OHLCV

12. References

References··backtesting, bt-series, references, bibliography

  1. Almgren, R., & Chriss, N. (1999). Optimal execution of portfolio transactions. Journal of Risk, 3(2), 5–39.

  2. Bailey, D. H., & Lopez de Prado, M. (2014). The deflated Sharpe ratio: Correcting for selection bias, backtest overfitting, and non-normality. The Journal of Portfolio Management, 40(5), 94–107.

  3. Bailey, D. H., Borwein, J. M., Lopez de Prado, M., & Zhu, Q. J. (2017). The probability of backtest overfitting. Journal of Computational Finance, 20(4), 39–69.

  4. Bailey, D. H., Borwein, J. M., Lopez de Prado, M., & Zhu, Q. J. (2014). Pseudo-mathematics and financial charlatanism: The effects of backtest overfitting on out-of-sample performance. Notices of the American Mathematical Society, 61(5), 458–471.

  5. Concretum Group. (2026). Can you trust your intraday database? Concretum Group Substack. https://concretumgroup.substack.com/p/can-you-trust-your-intraday-database

  6. Cont, R., Stoikov, S., & Talreja, R. (2010). A stochastic model for order book dynamics. Operations Research, 58(3), 549–563.

  7. Garman, M. B., & Klass, M. J. (1980). On the estimation of security price volatilities from historical data. The Journal of Business, 53(1), 67–78.

  8. Harvey, C. R., Liu, Y., & Zhu, H. (2016). …and the cross-section of expected returns. The Review of Financial Studies, 29(1), 5–68.

  9. Hansen, P. R. (2005). A test for superior predictive ability. Journal of Business & Economic Statistics, 23(4), 365–380.

  10. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.

  11. Lopez de Prado, M. (2018). Advances in financial machine learning. John Wiley & Sons.

  12. Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. The Journal of Business, 53(1), 61–65.

  13. Quantreo. (2025). Stop trusting your backtests until… Quantreo Newsletter. https://www.newsletter.quantreo.com/p/stop-trusting-your-backtests-until

  14. White, H. (2000). A reality check for data snooping. Econometrica, 68(5), 1097–1126.

  15. Wiecki, T., Campbell, A., Lent, J., & Stauth, J. (2016). All that glitters is not gold: Comparing backtest and out-of-sample performance on a large cohort of trading algorithms. The Journal of Investing, 25(3), 69–80.