Crude Oil Strategy Mining Study (Part 5)

Today I want to continue discussing results from my latest crude oil study.

Monte Carlo drawdown (MC DD) relative to actual DD (fifth-to-last paragraph here) does not seem to be useful. 813 out of 1633 strategies had a MC DD less than the actual DD. These strategies performed significantly worse over incubation than strategies with actual DD less than MC DD (PNLDD -0.11 vs. 0.24, Avg Trade -$59 vs -$15, and PF 0.93 vs. 1.02). While this is somewhat of a surprise, over 81% of these were from the worst group. Best strategies significantly outperformed worst.

Randomized OOS had a significant effect on performance in favor of strategies that pass. In this study, 280 strategies passed Randomized OOS while 1382 failed. Passing strategies averaged 0.13 PNLDD, -$22 Avg Trade, and 0.99 PF versus 0.06, -$40, and 0.97 for failing strategies, respectively.

With regard to worst strategies, none passed Randomized OOS and a vast majority met the MC DD criterion. Best strategies included only a few that passed Randomized OOS and a few that met the MC DD criterion.

Like any study, this study is not without its limitations and/or criticism.

I should have required one Randomized OOS to pass rather than two. On a few occasions, I saw strategies pass and then not pass (or vice versa) upon retest. This made me think the test lacked sensitivity. Because I did not study this over a large sample size, I don’t know how often it actually takes place. In case this makes anybody uncomfortable, one software suggestion would be to make number of Randomized OOS simulations a customizable setting. I would prefer 2,000 simulations to running the test twice in hopes of saving time, but 2,000 curves on the spaghetti graph might be too messy.

I should have programmed the spreadsheet to compute Avg Trade since I recorded Net PNL and number of trades. Automating this would save time and decrease the probability of transcription errors.

I will continue next time.