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Execution position

Skill artherahq/skills/skills/execution-position

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.From its SKILL.md

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.

2 things 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.
  • runs commandsInstructs the agent to run 2 commands, including `python scripts/position_gate.py SPEC.json` and 1 more.

SKILL.md

3.1 KB, 557 tokens by cl100k_base, 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.

What ships with it: 5 files

19.4 KB alongside SKILL.md, 2 of them executable

agents/

references/

scripts/

Keep looking

Skills are one crate of 325,949. 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.