agentsclimarketplace

Execution position

Skill artherahq/skills/skills/execution-position

Reusable Agent Skills for quantitative finance research, extracted from the Aria toolchain.

Install
npx -y skills add artherahq/skills --skill execution-position

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Convert a trading signal into a sized, risk-gated PAPER order intent — or a refusal with reasons. Trigger for "这个信号该买多少", "帮我算仓位", "凯利公式下注多少", "现在要不要加仓", "position sizing", "how much should I buy", "simulate this trade", or whenever the user (1) has a signal/decision and asks for the size, (2) wants a trade checked against limits before acting, (3) asks about stops or exposure, or (4) another skill hands over a strategy/rebalance that needs execution shape. Fire even for casual "梭哈吗". Do NOT trigger for portfolio-wide weight construction (portfolio-optimization) or post-hoc risk analysis (risk-assessment). NEVER place live orders.

SKILL.md

3.1 KB, as published. Nobody here has run it

Execution & Position

The distance between a good signal and a good trade is sizing, limits, and costs. This skill is the boring adult at the door: it sizes with declared inputs, checks the hard limits, prices the friction, and emits paper intents only.

Authority boundary (read first)

The gate's output is a paper_only: true intent. A request for live execution FAILS the gate mechanically — live orders are a human decision made outside this skill, in the broker's own interface. This mirrors the platform rule: unimplemented broker paths return errors, never fake fills.

Sizing doctrine

  • vol_target (default): weight = risk budget / annualized vol. Needs a real volatility estimate; the gate refuses to guess when none is supplied.
  • kelly: only from a declared edge (win rate + payoff ratio) — the declaration is the user's claim and ships verbatim in the intent for audit. Computing an edge from sample means is prohibited here (that is backtest-validation's jurisdiction, and even then it is a backtest, not an edge). Fraction hard-capped at 0.25: full Kelly on an estimated edge over-bets by construction.
  • fixed_weight: explicit user weight, still subject to every gate.

Workflow

  1. Assemble the spec (references/order-schema.md): order, portfolio state, limits, market context (vol, ADV, spread). Missing limits get conservative defaults; missing volatility fails vol-target sizing honestly.
  2. Run python scripts/position_gate.py SPEC.json (or --demo).
  3. On PASS/WARN: present the intent — side, delta weight, notional, cost estimate in bps — plus every warning verbatim (a noise_stop warning is more valuable than the intent itself).
  4. On FAIL: the deliverable is the refusal and its reasons. Do not shrink the order just enough to sneak past a limit without telling the user which limit it was.
  5. Execution of the paper intent goes to the platform's paper-trading path; filled paper results can then feed risk-assessment.

Guardrails

  • paper_only on every intent; live requests fail mechanically.
  • No volatility estimate → no vol-target size. No declared edge → no Kelly.
  • Kelly fraction cap 0.25 is not negotiable via prompt.
  • Cost figures are labeled estimates (linear impact placeholder).
  • Liquidity gate: > 10% of ADV fails; no ADV data → check skipped and said so.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.