same data, in plain English

4 MCP tools
wrap this family.

Connect the ApexVol MCP server and an assistant can call these by name. One URL, a sign-in, no token to paste.

  • scan_volatility_arb
  • screen_market
  • get_market_overview
  • scan_relative_value

Set it up in:

What people build

Three things
this is for.

Daily premium-selling listRun high_iv_rank then high_vrp, intersect them, drop names with earnings inside ten days. Regime gateRead market_regime once a morning and size short-vol positions off it. Pairs ideasTake the vol_pairs screen or the relative-value scan and price the rich leg against the cheap one.

Access

Auth
Bearer token, prefix avmcp_, created under Account, then API Access. Shown once, hashed at rest, rotates in one click.
Limits
60 requests a minute and 1,000 an hour per token, plus a monthly allowance by plan. Every response carries the remaining counts in headers.
Plans
Included with every paid or trial plan from $55 a month. Each endpoint follows the tier of its web feature; the cards above show which.
Spec
OpenAPI 3.1 at /docs/api/openapi.json, this family as one Markdown file, the whole reference at /llms-full.txt. Shared conventions, the error model and the changelog.

Questions

Asked before
the first call.

Which screens exist?

high_iv_rank, low_iv_rank, high_vrp, earnings_this_week, high_skew, steep_contango, mean_reversion, vol_pairs, decorrelation, unusual_volume and pin_risk. Pass list to get the current set with descriptions.

How big is the universe?

The liquid US options universe the data feed covers. Every result carries total_scanned and universe_size so you know what the screen looked at.

How fresh is it?

The analytics behind the screens refresh each trading day; the market overview adds intraday index prices.

Which plan?

The screen and the market overview are Premium. The relative-value scan and the arbitrage scan are Pro.

Next

Other families
and the pages behind them.

Options ScreenerThe same presets, on the page, with filters. Screener guideHow to filter by IV, Greeks, volume and earnings. Highest VRP stocksThe ten richest premiums, refreshed every trading day.

Included with
every paid plan.

From $55 a month, tier-matched: your token queries the data your plan includes and Pro unlocks the full surface.

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