VaR/beta/regression primitives for tech.ml.dataset
Historical-VaR, marginal beta and single-factor regression are standard techniques with
nothing portfolio-specific about them once you're handed a return series. This library
carries the plain-math pieces out of the app that consumes them: quantile/drawdown/VaR,
OLS/conditional-beta/marginal-beta decomposition, return/PnL transforms, and futures
panama-roll adjustment. Nothing here knows about a bond, a portfolio, or a database — it
takes vectors, matrices and tech.ml.datasets and returns numbers.
Same split as svm-techml-smile: the generic technique lives here, the domain wiring (what data feeds it) stays in the app.
[net.clojars.alex314159/var-techml-neanderthal "0.1.1"]
| ns | what's in it |
|---|---|
var-techml-neanderthal.core | re-exports everything below — require this one ns if you don't care about the split |
var-techml-neanderthal.returns | return/PnL series transforms, day/week/month bucketing, one-year/one-year-weeks/one-year-months |
var-techml-neanderthal.regression | single-factor OLS, conditional (up/down) beta, marginal-beta decomposition |
var-techml-neanderthal.risk | quantile, max drawdown, historical VaR over a return series |
var-techml-neanderthal.futures | panama-roll adjustment across a sequence of futures contracts |
Note core aliases via a macro: REPL-driven tooling (Calva, CIDER) resolves the docs, purely
static analysis (clj-kondo, offline Cursive index) doesn't — require the defining ns there.
lein test
Can you improve this documentation? These fine people already did:
Alexandre Almosni & alex314159Edit on GitHub
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