Proposed ETF Portfolio for Rotational System

I have to say that I’m very thankful that I asked for the collective wisdom of the blogosphere. The comments were very helpful and have absolutely solidified my thinking in how to approach this next ETF rotational system.

Okay, so the final step before we jump into this system is to decide the portfolio of ETFs on which to test.

My thoughts about this portfolio are simple. The ETFs should be liquid, cover the S&P500 sectors, contain some country specific ETFs, some commodities, and some inverse ETFs. I do not want leveraged ETFs and ideally we are looking for more than 5 years of trading.

I believe the list meets my goals, except the inverse ETFs are not quite as liquid as I hoped for and some of the ETFs have only been trading for a few years.

If there are any ETFs that you believe should be added or subtracted, please let me know in the comments section.

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I’m going to ask for some input from the blogosphere with this issue.

I am a firm believer that a simple system is more robust than a more complex system. This means that a simple system is less likely to be curve-fit and more likely to survive changing market regimes without a major breakdown.

What I’m am struggling with is touched on briefly in the previous post about a 4 Factor ETF System.

As I think more about factors, I think it may be important to make a distinction about the number of inputs required to derive the factor.

For example, if we use Rate of Change to rank ETFs, then we have one factor (Rank) with one input (n periods):

Rank=ROC(n periods).

What if we instead use two differing ROCs, and weight them? That would look like this:

Rank=ROC(n periods*weight)+ROC(n periods*weight)

While “Rank” is still the single factor, we now have multiple inputs required to make this factor.

What I’m pondering is this: Are inputs equal to factors?

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Thinking about what kind of ETF rotational system I would like to trade prompted me to ruminate a while upon which factors ( constituent or element that brings about certain effects or results) should be included. I want to keep things simple, which for me means not using more than four factors.

For an ETF system, we need to be able to assign some measure to the ETF in order to compare its performance relative to all of the other ETFs in the universe. I will call this factor 1.

Factor 1: This particular factor is often a measure of strength or weakness, perhaps Rate-of-Change, as we explored in the Fidelity Select system. The factor could be a moving average, or combination of moving averages, or the slope of a linear regression line.

For this next system, we will use RSI for factor 1.

Factor 2: Our next factor concerns how long the ETF will be held before it is rotated. Perhaps not rotating until after a specific amount of trading days have passed is an unnecessary waste of a factor. In that case, we could scan the ETF universe nightly to update the ranks for factor 1, and then rotate the next day into the ETFs that have moved into the top ranking.

As I believe that time exits have the tendency to be curve-fit, I want to make sure that we have a factor 2 that is robust, or else we will discard it and simply update the ETF rankings once a day.

Factor 3: The more I learn about trading and the markets, the more I realize the important role of volatility in affecting returns. For this reason, factor 3 will incorporate some measure of volatility. Will we use volatility to penalize the ranking mechanism, as we did in the Fidelity Select System, or will we seek to trade higher volatility ETFs? This will remain to be seen.

Factor 4: When I think about building a system that I could trade confidently for years at a time, factor 4 necessarily becomes some sort of drawdown protection. Factor 4 might only need to be something as simple as a moving average filter. Perhaps the moving average filter is replaced with inverse ETFs, and we instead diversify among a number of ETFs.

Other considerations, which I classify as filters rather than factors, concern how we decide which of the ETFs to exclude from our universe. Perhaps we only want ETFs that are liquid or perhaps we just want to include the S&P500 sectors. I have often wondered if these things that I consider filters are actually factors. This issue would make for an interesting discussion. Anyway, for now, I will not consider how we filter the universe to be a factor.

Next we will look at the basic ETF rotational system. I will outline the initial factors to be tested and will likely discuss my rationale for using them.

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Our focus for PDS has always been to provide actionable picks with an edge, wrapped in a site that is simple and easy to use.

To that end, we have added ATR(10) metrics for each stock picked.

A quick glance at these metrics will aid in accurate and efficient position-sizing and setting of stops. Of course I am biased, but I think it is a great addition as it eliminates a step between getting the signals and entering orders into the platform.

Of course, if you are not a subscriber and would like to take a quick peek at the site, the 1 day trial is free and requires no billing information.

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Things have been busy busy busy around these parts. Old skool iBCers remember when I ripped out the shower stall and installed tile, a couple of years ago. I did a post on it but I think all the pictures were deleted. Anyway, I’ve now replaced the tiled floor in the bathroom, and the trim, and repainted everything, over the last several weekends. The wife is happy…but I’m behind in my weekend research.

PDS has been pulling back like we would expect. The system metrics outperformed over the past month or so, and now it appears they are moving back towards the historical norm.

I can’t help but think that it will soon be time to short again, and so I will be dusting off a short-system or two over the next week or so for testing and tweaking. I would be thrilled to be able to pair PDS with a good short system.

I am going to continue with the rotational systems. Jeff over at Market Rewind has made a suggestion or two, and I have a new idea or two to play around with. The next rotational system will trade ETFs and is going to be buying strength.

I had to put The Slippage Project on the back burner, but I should have all of my trades loaded into the spreadsheet within a week or so, and then I’ll work on project implementation.

Finally, there is a top-secret super duper double classified new system that a reader presented to me to try out. Expect to see more on it soon. It will appeal to daytraders, and will likely be able to be traded with limit orders intraday.

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PDS is absolutely killing it.

I have included a .pdf of closed Power Dip trades, and they are nothing short of stellar, surpassing even my expectations.

power-dip-winship

The website looks much better than the .pdf, but don’t let that distract from the massive winship shown in the closed trade logs.

We have recently added a new feature to PDS, and that is a Daily Dip email, which summarizes all system activity for the day. The system gives a ranked list of stocks, tells you the day to buy, tells you the day to sell, and communicates all of this daily through two short emails. What could be more easy?

The trial is free and requires no billing information.

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Now that PDS has successfully launched, I want to direct some attention to a brand new project, one that has the potential to benefit a large number of traders. It is called The Slippage Project. This project will rely on crowdsourcing to quantify the average amount of slippage¹ one incurs when using market orders on the open. Before I get into the specifics of how this project will be implemented, some background information is necessary.

What Is the Opening Auction or Opening Cross?

When one places a market order to be executed at the next open (market-on-open or MOO), he or she is agreeing to participate in the opening auction or opening cross. In simple terms, during the opening auction, the stock exchange runs an algorithm (at least on the Nasdaq) and attempts to match buy orders with sell orders. If there is an imbalance such as more buy orders than sell orders, the stock will gap up as there is more demand than supply.

Link to NYSE Arca Equities Opening Auction

Link to How Nasdaq’s Opening and Closing Cross Works (Highly recommended reading).

When we agree to participate in the opening auction or cross, we accept that we will get the opening price. However, and this is very important, there can actually be different opening prices for each security!

The Official Exchange Open vs. The Consolidated Open

When a stock opens for trading at 9:30 a.m., the cross that takes place on the major exchange, e.g., the NYSE or Nasdaq, is recorded as the Official Open price.  While the Nasdaq is very quick at crossing trades, the NYSE is very slow, primarily because the Nasdaq is an electronic exchange while the NYSE is still suffering from the remnants of the specialist system. If a trade goes through an ECN before the opening cross is finished (this happens often on the NYSE, remember it is slow) the ECN trade is recorded as the consolidated open. For example, while the NYSE is busy counting on their fingers and toes, trying to match orders for ABC stock, an ECN may match a trade at 9:30:01 a.m. (making the consolidated open). ABC stock may not officially be opened by the NYSE until 9:31:00 a.m, and when it does open, the official open price is recorded.

So to recap, we have an official open price, and a consolidated open price. The problem is that on the NYSE, the two prices are usually different!

To make matters worse, most data providers, including Yahoo, Google, Tradestation, Interactive Brokers, etc. publish the consolidated open price, and NOT the official open. And here is the critical point: Most retail traders could never actually get the consolidated open price, only the official open price. Yet the consolidated open is what is commonly reported.

The Problem, and a Solution

The problem is that we are presented with an opening price that is unattainable.

When we backtest strategies that trade the open or the close ( more on trading the close later), we are assuming that we could have purchased a stock at a price that would not have been obtainable in the real world.

For strategy traders, if on average we are filled at a price higher than the consolidated open, it will mean that our strategy may not perform as well as it did during backtesting. Similarly, if we consistently get filled beneath the consolidated open, it will mean that our strategy may actually exceed the performance results derived from backtesting.

The solution is to record hundreds if not thousands of opening buys and sells, and calculate the differences between the official opens and the consolidated opens. After recording enough opening prices, we can determine a realistic figure to use during backtesting.

My friend BHH and I started recording  our opening trades, some time ago (credit must go to him for the idea). When we stopped logging trades, we were actually seeing a positive difference, meaning that on average we were getting filled at prices lower than the published opens.

Tito over at Hack the Market wrote about a similar project where he logged roughly 850 trades, and saw a price improvement of  .04% on the trades where he was not filled at the published open.

For us strategy traders that trade the open or the close, this information is invaluable. We may be seeing positive price improvements that will negate the cost of commissions, or not. The alternative is that we should be building higher costs into our systems to account for negative slippage.

Implementation

I do not want any exploding heads, so implementation of the project will be discussed in the next post. Stay tuned! This is a project that will benefit many of us retail traders, but in order for it to be successful, we will all have to participate!

¹ This is not slippage in the traditional sense of the term Rather, we are talking about the difference between the official and consolidated opening prices.

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