Sentinel Research
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Experiments
Five experiments, run in order. Each script is 0N_<name>.py. Scripts that
don't exist yet are listed here as a spec so the CI workflow and the paper's
Section 6 stay in sync with what's actually implemented.
| # | Script | Purpose | Status |
|---|---|---|---|
| 1 | 01_generate_gbm.py |
Generate pure-noise GBM price series (fixed seed, documented params) | pending |
| 2 | 02_baseline_replication.py |
Run K12 golden hyperparameters on real BTCUSDT 1m, buggy backtester → expect Sharpe ≈ 14.49 | pending — needs audit/input/code |
| 3 | 03_honest_replication.py |
Same hyperparameters/data, time_machine.py engine → expect Sharpe ≈ -0.25 |
pending — needs audit/input/code |
| 4 | 04_noise_control.py |
Run both engines across ≥30 independent GBM seeds, compare Sharpe distributions | pending |
| 5 | 05_noise_harness.py |
CI-gating version of experiment 4: fails the build if mean Sharpe on noise falls outside a pre-registered null band | pending |
Reproducibility rules
- Every script must take
--seedand print it in its output. - Every output JSON must include: seed, kernel version/hash, library versions (numpy/pandas), and a UTC timestamp.
- No script reads from
audit/input/directly in a way that would couple the public reproduction path to the forensic copy —audit/input/is for our own verification, not for the published reproduction instructions.
Environment
Pin dependencies in requirements.txt (to be added alongside the first
script). CI installs from that file — see .github/workflows/noise-harness.yml.