keyed-dynamic-universe

@microprediction · 收录于 2 天前 · 上游提交 1 个月前

Maintain an online covariance over named series whose set changes over time (e.g. assets entering and leaving). Use when observations arrive as dicts keyed by name rather than fixed-length vectors. Wraps precise's keyed / FixedUniverse / DynamicUniverse adapters.

场景资产组合风险监控动态协方差

适合你，如果资产列表不断变化且需要实时协方差矩阵

/ 下载安装

https://github.com/microprediction/precise/tree/main/.claude/skills/keyed-dynamic-universe ↗

/ 通过 npx 安装 校验哈希

npx oh-my-skill add microprediction/precise/keyed-dynamic-universe

/ 通过 bash 安装

curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- microprediction/precise/keyed-dynamic-universe

/ 已经装过?验证本机副本,不用重装

npx oh-my-skill verify microprediction/precise/keyed-dynamic-universe

安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。

333GitHub stars

~412上下文体积 · 单文件

索引托管

怎么用

商店整理自技能原文 · 版本 d89c883 · 表述以原文为准

它做什么

安装后，Claude 能维护一个随名称集合变化而更新的协方差矩阵。它接受以字典形式（如 {"BTC":0.01}）输入的观测，自动处理新名称加入或旧名称退出，并输出一个按名称索引的协方差矩阵。

什么时候触发

当你以字典形式（keyed dict）提供观测数据，例如金融资产收益率序列，且名称集合可能随时间变化时触发。

装好后可以这样说

返回当前协方差矩阵中对应项

新名称自动纳入计算

输出字典或DataFrame

技能原文 SKILL.md作者撰写 · MIT · d89c883

Keyed covariance over a changing universe

In streaming/finance settings observations are dicts keyed by name, and the set of names changes. keyed decorates any positional estimator to consume keyed dicts and emit keyed output.

pip install precise            # add [pandas] for to_frame()

from precise import keyed, EwaCovariance

est = keyed(EwaCovariance(r=0.05), dynamic=True)     # universe may change over time
est.partial_fit({"BTC": 0.01, "ETH": -0.02})         # river-style: also .update / .learn_one
est.partial_fit({"ETH": 0.00, "SOL": 0.03})          # BTC drops out, SOL enters

est.covariance_["ETH"]["SOL"]                         # dict-of-dicts over the live universe
est.to_frame()                                        # pandas DataFrame  (needs [pandas])

Fixed vs dynamic

keyed(est) / dynamic=False → FixedUniverse: one wrapped estimator; missing keys are imputed. Use when the set of names is stable and you just want dict ergonomics.
keyed(est, dynamic=True) → DynamicUniverse: tracks multiple live key-sets with staleness / longevity eviction and assembles a pairwise matrix. Use when names genuinely enter and leave.

Notes

The adapter adds no covariance math of its own — it wraps the positional estimator, so any estimator from the estimate-online-covariance skill works inside it (LedoitWolfCovariance, HuberCovariance, …).
The assembled matrix is projected to the nearest PSD; expect small adjustments when the live universe changes.
To score/compare keyed estimators, extract the dense covariance_ (e.g. via to_frame().values) and use the score-covariance-estimate skill.

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