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.
npx skills add xingwudao/open-xquant --skill tune-parametersgit clone --depth 1 https://github.com/xingwudao/open-xquantWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xingwudao/open-xquant/tune-parameters)<a href="https://agentmods.dev/skills/xingwudao/open-xquant/tune-parameters"><img src="https://agentmods.dev/badge/skills/xingwudao/open-xquant/tune-parameters/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xingwudao/open-xquant/tune-parameters"><img src="https://agentmods.dev/badge/skills/xingwudao/open-xquant/tune-parameters.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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
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.
- 9d ago First seen · 107 lines · 37 tokens per session scan A 797e54b71dc4
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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