Practically combining trend-following and seasonality

Here is a simple, robust method to combine trend-following and seasonality to achieve high return with low exposure and drawdown.


I use the simple filter from Faber 2007: invest when price is above its 10 month simple moving average.


Using bi-annual seasonality from my post series, I require the average return of the upcoming month over the previous 30 years to be greater than a threshold.


I use the “small-value” Fama-French portfolio “value-weighted” from 1984 – 2014 (using 1954 – 1984 for the initial averaging).  This portfolio is not directly investible but funds such as Vanguard’s VBR closely approximate.


Using Amibroker for analysis, the profit distribution is positively skewed:



CAR 15%, Exposure 50%, Max. DD 9%

55 trades, average hold: 4 months, 80% winners

Sharpe 1.3, Profit Factor 16


TRADE LIST (partial: 1992 – 2014)




Note: the threshold is cumulative over 15 datapoints i.e. a threshold of 15 equates to 1% average return per month.


6 thoughts on “Practically combining trend-following and seasonality

    • The loop calculates the sum of 1 month returns, ROC(C,1), for these historical months: -23, -47, -71 … -359 (i.e. 1-24*n, a bi-annual or 24 month periodicity).

      So the sum of these 15 returns is ‘r’ and this must be greater than a threshold ‘T’ for entry.

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  2. In Amibroker settings, periodicity should be monthly right? Which is the minimum lenght of time series so that this code works? For me this code doesn’t produce trades…

    • Monthly periodicity, zero trade delays (e.g. enters on April 30), 60 years of daily data with test duration from 1984 to 2014. Let me know if you still have a problem, maybe I can post my settings later from home.

      Depending on your data series, you will need to adjust the threshold: perhaps it is too high to allow trades for your dataset? Try widening it.

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