Crude Oil Strategy Mining Study (Part 1)

Today I continue a blog mini-series focused on putting context around trading system development. I am trying to better understand what can be expected from strategies that look good in the developmental phases.

With each subsequent study, I am discovering nuances and better learning the software. My previous study is described here. In the current study, I have attempted to compile pieces from previous studies and avoid earlier mistakes.

As I learn, the exact methodology varies from study to study. I previously studied equities while I now move forward with crude oil. The following study is my first to include a dedicated stop-loss. What follows is also my first study featuring a highest high (lowest low) stop for long (short) trades. Subsequent studies may not include these. Regardless of the differences, my ultimate goal is to walk away with some big-picture conclusions based on recurring themes found inside the numbers.

Full details on the following study is included in Mining 8, but I will go into extensive detail here.

My process with the software was very repetitive:

• Enter correct settings (e.g. long/short, HHV/LLV stop, 2/4 rules, OOS at beginning/end), clear and select Random 1000 entry signals, and remove signals in Time category.
• Run continuous simulation x 5′.
• Sort by All: PNLDD and screenshot worst [34*] strategies.
• Eye results to make sure no duplicate strategies included (discard and replace).
• Transcribe 34* strategy numbers into spreadsheet.
• Sort by All: PNLDD and screenshot best [34*] strategies.
• Eye results to make sure no duplicate strategies included (discard and replace).
• Transcribe 34* strategy numbers into spreadsheet.
• Select “Re-run strategies with adjustments,” adjust backtest dates, and [4-rule strategies only] require all rules.
• Maximize Results window, right click x2,* and select “All.”
• Group together first six* Results windows then every four thereafter with first on top, second below top row of overall Results window (seen in background), and subsequent windows somewhere below.
• Once all 34 re-runs complete, select Results windows four (six for first group) at a time and sequentially drag on screen with Net PNL vertically aligned.
• With four (or six) tiled on screen,* drag Excel spreadsheet directly below fourth Results window and transcribe Net PNL, PNLDD, # trades, Avg Trade, and PF.
• With six tiled, transcribe stats for first four, drag spreadsheet just above Results windows 5-6, transcribe stats for these, and close out 5-6.
• [Drag spreadsheet below fourth Results window if six originally tiled and] Close out Results windows 1-4.
• Run Randomized OOS, noting in spreadsheet strategies that pass two consecutive runs (second paragraph here).
• Run Monte Carlo (MC) analysis, noting in spreadsheet strategies with resample average DD less than backtested DD (seventh paragraph here).
• Close all MC and Randomized OOS windows.
• Sort by All: PNLDD worst to best.
• Repeat Randomized OOS and MC analysis steps for worst strategies then close all associated windows.
• Repeat steps for re-running strategies over incubation and transcribe OOS2 (third bullet point) performance stats.
• Re-randomize entry signals, remove time-related signals, and enter or check to make sure correct settings still entered.
• Rinse and repeat.

I will continue next time.

* — Many of these steps are reflective of my particular screen size, resolution, etc.

Stepping Back to Walking Forward (Part 1)

Reflecting on recent studies, I’m starting to see a bridge between data mining and walk-forward (WF) optimization.

Let me begin by discussing the assumption that all trading systems break. I will not be able to verify this myself until I have developed a respectable sample size of viable strategies [I have zero thus far] with further data on strategy lifetimes. Nevertheless, many gurus talk about continuous system monitoring in case of breakage. Many traders mention it, too.

One explanation for the poor performance I have seen in recent studies (see third-to-last paragraph here) could be that the incubation period is too long. I’ve been testing good-looking strategies over a subsequent four-year period. What if the average lifetime of a profitable strategy is six months? Averaging over four years when real outperformance may be limited to six months could result in overall mediocrity.

Recall my discussion in this second- and third-to-last paragraph where I suggested the most important feature of strategy performance might be a maximal number of profitable runs over the complete backtesting period. This echoes WF with the exception of reoptimizing parameter values: here I want to stick with one constant strategy all the way through.

A shorter average strategy lifetime means validation or incubation should be tested over shorter time intervals. The WF process seems perfectly suited for this: test over relatively brief OOS periods, slide the IS + OS window forward by those brief increments, and concatenate OOS performance from each run to get a longer OOS equity curve. I like the following image to illustrate the concept:

I started to describe the data-mining approach to trading system development with regard to how it diverges from the WF process here. I feel I may now have come full circle albeit with the one slight modification.

As I struggle to rationalize the Part 7 results (first link from above), I can shorten the incubation period or leave it unchanged in subsequent studies.

Perhaps I am suggesting a new fitness function altogether: percentage of OOS runs profitable.

Backtracking a bit, I think much of this evolved because I have yet to see validation data for the different stress tests. Such data is not forthcoming from the product developer who says in a training video, “at the end of the day, we are still competitors and I have to keep some things to myself.” This is data I will have to generate on my own.

Mining for Trading Strategies (Part 7)

Although my next study aimed to correct the mistake I made with the previous [long] study, I accidentally mined for short strategies. This was illuminating—as described in these final three paragraphs—but did not address the comparison I really wanted to make (see Part 6, sixth paragraph).

Given the fortuitous discovery I made from that short study, though, I proceeded to repeat the long study by switching around IS and OOS periods. I trained strategies on 2007-2011 and incubated from 2015-2019. I tested only 4-rule strategies expecting to find worse performance due to an inability to effectively filter on regime [which may have taken place in the previous study]:

Results are mixed. Beginning outperforms on Net PNL and Avg. Trade. End outperforms on PNLDD and PF. None of these differences are statistically significant.

This does not seem to be consistent with my theory about mutually exclusive regimes. That would be one regime controlling 2007-2011, a different regime controlling 2011-2015, and just one ruling 2015-2019.

In the face of a persistent long bias, such regimes would not exist: long strategies would be profitable whereas short strategies would not. As a general statement, this is true. Looking more closely, however, it certainly is not. 2007-2009 included a huge bear (benefiting shorts) while 2009 to (pick any subsequent year) benefits long strategies.

And if it’s tough to figure out equity regimes, then good luck finding anything of a related sort when it comes to CL or GC where words like “trend” and “mean reversion” don’t even begin to matter (based on my testing thus far).

Where to go from here?

• I should repeat the last study with 2-rule strategies to make sure I don’t get any significant differences there either.
• I could require a regime filter for the 4-rule strategies just in case none/few have been implemented.
• I need to better understand the short strategies. It’s one thing to find strategies that pass IS and OOS. It seems to be quite another to find strategies that do well on a subsequent OOS2. Performing well in OOS2 is what ultimately matters.

I have one other troubling observation to mention. Although the long strategies in all three studies (including that from Part 5) incubated profitably on average, performance does not begin to approach what we might require from OOS in any viable trading strategy. I did see flashes of brilliance among the 4-rule strategies (6, 9, and 10 strategies for each of three long studies posted PNLDD over 2.0 with a few excursions to 6 and one over 14!). On average, though, 203 top long strategies over eight years averaged a PF ~1.21 and PNLDD ~0.8 over four years to follow of a consensus bull market.

And don’t forget that we don’t have context around the 1.21/0.8 without comparable numbers for long entries matched for trade duration (Part 6, sixth paragraph).

Forget all the pretty pictures (see second paragraph here); if this is an accurate representation of algorithmic trading then those who say you’re not going to get rich trading equities certainly weren’t kidding!

Stability

Continuing on with my year-long organization project, this is an unfinished draft from January 2019 on stability.

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The extent to which stability seems to be left out of trading system development is alarming.

I worry that lack of [a] stability [derivative] has the potential to disrupt the entire backtesting endeavor. “Past performance is no guarantee of future returns” may be a microcosm of everything I am to say below.

Trading system development often looks at a large sample size of trades and gives averages. One problem with looking at the average across a large time interval is that the local averages at different points within the whole interval may vary greatly. Suppose I find the average ATR is 15. That doesn’t tell me whether 80% of occurrences are at 15 or whether 2% of occurrences are at 15 with 49% each at 5 and 25. Huge difference!

If I use a VIX filter and say “bad things happened with VIX over 30” but this only happened 60 of 4000+ times and most were in 2008 (third paragraph here), then have I added a robust guideline or simply one that is curve fit to the past (i.e. based on something that is unlikely to repeat)? I think one benefit of walk-forward is that by training over the recent past, I won’t get locked into using values that may be historic extremes and cherry-picked over a long period of time. For example, the long-term average for VIX may be 16-18 but in 2017 we went the whole year without seeing 12 and for at least a couple years we only rarely got over that LT average.

Therefore, anytime I’m going to look at historic data and determine a critical value, I need to look at frequency of that value and the distribution of those instances. If I could eliminate the losses of Feb 2018 then it might make for meaningful portfolio performance. However, if Feb 2018 were the only time such an event occurred, then I’m entering into a behavior pattern whereby I insure myself against any unique past event ever seen. This is like mummifying a corpse by placing paper mache over every single square inch of flesh (see third paragraph here) to eliminate exposure. In the end, this is either the Holy Grail (reward without risk) or a flat position (no risk, no reward). I can’t expect to have everything covered.

Rolling averages are a means to assess stability. If I have 16 years of data then an average over the entire 16 years may not be very meaningful. Taking 3-year rolling averages, though, and then looking at what percentage of the 14 rolling periods generate a return in excess of X%, for example, is a better way to describe the whole. The distribution is also important: by percentage return and by date. I don’t want to see anything to suggest the system is broken (e.g. all winners in the first eight years and all losers in the last eight).

Stability also matters with regard to correlation. Correlation is used as support for particular strategies (e.g. intermarket) or creating multi-strategy portfolios. Correlations change, though. I think it would be useful to plot rolling correlations to better understand whether it makes any sense to report static correlations or whether they fluctuate enough to be meaningless.