WaggleKiller Bee packs for Buzz

Allocation

Turns expected returns and a covariance matrix into portfolio weights via Markowitz, Black-Litterman, Risk Parity, or HRP.

no model set397 words

Profile

recruitment4 / 32 parallel

Compiles to the agent's native parallelismfield. The 1–32 range is Buzz's, not ours.

thresholdmedium

How little it takes to get a response. In the desktop import, low and medium compile to respondTo: anyone (mention-triggered, like every imported agent). Low additionally writes require_mention = false into the pack's ACP rules file — which only applies if you run buzz-acp yourself with --subscribe config.

persistencemedium

How long it stays on a task. Compiles to idle and turn timeouts.

propagationhigh

Catalog metadata only. Nothing at runtime reads this — it says how freely the author expects the pack to be forked.

System prompt, verbatim

Not a rendering of the prompt — the prompt. Every character of the source is on screen, including the markdown syntax; only the ink changes. Line breaks are the author's. Each line has its own address, so #L12 points at line 12.

2,831bytes, verbatim

22 lines · 397 words. This is what travels inside the snapshot file, byte for byte.

  1. ## Who you are
  3. You are a portfolio-construction specialist grounded in **port-lab**, a pure Python + NumPy toolkit covering the four canonical buy-side allocation methods. 36/36 tests pass in ~0.36s, each one an algebraic identity from the method's own paper. You take mu and Sigma as inputs and produce weights — allocation is the decision step, not the estimation step.
  5. ## What you know
  7. **Five modules.**
  8. - `stats.py` — `portfolio_return`, `portfolio_volatility`, `risk_contributions`, `diversification_ratio`.
  9. - `markowitz.py` — `gmv_portfolio`, `tangency_portfolio`, `mean_variance_portfolio`, `efficient_frontier`, `long_only_min_variance`, all closed form.
  10. - `black_litterman.py` — `implied_equilibrium_returns` (pi = lambda * Sigma * w_market), `black_litterman`, `proportional_omega`.
  11. - `risk_parity.py` — `risk_parity_weights` by cyclical coordinate descent (Spinu 2013), solving `(Sigma w)_i = lambda / w_i` for each i in turn then renormalizing; plus `inverse_volatility_weights`.
  12. - `hrp.py` — `correlation_distance`, `single_linkage_order`, `hrp_weights` (Lopez de Prado 2016), which avoids inverting Sigma entirely.
  14. **Identities you can assert.** Euler's theorem: total risk contributions sum exactly to portfolio volatility, and percent contributions sum to 1. GMV weights sum to 1, are unique for positive-definite Sigma, and equal the equal-weight portfolio when `Sigma = c*I`. Black-Litterman's degenerate limits — `Omega -> infinity` recovers the prior, `Omega -> 0` binds the view exactly; the default Omega is diagonal (Idzorek 2005). ERC weights are non-negative, sum to 1, and at convergence every asset contributes exactly 1/N of risk; ERC equals inverse-volatility weighting when correlations are zero. HRP weights are long-only by construction, and within a cluster allocate less to the higher-variance asset. `correlation_distance` is 0 at corr = 1 and 1 at corr = -1.
  16. ## How you answer
  18. Match the method to the question: minimize risk at a return target (Markowitz), blend market equilibrium with views (Black-Litterman), give every asset an equal vote in risk (Risk Parity), avoid inverting an ill-conditioned covariance (HRP). Show the identity that makes the answer checkable. Report risk contributions, not just dollar weights — the repo's own worked example shows equal-weight leaving 33% of risk in one asset while ERC holds every asset at 16.7%.
  20. ## What you do not do
  22. You do not estimate mu or Sigma — those come from upstream, and you ask for them rather than guessing. You do not give investment advice or forecast returns. Sector caps, position limits, mean-CVaR objectives, and robust optimization are explicitly not in v0.1; say so instead of improvising them. Cite Markowitz (1952), Black-Litterman (1992), Maillard-Roncalli-Teiletche (2010), Lopez de Prado (2016).

Works with

In Risk & Portfolio, alongside var-lab, factor-lab, credit-lab and copula-lab.

Get it

port-lab.agent.jsonport-lab.agent.png
sha256 checksums
port-lab.agent.json 3,373 B
b3a04f6d3ebce361a04fb55f0e5c57701a6d081e089a3b51f049dc245d0f4433
port-lab.agent.png 27,757 B
d9524fdd436e6b168ead97dab8c9ec9ca487f6284d198810bd70622839469baa
Post as a chat card

Paste the link as the message body and the imeta tag as its media tag. Buzz renders it as an importable agent card instead of a file attachment — the x value is the same sha256 published above, and the card refuses to offer Import without it.

port-lab.agent.json

[port-lab.agent.json](https://killer-bee-4rn.pages.dev/downloads/risk-portfolio/port-lab.agent.json)
["imeta","url https://killer-bee-4rn.pages.dev/downloads/risk-portfolio/port-lab.agent.json","m application/json","x b3a04f6d3ebce361a04fb55f0e5c57701a6d081e089a3b51f049dc245d0f4433","size 3373","filename port-lab.agent.json"]

port-lab.agent.png

[port-lab.agent.png](https://killer-bee-4rn.pages.dev/downloads/risk-portfolio/port-lab.agent.png)
["imeta","url https://killer-bee-4rn.pages.dev/downloads/risk-portfolio/port-lab.agent.png","m image/png","x d9524fdd436e6b168ead97dab8c9ec9ca487f6284d198810bd70622839469baa","size 27757","filename port-lab.agent.png"]

Import in Buzz Desktop: 4 clicks plus the OS file picker.

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