vs-benchmark-audit

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Aktualisiert12. Juli 2026 um 23:11

Take a book (weights per ticker), compute the daily portfolio return series, and run the full tearsheet with deflated Sharpe correction (Bailey & Lopez de Prado) plus rolling IC vs benchmark. Emits a verdict (real_alpha / possibly_alpha / essentially_beta / underperforming / no_edge_evident) based on DSR significance, alpha annualized, and beta. Answers "is this book actually alpha, honestly?" Requires Stocks Basic.

Installation

Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.

Quelle

rgourley

rgourley/quant-garage

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Basierend auf der SOC-Berufsklassifikation

Finanz- und InvestitionsanalystenWirtschafts- und Finanzberufe·SOC 13-2051

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SKILL.md

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vs-benchmark-audit

description

vs-benchmark-audit

You hand over a book (weights per ticker) and a benchmark (default SPY). The skill pulls daily bars, computes the portfolio return series, and runs the full performance tearsheet with the deflated Sharpe correction, plus a rolling 63-day IC vs the benchmark.

Answers "is this book actually alpha, honestly?" — with a verdict that separates real alpha from beta from noise.

When to invoke

Post-quarter review: did my strategy add anything above beta?
Investment committee prep on a candidate manager or strategy
Auditing a historical backtest with proper DSR correction
The user says "vs benchmark", "alpha vs beta", "is this real"

What you need

Positions (--positions T=w,T=w,...)
MASSIVE_API_KEY exported
Stocks Basic minimum

Optional:

--benchmark (default SPY)
--lookback-days (default 504, 2 years)
--ic-window (default 63, one quarter)
--n-trials-dsr (default 1): multiple-testing correction for Deflated Sharpe. Pass N if this book was picked from N candidates during search.

What you get back

Layer 1: JSON. Full tearsheet (CAGR, Sharpe, DSR, Sortino, Calmar, max DD, ulcer, tail ratio, profit factor, hit rate, beta, alpha, tracking error) plus rolling IC mean and std vs benchmark. Top-level verdict.

Layer 2: rendered note. Header verdict + return stats block + vs-benchmark block + Take.

How it works

Pull daily bars for each position and the benchmark.
Align to common dates.
Compute daily portfolio returns (weighted sum of position returns, renormalized to abs-weights = 1).
Run quant_garage.performance.tearsheet with benchmark kwarg populated so beta / alpha / tracking error come through.
Compute rolling ic_window-day Pearson IC of portfolio vs benchmark returns for a time-varying correlation lens.
Emit verdict:
- real_alpha: DSR significant at 5% AND alpha > 2% annualized
- possibly_alpha: alpha > 0 AND Sharpe > 0.5 but DSR not sig
- essentially_beta: beta > 0.8 AND |alpha| < 2%
- underperforming: annualized return < 0
- no_edge_evident: everything else

Foundations used

quant_garage.performance.tearsheet
quant_garage.backtest.rolling_ic_series
massive-api-patterns

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name

vs-benchmark-audit

description

vs-benchmark-audit

Answers "is this book actually alpha, honestly?" — with a verdict that separates real alpha from beta from noise.

When to invoke

Post-quarter review: did my strategy add anything above beta?
Investment committee prep on a candidate manager or strategy
Auditing a historical backtest with proper DSR correction
The user says "vs benchmark", "alpha vs beta", "is this real"

What you need

Positions (--positions T=w,T=w,...)
MASSIVE_API_KEY exported
Stocks Basic minimum

Optional:

--benchmark (default SPY)
--lookback-days (default 504, 2 years)
--ic-window (default 63, one quarter)
--n-trials-dsr (default 1): multiple-testing correction for Deflated Sharpe. Pass N if this book was picked from N candidates during search.

What you get back

Layer 2: rendered note. Header verdict + return stats block + vs-benchmark block + Take.

How it works

Pull daily bars for each position and the benchmark.
Align to common dates.
Compute daily portfolio returns (weighted sum of position returns, renormalized to abs-weights = 1).
Run quant_garage.performance.tearsheet with benchmark kwarg populated so beta / alpha / tracking error come through.
Compute rolling ic_window-day Pearson IC of portfolio vs benchmark returns for a time-varying correlation lens.
Emit verdict:
- real_alpha: DSR significant at 5% AND alpha > 2% annualized
- possibly_alpha: alpha > 0 AND Sharpe > 0.5 but DSR not sig
- essentially_beta: beta > 0.8 AND |alpha| < 2%
- underperforming: annualized return < 0
- no_edge_evident: everything else

Foundations used

quant_garage.performance.tearsheet
quant_garage.backtest.rolling_ic_series
massive-api-patterns