Strategy generation

Skill artherahq/skills/skills/strategy-generation

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

Install

npx -y skills add artherahq/skills --skill strategy-generation

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

Turn a natural-language trading idea into a disciplined, testable strategy spec and implementation. Trigger for "帮我生成一个动量策略", "写一个BTC均值回归策略", "设计多因子选股", "做一个ETF轮动", "generate a momentum strategy", "build me a trading strategy", "turn this idea into code", or whenever the user (1) describes a trading idea and wants it made concrete, (2) asks for strategy code, (3) wants strategy variants compared, or (4) asks to improve an existing strategy. Fire even for vague asks ("有什么好策略推荐吗" — the answer is a disciplined spec, not a tip). Do NOT trigger for validating an already-backtested result (backtest-validation) or for pure factor evaluation (factor-research).

SKILL.md

3.7 KB, as published. Nobody here has run it

Strategy Generation

A strategy that exists only as prose is a mood. This skill converts ideas into specs that machines can refuse, code that gates can check, and claims that survive the validation pipeline — or die there honestly.

The pipeline (six stages, three gates)

IDEA → SPEC ──gate 1──→ CODE → RETURNS ──gate 2──→ RISK ──gate 3──→ PAPER
        │                          │                  │
        │ validate_strategy_spec   │ backtest-        │ risk-assessment
        │ (this skill's script)    │ validation       │ profile

DATA — establish market, universe, frequency, history budget, and whether the data pipeline is PIT-reviewed (point-in-time-research).
SPEC — fill the contract in references/spec-schema.md: named archetype, testable entry/exit logic, explicit parameters, at least one hard risk control, a cost assumption in bps, and variants_tried.
Gate 1 — python scripts/validate_strategy_spec.py SPEC.json. FAIL means fix the spec; do not write code around a broken spec.
CODE — implement exactly the spec. Parameters come from signal.parameters, not from numbers invented mid-implementation. If implementation forces a change, update the spec and re-run gate 1.
Gate 2 — produce the returns series and run it through backtest-validation with --trials = the spec's variants_tried (updated if more variants were tried during coding — honesty compounds). A FAIL verdict is a result: report it, don't tune until it passes.
Gate 3 — run risk-assessment on the surviving equity curve and holdings. High-risk verdicts require explicit user confirmation.
PAPER — the strongest deployment this skill may recommend is paper trading. Live execution is a user decision outside this skill's authority.

Archetype discipline

Name the archetype (momentum, mean_reversion, multi_factor, pairs_trading, technical, ml_enhanced, event_driven, value) and stay inside its logic. "Momentum with 12 confirming indicators" is not momentum — it is 12 extra parameters begging the overfit preflight to fail.

Counting variants (the honesty ledger)

Every parameter combination tried, universe swapped, or date range adjusted increments variants_tried. The number follows the strategy to backtest-validation's Deflated Sharpe Ratio. Understating it does not improve the strategy; it just moves the lie downstream where the DSR will price it.

Guardrails

No code before the spec passes gate 1.
No performance claims before gate 2; no "expected returns" from thin air.
Forbidden claims ("guaranteed", "risk-free", "稳赚", "保证收益") fail the spec mechanically — the validator scans every string field.
Position sizing "all-in" or absent hard risk controls fail the spec.
Deploy advice caps at paper trading, always.