Regime Change Backtest Example

Regime Change is used in finance to describe when a condition changes.   IF [condition1] is met,  THEN invest in [X]...   ELSE invest in [Y].   ETFs allow us to easily test conditions which are defined not by some calculation you've created to simulate an index,  these are publicly traded securities with real money invested in them.  There is no ambiguity as to the rules when you use real-world securities as is so often the case with non-financial regime tests.  

Here is a simple example to get the hang of it, is the NASDAQ-100 going up?  If it is, buy it.  If it isn't, invest in a different type of ETF.   In this example, the different type of ETF is defined by the QUALITY FACTOR.   Quality stocks are those with strong balance sheets, lower earnings variability & higher Return On Equity -- as ranked by indexing firm MSCI.   QUAL actually owned real stocks on each day with real money, we aren't subjectively now determining what should be classified as quality and what shouldn't.

What does the performance report look like for this idea?  See below for summary version of an ETFreplay.com backtest report  (statistical analysis excluded in image below).

Then try other ideas.   All of your ideas don't have to work for you to be very successful at this.   Indeed, this strategy has underfperformed its benchmark 46% of the time in last 5 years (as measured by relative performance in each calendar month).  Yet the outperformance over time has been good.

 

Uptrending Ratio Indicates Relative Strength ETF - Backtests QUANTIFY It

An uptrend is a series of higher highs and higher lows.   Using a ratio between 2 securities shows which is relatively stronger.   A Relative Strength analysis can quantify which security within a list of MORE THAN 2 securities is strongest.  

So let's look at one current situation.    Emerging markets have shown good relative strength on shorter-term basis.  If this continues then a higher low and higher highs situation could develop (vs SP 500).   That said, SPY has continued to be strong -- both Emerging markets AND US Stocks have been strong this year.   It actually hasn't mattered which you've owned --- so even if you were wrong on thinking a ratio would go up/down, you still made good money either way.    This won't always be the case though.

 

 

Channel Backtest Example: China A-Shares ETF ASHR

Channels are a good, simple supplement that offer an ABSOLUTE look and can be used in conjunction with other RELATIVE studies. Wider channels give your trade room to work. Tighter channels will cause some whipsaw losses. If you are bullish on an ETF based on a range of factors, then running a skewed channel might be a good idea --- ie, run the exit (Sell channel) at 0% but a buy at just 60%... This allows you to get in quickly while still offering room for the investment to work. This study uses a simple 67% / 33% buy/sell trigger with a ~6 month lookback (26-weeks means you will trade usually on a Friday -- if holiday then Thursday). Entries and exits only occur on the close of the last day of the week (Fridays) allowing for easy monitoring. Note that this look has trades that have lasted a while. This is because the sell rules will allow a fair bit of movement before exiting.

Finally, because a channel uses a percentage,  it may be easier to see the trend in the ETF than othewise.   An uptrend is defined by higher lows and higher highs.    A downtrend is defined by lower highs and lower lows.   The channel is a another tool to have to see and understand what is happening in the market.

 

 

 

Introducing Dashboards: A Way To Help Organize Workflow In Research & ETF Portfolio Backtesting

We have added Dashboards as a new feature to the website so that subscribers can easily view multiple models and/or markets in one place.

Dashboards can be used in numerous different ways, including:

  • to view market activity from various angles with a mixture of several different models
  • to look at one model applied to several different ETFs / markets
  • to monitor different variations of a single model

etc etc.

Most importantly, you have the choice of how to set up your own personal dashboard(s).

To create a dashboard:

  1. From 'My Account' in the top menu, choose 'My Dashboards'
  2. Click 'Add Dashboard' button in the top right corner, enter a name and click save
  3. A dashboard with 6 empty windows will now appear
  4. Click the 'Add Item' button in top right corner of one of the empty windows
  5. Choose between:
    1. 'Screener'
    2. 'Ranks'
    3. 'Ratio Chart'
    4. 'MA Chart'
    5. 'MA List'
    6. 'Channel Chart'
  6. Enter the rest of the required details, click 'Save' and the desired chart or table will appear in the window.
  7. Repeat steps 4, 5 and 6 for the other empty windows, up to a total of 6 per dashboard
  8. Items can be edited / removed by clicking 'Edit Item' in the top right corner of the required window
  9. The 'through' date can be changed via the calendar control in the top left corner

The video below demonstrates one way to use ETFreplay Dashboards:

 

to expand video on screen, click the 'expanding arrows' icon in the bottom right corner of the video screen

 

Comparing 2 Ultra-Simple ETF Backtests Side By Side

 The example below is pretty self-explanatory but in a nutshell it compares 2 strategies set side by side in detached browser windows. 

Strategy A on left uses 2 pieces:   1.  50% choose 1 of 3 ETFs using 11-month total return  2.  other 50% using 6-month returns on the same portfolio of ETFs, also choose 1...

Strategy B on right using 1 strategy:  using ONLY 11-month returns.

Rather than only highlight just the overall total return of each,  of high importance is looking at the year by year (Calendar) returns vs a benchmark.   The 100% 11-month strategy has seen years of large outperformance and underperformance.   The blended strategy would have been much easier to stick by and actually achieve the end result - in addition it added return over the period.    We know from many research papers that 3 - 12 month relative strength all have some level of validity long-term.   No matter what the very long-term backtest looks like for these 2 strategies, we cannot know for sure which one is going to do better over the next 10 years.   But we can glean information by studying different types of backtests and help make a judgment about what is happening now.    Indeed, backtests primary function is to help guide you to understand what is happening in the most recent (current) period.