Factor Evaluation
Evaluate a factor library — recompute Information Coefficient (IC), ICIR, win rate, and turnover on held-out data, and surface train→test decay. Use to judge how good a mined library actually is out of sample. Triggers on "evaluate factors", "compute IC", "how good is this library", "factor metrics", "ICIR", "is this factor overfit", "out-of-sample".
Install
$ agentstack add skill-minihellboy-factorminer-factor-evaluation ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-minihellboy-factorminer-factor-evaluation) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Factor Evaluation
Mining proposes factors; evaluation decides whether to believe them. This skill recomputes a library's metrics on a chosen split and exposes overfitting.
See references/metrics.md for precise metric definitions (IC vs. paper-IC, ICIR, redundancy correlation).
Workflow
1. Recompute metrics
factorminer evaluate output/run1/factor_library.json \
--data path/to/market_data.csv \
--period test--period selects the split: train, test, or both. Always lead with test — in-sample IC is not evidence.
2. Read the table
The output table reports, per factor: IC Mean, Paper IC, Abs IC, Paper ICIR, Win%, and Turnover. The summary block gives library-level means and the IC range.
3. Check decay
factorminer evaluate output/run1/factor_library.json --data market_data.csv --period both--period both adds a decay table (train Paper IC → test Paper IC → delta). A large negative delta is the signature of an overfit factor. Report decay honestly; do not quote the train number as the headline.
4. Rank the survivors
To shortlist the strongest signals only:
factorminer evaluate output/run1/factor_library.json --data market_data.csv --period test --top-k 10The top-K-by-IC table is the signal shortlist — the natural handoff to a research-idea workflow that wants to know which quantitative signals are currently working. The MCP screen_factors tool returns this same shortlist directly.
Interpreting the numbers
- IC ≈ 0.03–0.05 out of sample is a respectable single factor on liquid universes.
- ICIR matters more than IC: a small but stable IC beats a large erratic one.
- High turnover quietly erases IC once costs are applied — carry it into
factor-backtest.
Guardrails
- Never present
trainmetrics as the result. The deliverable is thetestnumber. - If every factor decays to ~0 on test, the library failed — say so. Do not search for a split that flatters it.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: minihellboy
- Source: minihellboy/factorminer
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.