Getting Started

A guide for new subscribers

The starting point, whatever strategy you intend to pursue, is to determine the types of securities (ETFs, stocks etc.) you want to be involved with and, importantly, feel comfortable with. You will almost certainly have your own ideas, but if not, the following portfolios offer some very simple starting points:

• Sample Portfolio - automatically given to all new subscribers, it contains the following list of rotation choices:
  • EWC -- country fund with natural resource slant and lower volatility than emerging markets
  • SPY -- large cap U.S. stocks
  • GLD -- gold
  • IEF -- intermediate duration U.S. government bonds
  • SHY -- short-duration U.S. treasury
• iShares 60/40 Balanced Allocation ETF
• Permanent Portfolio
• Bogleheads Lazy portfolios

Aside from considering asset classes, regions, countries and industries etc., it's essential to understand the nature and risk characteristics of the ETFs. We have several tools to help with this, for example:

• Monthly Returns: enables you to very quickly see what sort of monthly return is to be expected for the ETFs in your portfolio and what constitutes a genuine outlier
• Down Day Stats: allows you to easily see how the ETFs in your portfolio generally perform when the broader market has a bad day
• Max Drawdown: see the max drawdown per calendar year for each of your chosen securities
• Volatility Chart

With an initial universe in place, you can then give thought to the strategy. If you haven't done so already, we'd encourage you to watch the following 'ETFreplay Overview' video:

Moving Averages and Channels provide a relatively simple introduction to backtesting; most people inherently seem to understand why these trend following methods make sense. The next level up is the Ratio MA, which takes moving averages a step beyond basic by showing the moving average of the ratio between two securities. This is a more intermediate level as now you are relating one dynamic thing to another dynamic thing, rather than just one security against its own history as with a standard moving average strategy.

The next level beyond that is Relative Strength. Relative strength is more powerful because it can involve more than two securities. It is harder to visualize relative strength because you are comparing a list of securities all at once. There is no chart or moving average to view because it’s just not that simple. You are instead using a 'model' of multiple factors (see the Screener) and can set that model to different weightings and time periods. With Relative Strength strategies, it is particularly important that you understand and get comfortable with:

1. how the ranking of the securities in your list works
2. how the choice of parameters impacts the model
3. the structure - regular scheduled rotations to the invested portfolio.

For newer subscribers, it's best to begin with the Portfolio RS backtest

The 3-Factor Relative Strength model is loosely based on the Sharpe Ratio, which measures return per unit of risk. However, while Sharpe effectively equally weights a time period for return and volatility, the RS model takes the Sharpe Ratio concept and decomposes it into three separate factors:

• 2 time frames for returns (ReturnA and ReturnB - higher and lower time frame)
• 1 time frame for volatility

Volatility is a measure of risk and is the annualized standard deviation of daily returns over the lookback period specified. Risk is uncertainty and the larger the range of possible outcomes, the higher the volatility will be and therefore the greater the risk. Consequently, unlike returns, it is ranked low to high. Therefore, the greater the weight you assign to volatility, the more volatile ETFs in your list are penalized.

When using the backtest, start with your defined universe and choose an initial set of parameter values that make sense for the strategy. i.e. very short lookbacks aren't suited to a strategy targeting longer-term trends and vice versa. Sticking to appropriate parameter values lessens the possibility of being misled by an isolated / lucky result.

Begin by running a backtest of sufficient length that includes a mix of environments; up, down and sideways markets. Analyze the results and look beyond the headline statistcs - examine the return and drawdown for each year vs your benchmark. Was it a wild ride? Could you have actually stuck with it (be realistic)? Does huge out-performance in only one or two years mask under-performance the rest of the time? If so, was that because the model does well only in a particular environment, (if so, can you live with that?) or, was it just lucky at certain times? Now run a backtest over the last few years to get another view. Has performance diminished recently, despite strong returns overall?

Change the parameter values slightly, run another backtest and compare the results. A robust model will be moderately sensitive to small differences in parameter value. i.e. performance will vary, but slightly different parameter values should not produce wildly different returns. By contrast, larger differences in parameter value should be expected to have a bigger impact on performance. note: adjacent monthly parameters will exhibit larger performance differences than adjacent daily lookbacks.

Once you have a solid foundation in place you can then move to the most advanced level, which is to incorporate multiple layers, such as:

• Core-Satellite
• Advanced Relative Strength
• Regime Relative Strength - see below 'Regime Switching' sidebar

These backtests allow you to see the results of mixing multiple strategies together. In the case of Core-Satellite, this means that you can pursue a blended approach, where the core forms a solid base (i.e. low volatility) with the focus on rebalancing and then more aggressive tactical satellites are layered on top. This means:

• If tactical strategies suffer due to adverse trading conditions, a conservative core portfolio that is given material weight will significantly dilute any drawdown (always plan ahead)
• Having an infrequently traded core portfolio substantially reduces trade activity
• In a momentum environment, the tactical strategies will enhance returns of the core portfolio

This example of the core-satellite framework shows how it works. That example isn't meant to be a comprehensive strategy, its purpose is simply to provide investors with a solid starting point from which to carry out their own research.

No matter the strategy, before using it to form an actual portfolio ask yourself; do I fully understand the process and am I happy with it? Have I planned ahead; do I have the necessary risk control measures in place? Do I truly believe in this strategy, or if I take some losses will I lose faith in it? Then run more tests, analyze, learn and repeat.

The following resources will also help you to get started:

• Video Examples
• Blog with posts organized into specific categories
• Frequently Asked Questions
• Help Notes: just click the icons

Finally, it is worth remembering the following:

1. The future is unknown; a range of possibilities exist (it won't be exactly like the past, so there's little point in trying to precisely optimize historic performance)
2. In a probabilistic environment, nothing works all the time (and therefore strategies that work for a given cycle in the short term may not be best in long run)
3. If all your investment ideas turn out to be bad, no model is going to perform well

Need assistance? Contact us with any specific questions.