The False Strategy theorem tells us that the optimal outcome of an unknown number of historical simulations is right-unbounded — with enough trials, there is no Sharpe ratio sufficiently enough to reject the hypothesis that a strategy is false. Given the ease with which one can use a computer...

We demonstrate a computer program that designs a portfolio consisting of common securities, such as the constituents of the S&P 500 index, that achieves any desired profile via in-sample backtest optimization. Unfortunately, the program also shows that these portfolios typically perform...

We carry out several test cases to illustrate how the Probability of Backtest Overfitting (PBO) performs under different scenarios. We also assess the accuracy of PBO using two alternative approaches (Monte Carlo Methods and Extreme Value Theory).The paper "The Probability of Backtest...

Proofs to the propositions in "Stop-Outs Under Serial Correlation".The paper "Stop-Outs Under Serial Correlation and 'The Triple Penance Rule" to which these Appendices apply is available at the following URL: "http://ssrn.com/abstract=2201302" http://ssrn.com/abstract=2201302

In the field of mathematical finance, a “backtest” is the usage of historical market data to assess the performance of a proposed trading strategy. It is a relatively simple matter for a present-day computer system to explore thousands, millions or even billions of variations of a proposed...

Most firms and portfolio managers rely on backtests (or historical simulations of performance) to select investment strategies and allocate them capital. Standard statistical techniques designed to prevent regression over-fitting, such as hold-out, tend to be unreliable and inaccurate in the...

We introduce two online backtest overfitting tools: BODT simulates the overfitting of seasonal strategies (typical of technical analysis), and TMST simulates the overfitting of econometric strategies (typical of academic journals). We show that econometric methods lend themselves to extreme...