quant-real-factor-10d-compressed-range-position

Name: quant-real-factor-10d-compressed-range-position
Author: lyyyxin

Use when computing the 10D Compressed Range Position factor from user-supplied OHLCV data or reviewing its bundled real-data validation metrics.

by lyyyxin(0) 0 installs

Free

Free account. Installing gives you the manifest plus copy-paste snippets.

See reviews

About

Imported from lyyyxin/lxiny (.quantskills/packages/skills/skill-quant-factor-directional-alpha/factors/R767-10d-compressed-range-position/SKILL.md). Install upstream with npx skills add lyyyxin/lxiny --skill R767-10d-compressed-range-position. Copyright stays with the author.

10D Compressed Range Position

Use this Skill to compute 10日压缩区间位置 / 10D Compressed Range Position from caller-provided OHLCV data.

Workflow

Load a pandas.DataFrame with open, high, low, close, volume; include date and symbol for cross-sectional research.
Call scripts/factor.py::compute_factor(df) to compute the factor column.
Call generate_signals(df) for a simple rank-based long/short signal.
Review validation_real/report.md before using the factor in a model.

Runtime Contract

Framework-neutral Python: pandas and numpy.
The caller owns data vendor, universe, calendar, costs, slippage, and execution modeling.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

# after Install: the listing is in your workspace's routing pool - a plan picks it for its slot
curl -s -X POST https://api.opensmartroute.ai/api/v1/route -H 'Authorization: Bearer $OSR_API_KEY' -H 'Content-Type: application/json' -d '{"text": "...", "plan": true}'

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

lyyyxin-lxiny-r767-10d-compressed-range-position.ocm.jsonjson

{
  "ocm": "1",
  "id": "lyyyxin-lxiny-r767-10d-compressed-range-position",
  "kind": "skill",
  "name": "quant-real-factor-10d-compressed-range-position",
  "description": "Use when computing the 10D Compressed Range Position factor from user-supplied OHLCV data or reviewing its bundled real-data validation metrics.",
  "publisher": "lyyyxin",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "github"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Use when computing the 10D Compressed Range Position factor from user-supplied OHLCV data or reviewing its bundled real-data validation metrics."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "github",
      "repository": "https://github.com/lyyyxin/lxiny",
      "path": ".quantskills/packages/skills/skill-quant-factor-directional-alpha/factors/R767-10d-compressed-range-position/SKILL.md",
      "ref": "e75bb9b005ac0927e97e6d342401479fad491f93",
      "url": "https://github.com/lyyyxin/lxiny/blob/e75bb9b005ac0927e97e6d342401479fad491f93/.quantskills/packages/skills/skill-quant-factor-directional-alpha/factors/R767-10d-compressed-range-position/SKILL.md",
      "key": "lyyyxin/lxiny/.quantskills/packages/skills/skill-quant-factor-directional-alpha/factors/R767-10d-compressed-range-position/SKILL.md"
    }
  },
  "instructions": "# 10D Compressed Range Position\n\nUse this Skill to compute `10日压缩区间位置` / `10D Compressed Range Position` from caller-provided OHLCV data.\n\n## Workflow\n\n1. Load a `pandas.DataFrame` with `open`, `high`, `low`, `close`, `volume`; include `date` and `symbol` for cross-sectional research.\n2. Call `scripts/factor.py::compute_factor(df)` to compute the factor column.\n3. Call `generate_signals(df)` for a simple rank-based long/short signal.\n4. Review `validation_real/report.md` before using the factor in a model.\n\n## Runtime Contract\n\n- Framework-neutral Python: `pandas` and `numpy`.\n- The caller own",
  "cost": {
    "context_tokens": 168
  }
}

Fetch it by URL: GET /api/v1/registry/lyyyxin-lxiny-r767-10d-compressed-range-position/manifest?version=1.0.0