vs-benchmark-audit

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更新时间2026年7月12日 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.

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来源

rgourley

rgourley/quant-garage

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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