ml4t-backtest-overfitting skillA
ml4t-backtest-overfitting is agent-read markdown (skill) from ml4t/skills: Detect and prevent overfitting to historical data via multiple testing corrections and pre-registration. Use when evaluating strategy variants to ensure performance is not a data-mining artifact..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Backtest Overfitting
Testing many strategies on the same data guarantees finding one that looks profitable by chance. With 100 independent trials at p < 0.05, you expect five false positives.
## The Problem
Every parameter you tune, every feature you try, and every universe filter you adjust is an implicit trial. A researcher who reports a Sharpe ratio of 2.0 after exploring 200 configurations has not found alpha - they have found the luckiest draw from a noise distribution. Correcting for the number of trials, by haircut here and by Deflated Sharpe Ratio in `ml4t-deflated-sharpe`, is what separates the two. Without it, most published backtests are statistically meaningless.
## The Pattern
### WRONG
```python
# Tune until something looks good
best_sharpe = 0
for lookback in [5, 10, 21, 63, 126, 252]:
for top_k in [5, 10, 20, 50]:
result = backtest(lookback=lookback, top_k=top_k)
sharpe = result["sharpe"]
if sharpe > best_sharpe:
best_sharpe = sharpe
best_params = (lookback, top_k)
print(f"Best Sharpe: {best_sharpe:.2f}") # meaningless without correction
```
### CORRECT
```python
import numpy as np
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-backtest-overfitting@git:20260901.1323bf4mdr add ml4t/skills/ml4t-backtest-overfitting@sha256:be65e56bddcbd414Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_ejvcx4toailuvhde)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.1323bf4 latest | 2026-09-01 | 1323bf4 | 5,054 B | A | view · diff |
| git:20260901.f84f825 | 2026-09-01 | f84f825 | 5,015 B | A | view · diff |
| git:20260901.32123e7 | 2026-09-01 | 32123e7 | 4,995 B | A | view · diff |
| git:20260901.2c5ab85 | 2026-09-01 | 2c5ab85 | 4,942 B | A | view · diff |
| git:20260901.c415df0 | 2026-09-01 | c415df0 | 4,773 B | A | view |
Audit of the latest version
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Source
ml4t/skills · 19 stars · license Apache-2.0 · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ejvcx4toailuvhde GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-backtest-overfitting GET https://markdownregistry.com/api/v1/blob/be65e56bddcbd414ae8b9049a0ad5bf7eda87fdab9118fdc637ff8ba01d4372a
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