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nport

by bensacc
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nport

基于 SEC 表格 N-PORT 和表格 N-CEN 构建的规范化基金持仓与服务提供商图谱，与 GLEIF LEI 注册表关联，通过 MCP 提供给代理。

设计：docs/design.md。 关于源数据的经验笔记：docs/data-notes.md。

状态：仅摄取层。 原始层和 Parquet 层端到端落地；dbt 转换、MCP 服务器和 Terraform 尚未构建。

快速开始

uv sync
export SEC_USER_AGENT="Your Name your@email.com"   # the SEC throttles anonymous traffic

uv run nport-ingest periods nport    # what quarters are published upstream
uv run nport-ingest all              # N-PORT + N-CEN + GLEIF, latest available
uv run nport-ingest status           # what is landed, from the manifests

落地全部三个源需要几分钟，并在 ./data 下写入约 1 GB。要落地单个源或更早的季度：

uv run nport-ingest land nport --period 2026q1
uv run nport-ingest land ncen --raw-only     # skip Parquet conversion
uv run nport-ingest land gleif --force       # re-download and re-convert

Related MCP server: civicgraph-mcp

存储

NPORT_DATA_ROOT 控制所有内容的存放位置，是本地迭代与在 ECS 中运行之间的唯一区别：

export NPORT_DATA_ROOT=./data                 # default
export NPORT_DATA_ROOT=s3://my-bucket/nport   # needs the `s3` extra: uv sync --extra s3

布局，依据 docs/design.md 第 2 节：

{root}/raw/{source}/{period}/{archive}.zip             original bytes, immutable
{root}/parquet/{table}/period={period}/data.parquet     zstd, all columns VARCHAR
{root}/parquet/_manifest/{source}/period={period}/
    manifest.json    source URL, raw SHA-256, per-table row counts
    schema.json      column names, types, descriptions and PKs, from the
                     CSVW sidecar the SEC ships inside each archive

除非传入 --force，否则重新运行已落地的期间是空操作，这使得任务可以安全地从计划中重试。如果 SEC 以相同文件名重新发布某个季度，运行会警告字节数发生变化，而不是静默提供过期数据。

摄取层中的设计决策

每个 Parquet 列都是 VARCHAR。 类型定义属于 dbt staging/。源数据中充满了会破坏类型推断的值——日期如 28-FEB-2026、数字如 -.0091723714，以及出现在数字列中的 N/A——因此这里的推断要么失败，要么静默强制转换，而且会把业务逻辑放在必须从原始字节保持可复现的那一层。

下载 URL 是发现的，而非构造的。 大多数 SEC 归档遵循 {period}_{source}.zip 模式，但几个 N-CEN 季度在重新发布后以 2025q1_ncen_0.zip 形式提供。索引页面会被抓取以获取 href，并以常规模式作为回退。

归档自身的模式元数据会被提取。 两个 SEC ZIP 都包含一个 CSVW *_metadata.json，声明了每个表的主键、列类型和列描述。这是 transform/ 中粒度断言的权威输入，因此它被写出为 schema.json，而不是留在 ZIP 中。

布局

src/nport/ingest/        fetch -> validate -> land as Parquet
    sources/             one module per upstream source
    convert.py           ZIP of delimited text -> Parquet, via DuckDB
    land.py              orchestration and manifests
    storage.py           URI addressing over fsspec (local or S3)
transform/               dbt project (dbt-duckdb)          [not built]
src/nport/server/        FastMCP server                    [not built]
evals/                   scored question set               [not built]
infra/                   Terraform                         [not built]

开发

uv run ruff check src/ && uv run ruff format src/
uv run pytest

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