tune-parameters

tune-parameters is a skill for Claude Code, Codex from xingwudao/open-xquant. It costs 37 tokens per session (706 once invoked), scanned A, original, MIT.

A parameter-search workflow for open-xquant trading strategies using grid search, walk-forward validation, time-series cross-validation, and overfitting checks.

In plain words
What is it for?
It is for optimising approved strategy parameters across training and out-of-sample periods after the base strategy and evaluation metric are defined.
Why use it?
It helps compare strategy settings while reducing the risk of treating accidental results from historical data as reliable performance.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for optimising approved strategy parameters across training and out-of-sample periods after the base strategy and evaluation metric are defined.

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Install with agentmods
npx agentmods add skills/xingwudao/open-xquant/tune-parameters
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add xingwudao/open-xquant --skill tune-parameters
Clone the repo
git clone --depth 1 https://github.com/xingwudao/open-xquant

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 706 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00037 $0.00706
Opus 5 $0.00018 $0.00353
Sonnet 5 $0.00007 $0.00141
Haiku 4.5 $0.00004 $0.00071

Measured 9d ago against content hash 797e54b71dc4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

tune-parameters scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agent/skills/tune-parameters/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Parameter Tuner

You search parameters without turning in-sample luck into a claim.

Preconditions

  • The base strategy logic must already be clear.
  • The base spec or SDK strategy must pass validation.
  • Data must cover both training and OOS periods.
  • The user must approve the metric and search ranges.

Warn when the total grid exceeds 100 combinations.

SDK Pattern

The optimizer API works on Strategy objects, not directly on a YAML spec. If starting from a spec, compile it first or follow examples/modules/07_optimize.py.

from oxq.optimize.paramset import ParameterSet
from oxq.optimize.search import GridSearch

paramset = ParameterSet(name="sma_tuning")
paramset.add("sma_10", "period", list(range(5, 30, 5)))
paramset.add("sma_50", "period", list(range(30, 100, 20)))
paramset.add_constraint("sma_10.period < sma_50.period")

search = GridSearch(paramset).run(
    strategy=strategy,
    market=market,
    broker_factory=broker_factory,
    start="2018-01-01",
    end="2021-12-31",
    metric="sharpe_ratio",
)

Parameter component names must match strategy component names such as indicator aliases in required_indicators. If a name does not match, the parameter has no effect.

Walk-Forward Required

from oxq.optimize.walk_forward import WalkForward

wf = WalkForward(
    paramset=paramset,
    train_period="2Y",
    test_period="1Y",
    step="1Y",
)
wf_result = wf.run(
    strategy=strategy,
    market=market,
    broker_factory=broker_factory,
    start="2018-01-01",
    end="2024-12-31",
    metric="sharpe_ratio",
)
print(wf_result.deterioration())

Time-Series CV

from oxq.optimize.validation import TimeSeriesCV

cv = TimeSeriesCV(n_splits=4, expanding=True)
cv_result = cv.cross_validate(
    strategy=strategy,
    market=market,
    broker_factory=broker_factory,
    start="2018-01-01",
    end="2024-12-31",
    paramset=paramset,
    metric="sharpe_ratio",
)

Overfit Signals

  • IS Sharpe far above OOS Sharpe
  • best parameters lie on search boundary
  • OOS return or Sharpe turns negative
  • tiny parameter changes destroy performance
  • selected configuration has very few trades

Read the full file on GitHub · 107 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 107 lines · 37 tokens per session scan A 797e54b71dc4

Subscribe to this mod's changes

tune-parameters is a skill published in the GitHub repository xingwudao/open-xquant (127 stars, last pushed 7d ago), licensed MIT. It adds 37 tokens to every session and 706 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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