lookahead-bias-paper/experiments/README.md

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 --seed and 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.