Auditing Your Own Edge: Results From a Cross-Asset Backtesting Program

## Limitations

Backtests are upper bounds on live results, not predictions. The survivorship deflation is estimated, not yet measured point-in-time. Crypto costs are volatile and the sleeve's historical drawdown (-35%) is severe. Eight winning years in eight is partly a property of the era tested. The program treats all of this as standing risk, with monthly drift detection between live and modeled returns - and the honest summary of eight months of work is: one durable-looking edge, dozens of seductive dead ends, and a process that told them apart.

*Full implementation available on request.*

## Key definitions

**Momentum** - A strategy that buys assets with the strongest recent price performance and sells those with the weakest, based on the empirical tendency of price trends to persist over intermediate horizons.

**Survivorship bias** - Systematic overstatement of returns that occurs when a historical dataset includes only securities that survived to the present, excluding those that delisted or failed during the period.

**Sharpe ratio** - The average excess return of a strategy divided by its standard deviation, measuring risk-adjusted performance; higher values indicate better return per unit of volatility taken.

**Maximum drawdown** - The largest peak-to-trough decline in cumulative returns over a specified period, expressed as a percentage.

**Multiple-testing correction** - A statistical adjustment that raises the significance threshold for accepting results as real when many hypotheses are tested on the same dataset, reducing the probability of false discoveries due to chance.

**Chronological walk-forward split** - A backtesting discipline in which a dataset is divided into sequential in-sample and out-of-sample periods to simulate live deployment without lookahead bias.

**Holdout set** - A portion of historical data reserved and untouched during model development and validation, opened only once at the end to provide an unbiased performance estimate free from data-mining bias.

## References

- Benjamini, Y. & Hochberg, Y., "Controlling the false discovery rate: a practical and powerful approach to multiple testing," *Journal of the Royal Statistical Society*, 57(1), 1995. doi.org/10.1111/j.2517-6161.1995.tb02031.x

- CME Group, "Futures Specifications and Contract Details," CME Education. Https://www.cmegroup.com/education/

- De Moor, L., Sercu, P. & Vanpée, R., "Valuation of small cap stocks: lessons from Belgium," *Journal of Empirical Finance*, 19(5), 2012. doi.org/10.1016/j.jempfin.2012.06.003

- Federal Reserve Economic Data (FRED), "US Economic Data," https://fred.stlouisfed.org/

- Novy-Marx, R. & Velikov, M., "A taxonomy of anomalies and their trading costs," *Financial Analysts Journal*, 72(4), 2016. doi.org/10.2469/faj.v72.n4.4

- Pring, M. J., *Technical Analysis Explained*, McGraw-Hill (5th ed., 2014).

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*Educational research on historical data only - not investment advice, not a signal, and never a performance promise. Past results do not predict future performance. Drafting uses AI assistance; every citation is link-verified before publication and every paper is re-audited weekly against the library's editorial standard.*
*Last reviewed by the PropLedger research pipeline: 2026-08-26. Educational research on historical data; not financial advice.*