「quantitative」の検索結果

Constructs proprietary factor definitions with signal specification, universe application, and orthogonalization methodology. Use when defining custom factors, creating proprietary signals, or building factor libraries.

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Constructs stat arb strategies with pair selection, signal generation, and portfolio optimization under market neutrality constraints. Use when building stat arb models, designing market-neutral strategies, or optimizing pair portfolios.

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Structures backtesting methodology with out-of-sample testing, cross-validation, and overfitting detection techniques. Use when validating backtests, detecting overfitting, or ensuring backtest robustness.

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Constructs risk parity allocation with equal risk contribution, leverage optimization, and asset class volatility targeting. Use when building risk parity, equalizing risk contribution, or designing leveraged balanced portfolios.

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Evaluates factor timing strategies with macro regime indicators, valuation spreads, and momentum signals for factor rotation. Use when analyzing factor timing, evaluating rotation signals, or designing tactical factor allocation.

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Evaluates ML applications in investment with feature engineering, model selection, and implementation considerations for alpha generation. Use when evaluating ML for investing, designing ML pipelines, or assessing ML strategy feasibility.

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Evaluates signal half-life, turnover implications, and capacity constraints for systematic alpha factors. Use when analyzing signal persistence, evaluating factor decay, or estimating strategy capacity.

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Evaluates factor crowding with positioning analysis, valuation spread monitoring, and unwind risk assessment. Use when analyzing factor crowding, assessing unwind risk, or monitoring factor valuation extremes.

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Evaluates alternative data sources including satellite, NLP sentiment, web scraping, and geolocation for alpha signal generation. Use when analyzing alt data, evaluating new data sources, or integrating non-traditional signals.

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Measures implementation shortfall with paper portfolio comparison, delay cost attribution, and execution quality assessment. Use when measuring implementation shortfall, analyzing execution quality, or attributing trading costs.

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Use when ingesting text samples to extract quantitative writing style metrics. Produces a structured JSON style profile with vocabulary patterns, sentence structure, tone markers, and formatting preferences. Invoke before voice-clone-generator to establish style constraints.

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Evaluate an interface from a UX perspective, assessing hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring and actionable feedback. Use when the user wants an overall design or UX review—not when the main need is measurable accessibility/performance diagnosis, or final micro-detail polish.

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Specialized research and analysis prompt methodology for Claude. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output) for data analysis, policy research, systematic evidence review, investigative research, and research-specific deliverables. Use when building prompts for quantitative data interpretation, evidence-based policy recommendations, literature reviews, causal analysis, hypothesis-driven investigation, or briefing documents. Trigger on: "prompt for data analysis," "research prompt," "policy brief prompt," "literature review prompt," "prompt for investigating," "systematic review prompt," "build a prompt for evidence synthesis," "causal analysis prompt," "briefing document prompt." Also use when user describes a research or analytical task and needs a structured Claude prompt for it. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or auditing Claude Projects (use rootnode-project-audit if available).

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Systematically interpret ultraviolet-visible absorption spectra to identify chromophores, classify electronic transitions, apply Woodward-Fieser rules for conjugated systems, and perform quantitative analysis using the Beer-Lambert law.

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Evaluates market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.

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Use when the user asks to rank deployment platforms and stacks against their product with quantitative trade-offs. Not for source or remote mutation.

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Expert quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and high-frequency trading with focus on mathematical rigor, performance optimization, and profitable strategy development.

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Use when a task needs the judgment of a central bank governor — setting a policy rate decision against a dual mandate, deciding whether a distressed financial institution needs liquidity support or resolution, calibrating forward guidance language against credibility risk, weighing quantitative easing or tightening at the effective lower bound, or drafting the public communication that accompanies a policy decision.

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Checks DRY/KISS/YAGNI/architecture compliance with quantitative Code Quality Score. Validates decisions via MCP Ref. Reports with SEC-/PERF-/MNT-/ARCH-/BP-/OPT- prefixes.

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Use when adversarially reviewing a document that makes formal or technical claims — math derivations, physics papers, statistical analyses, benchmark reports, whitepapers. Inventories every equation and quantitative claim, verifies each AS NAMED in the text (never a paraphrase or a neighboring statement), and classifies VERIFIED / REFUTED / UNVERIFIABLE / VACUOUS. Triggers on "check this paper", "verify these claims", "is this derivation right", "review this proof", "audit this benchmark", "does the math hold up". For source-code review see code-review-and-quality; for skill/agent library audits see skill-library-review; for content quality scoring see content-ops.

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Scientific research engine for hypothesis testing, literature gap analysis, experimental validation, and data-driven discovery. Enforces adversarial review (Reviewer 2), 32 quality gates, tree search over hypotheses, confounder harness for quantitative claims, and serendipity detection. TRIGGER when: user asks to analyze scientific data, test hypotheses, validate findings, search for research gaps, design experiments, or investigate results. DO NOT TRIGGER when: pure code review, documentation writing, devops tasks, general conversation, or non-scientific data transformation.

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Quantitative agent evaluation using 4-metric framework (correctness/step_ratio/tool_call_ratio/latency_ratio) with ideal trajectory annotation and capability-categorical taxonomy. Use when measuring agent efficiency, comparing agent variants, or gating new agents through correctness→efficiency phases. Complements harness-eval (SE benchmarks) and evaluator-optimizer (qualitative rubric).

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Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.

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Search arXiv preprint repository for papers in physics, mathematics, computer science, quantitative biology, and related fields

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