Mixed Moving Averages – Test Results

In this round of testing we will be looking at a mix of different smoothing methods and averages:  The Moving Linear Regression or Time Series Forecast (TSF) and The Linear Regression Indicator (LRI) which aren’t actually moving averages but can be used in the same way.  Plus Wilder’s Smoothing AKA Smoothed MA (WS-MA) and the Triangular Simple MA (TriS-MA).  The aim is to identify if any of these indicators are worth using as a trading tool.

We tested each indicator going Long and Short, using Daily and Weekly data, taking End Of Day (EOD) and End Of Week (EOW) signals with smoothing periods varying from from 5 – 300 days or 60 weeks.~ These tests were carried out over a total of 300 years of data across 16 different global indexes (details here).

.

Annualized Return Mixed Moving Averages

.

Above you can see the annualized return statistics for each indicator.  The first thing that you will notice is that the LRI and TSF produce very similar results and neither of them are very good.  So for providing buy signals in this fashion the Time Series Forecast and The Linear Regression Indicator are knocked out cold in the first round.

The returns generated by the TriS-MA are reasonable but they are not good enough to out perform the EMA’s results so the Triangular Simple Moving Average is also knocked out of contention.  (Note – It didn’t dawn on us that the TriS-MA is almost identical to the Triangular Weighted Moving Average until after we had already tested it).

Wilder’s Smoothing produced some good returns when the smoothing period was less than 45 days but the performance dropped gradually to almost 7% as the length was extended.  The EMA exhibited similar behavior but bottomed out around 8% so while Wilder’s Smoothing is effective in this application, the Exponential Moving Average is still King.
.

Best Average of the Group – Long

.

We performed a total of 948 tests in this round; half of them on the long side and half on the short.  Rather than simply selecting the indicator with the greatest returns over the test period we identified the best for going long using the following criteria:

  • Annualized Return > 9%
  • Average Trade Duration > 29 Days
  • Annualized Return During Exposure > 15%
  • Annualized Return on Nikkei 225 > 3%

14/357 Averages made the final cut (see spreadsheet) but we selected the 30 Day Wilder’s Smoothing with End of Week Signals as the ultimate winner:
.

30 Day WS-MA, EWO Long.

Above you can see how the 30 Day WS-MA, EOW Long performed during the test period compared to the 75 Day EMA, EOW Long which was selected as the most effective Exponential Moving Average in a previous test.  The WS-MA with this particular smoothing period produced almost identical results to the EMA but didn’t offer any benefits.

Upon further testing we found that despite very different calculation the WS-MA and the EMA are actually the same indicator.  Simply double the WS-MA period and subtract one to find the equivalent EMA.  For instance a 38 period WS-MA is identical to a 75 period EMA.

.

  • ~ An entry signal to go long (or exit signal to cover a short) for each average tested was generated with a close above that average and an exit signal (or entry signal to go short) was generated on each close below that moving average. No interest was earned while in cash and no allowance has been made for transaction costs or slippage. Trades were tested using End Of Day (EOD) and End Of Week (EOW) signals for Daily data and EOW signals only for Weekly data. Eg. Daily data with an EOW signal would require the Week to finish above a Daily Moving Average to open a long or close a short and vice versa.
    .
  • – The average annualized return of the 16 markets during the testing period was 6.32%. The data used for these tests is included in the results spreadsheet and more details about our methodology can be found here.
  • Linear Regression Indicator (LRI) & Time Series Forecast (TSF)

    Linear Regression is a statistical tool used to predict future values from past values.  By using the least squares method, a straight line can be plotted that minimizes the distance between the resulting line and the data set in order to reveal a trend.

    The Linear Regression Indicator (LRI) plots the end value of a Linear Regression Line at each data point.  A variation on the same idea is the Time Series forecast (TSF) which is found by adding the Linear Regression Slope to the Linear Regression Line.  The TSF basically projects the LRI forward one period.  The TSF is also sometimes referred to as a Moving Linear Regression or Regression Oscillator.

    By calculating these two indicators on a moving basis the result looks similar to that of a moving average and can be used in the same way.

    .

    Calculating a Linear Regression Line

    .

    Linear Regression Line = a + bx

    Where:

    a = (Σy – bΣx) / n

    b = (nΣ(xy) – (Σx) (Σy)) / (nΣx² – (Σx)²)

    b = Linear Regression Slope.

    x = The current time period.

    y = The data series (Usually the close price).

    n = Number of periods.

    .

    Linear Regression Indicator & Time Series Forecast Excel File

    .

    Calculating these indicators by hand is a pain in the ass so we have build an Excel spreadsheet containing both the Linear Regression Indicator and Time Series forecast that you can download for free.  Find it at the following link near the bottom of the page under Downloads – Technical Indicators: Linear Regression Indicator (LRI) & Time Series Forecast (TSF).  Please let us know if you find it useful.

    .

    Linear Regression Indicator, Time Series Forecast and a Simple Moving Average

    .

    Linear Regression Indicator, Time Series Forecast and SMA.

    Test Results

    .

    We tested the Linear Regression Indicator and Time Series forecast through 300 years of data across 16 global markets to reveal which is the best and if either of them are worth using as trading tool for data smoothing – see the results.

    .

    .

    Wilder’s Smoothing was developed buy J. Welles Wilder, Jr. and he used it as a component in several of his other indicators including the RSI which is one of the most popular technical indicators of all time.