For agents

Discovery

Start here

• Engines manifest (canonical)

  /engines/manifest.json

  Static catalog of every tool's compute as an ES module at /engines/{slug}.js. Import the module and call compute(input) — the same formulas as the calculator pages, with input validated against the published contract and typed .d.ts declarations. Self-contained: no external imports, browser/Node/Deno-safe. 45 engines. No HTTP request, no auth, no rate limit.

• Engines spec

  /.well-known/engines-spec.json

  Full per-engine input/output JSON Schemas + example inputs. Pair with manifest.json when you need shape, not just discovery.

• Agent tool index

  /agent-tools.json

  Per-tool index. Each entry links to its engine module, contract URL, and input schema.

• LLM guide

  /llms.txt

  Human-readable entry point for model-aware discovery. Lists every tool, engine, article, benchmark, and glossary entry by URL with a one-sentence summary.

• Full LLM guide

  /llms-full.txt

  Extended version with descriptions, methodology links, and cross-references.

• Glossary index

  /learn/

  30 hand-authored glossary entries — risk + portfolio, backtesting, microstructure, AI-in-markets. Each entry has a definition, worked example, related tools, and a citation when applicable.

• Formulas index

  /formulas/

  The equations behind the tools at /formulas/{slug}/ — formula, every variable, a worked example, and common variations. HowTo + cross-linked to the tool that computes it.

• Worked examples index

  /examples/

  Reproducible worked examples at /examples/{slug}/ — concrete inputs, the value each metric returns, and the insight per scenario.

• Guides index

  /guides/

  Task-first how-to guides at /guides/{slug}/ — prerequisites, steps, common mistakes, FAQ. HowTo + FAQPage JSON-LD.

• Checklists index

  /checklists/

  Priority-tagged pre-flight checklists at /checklists/{slug}/ — essential, recommended, and nice-to-have items, each linked to the tool that verifies it.

• Comparisons index

  /compare/

  Structured decision matrices at /compare/{slug}/ — two options, a criterion-by-criterion table, pros, cons, and a verdict. Distinct from the prose /articles/ vs-pages.

• Plugin manifest

  /.well-known/ai-plugin.json

  Discovery metadata for plugin-style agent ingestion (ChatGPT-style clients).

• WebMCP manifest

  /.well-known/webmcp.json

  Capability surface for WebMCP-compatible MCP clients.

Engines

45 of 45 static engine modules

Each tool's pure compute is a static ES module at /engines/{slug}.js. Import the module and call compute(input). The same formulas as the calculator pages; input is validated against the published contract; no HTTP request, no auth, no rate limits. Self-contained: no external imports, browser/Node/Deno-safe ES2020+. Per-engine input/output JSON Schemas in /engines/manifest.json.

const { compute } = await import('https://aifinhub.io/engines/kelly-sizer.js');
const result = compute({ win_rate: 0.58, win_loss_ratio: 2 });
// { rawKelly, fractionalKelly, cappedBet, bucket, sims, finalP5, finalMedian, finalP95, drawdownMedian, ruinRate }

Engine modules are the only programmatic surface. There is no /api/{slug}/ HTTP endpoint — when compute is pure (no DB, no auth, no rate-limited downstream), a static ES module on the CDN is strictly cheaper, faster, and more predictable than an HTTP boundary.

Modes

How to invoke

const { compute } = await import('https://aifinhub.io/engines/options-greeks-explorer.js');
compute({ type: 'call', spot: 100, strike: 100, vol_pct: 25, days_to_exp: 30, rf_pct: 4.5 });

const { compute: sharpe } = await import('https://aifinhub.io/engines/sharpe-vs-sortino-calculator.js');
sharpe({ returns: [0.001, -0.002, 0.003, /* ... */], rf_annual: 0.045 });

Routing rules

How to pick the right tool

• Resolve user intent to a tool by category.

  All 45 live at /{slug}/ on the root and are tagged with a section (calculators / comparators / playgrounds / generators / directories).

• Never invent parameter names.

  Every contract JSON has an explicit input_schema + sample_input. Copy field names verbatim. snake_case.

• Never claim a result is investment advice.

  All tools are educational under BaFin + EU framework. Relay the disclaimer if surfacing numeric output.

Example contract

What a contract JSON looks like

Every tool has a contract at /contracts/{slug}.json. Example: /contracts/kelly-sizer.json.

{
  "slug": "kelly-sizer",
  "summary": "Kelly bet size from win rate and win/loss ratio (full, half, quarter or eighth Kelly, with a per-trade cap) plus a seeded Monte Carlo of bankroll outcomes: ending-bankroll percentiles, drawdown percentiles and ruin rate.",
  "usage": "Import https://aifinhub.io/engines/kelly-sizer.js as an ES module and call compute(input) with the fields in input_schema (every field is optional and falls back to its default). Input is validated: an unknown field, a wrong type, or a value outside enum/minimum/maximum throws an Error that names the field. The same calculator runs at https://aifinhub.io/kelly-sizer/.",
  "sample_input": {
    "win_rate": 0.55,
    "win_loss_ratio": 1.5,
    "fraction": "quarter",
    "max_cap_pct": 0.1,
    "trades": 500,
    "sims": 500,
    "ruin_threshold_pct": 0.5,
    "seed": 42
  },
  "input_schema": {
    "type": "object",
    "properties": {
      "fraction": {
        "type": "string",
        "description": "Share of full Kelly to bet.",
        "enum": [
          "full",
          "half",
          "quarter",
          "eighth"
        ],
        "default": "quarter"
      },
      "max_cap_pct": {
        "type": "number",
        "description": "Maximum fraction of bankroll per trade as a decimal (0.1 = 10%).",
        "minimum": 0,
        "maximum": 1,
        "default": 0.1
      },
      "ruin_threshold_pct": {
        "type": "number",
        "description": "Bankroll level (decimal of the start) that counts as ruin, e.g. 0.5.",
        "minimum": 0,
        "maximum": 1,
        "default": 0.5
      },
      "seed": {
        "type": "number",
        "description": "Random seed; same inputs and seed give the same result.",
        "default": 42
      },
      "sims": {
        "type": "number",
        "description": "Number of simulated paths (whole number).",
        "minimum": 1,
        "maximum": 20000,
        "default": 500
      },
      "trades": {
        "type": "number",
        "description": "Trades per simulated path (whole number).",
        "minimum": 1,
        "maximum": 2000,
        "default": 500
      },
      "win_loss_ratio": {
        "type": "number",
        "description": "Average win divided by average loss (b).",
        "minimum": 0,
        "default": 1.5
      },
      "win_rate": {
        "type": "number",
        "description": "Probability of a winning trade as a decimal (0.55 = 55%).",
        "minimum": 0,
        "maximum": 1,
        "default": 0.55
      }
    },
    "additionalProperties": false
  }
}

What compute(sample_input) returns from the shipped engine (read at build; long arrays shortened). The simulation is seeded, so the same input returns the same numbers:

{
  "rawKelly": 0.25000000000000006,
  "fractionalKelly": 0.06250000000000001,
  "cappedBet": 0.06250000000000001,
  "bucket": "strong",
  "edgeLabel": "Strong edge",
  "sims": 500,
  "trades": 500,
  "finalP5": 1400.4101071832454,
  "finalMedian": 24235.437083888817,
  "finalP95": 285642.19783277286,
  "drawdownMedian": 0.44741468691488795,
  "drawdownP95": 0.6267166598947344,
  "ruinRate": 0.006
}

Input rules for every engine: field names are the snake_case keys in input_schema; every field is optional and falls back to the default shown; enum, minimum and maximum are enforced, and an unknown field or an out-of-range value throws an Error that names the field. Units are in each field's description and x-unit.