Sentinel Research
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20 lines
1.1 KiB
TeX
20 lines
1.1 KiB
TeX
\section{The Noise Harness Methodology}
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\label{sec:noise-harness}
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% TODO content notes:
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% - Describe the GBM null-data generator: dS = mu*S*dt + sigma*S*dW, fixed
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% seed, parameters (mu, sigma, N steps, start price) documented in
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% experiments/README.md and reproduced in 01_generate_gbm.py.
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% - Key property to state explicitly: this series has zero exploitable
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% structure by construction — no autocorrelation edge, no regime, nothing
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% a real strategy could legitimately learn.
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% - Define the test: run the same kernel/backtester pipeline against N
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% independent GBM seeds. A backtester free of lookahead bias should
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% produce a Sharpe distribution centered at ~0 across seeds. A backtester
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% with a leak will produce a systematically positive Sharpe regardless of
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% seed, because the "edge" comes from the mechanics, not the data.
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% - State this as a pass/fail CI gate: mean Sharpe over >= 30 seeds must
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% fall within a pre-registered null band (e.g. -0.3 to 0.3); anything
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% outside that band fails the build. This is what
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% .github/workflows/noise-harness.yml is wired to enforce once
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% experiments/05_noise_harness.py exists.
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