Alright guys now you can take a look at how I personally analyses of seasonality. To be perfectly honest, this is much better for commodities which have an intrinsic cycle governed by nature say corn, soybeans or wheat, and for things that have a fundamental yearly business cycle to them, for example energy products. When we consider currencies especially EUR/USD which is a gathering of many different countries all with different resources, business cycles, and policies. For me its hard to conceive that a cycle would be stable, or at least grounded in something so fundamental as to be in the bed rock of the instrument.
However this is not a forum on any of the products that best fit this type of study, so i had to go with the best analogue. USD/CAD why because its a comdoll with some correlation, i note SOME, with crude oil. Which has cyclical properties. So as an example I thought it would be best to use this, as the cycle will probably be much clearer than if i used EUR/USD.
Lets take a pause for a second and I am the first to say there is a lot going on in this picture. Its probably overwhelming for some of you who don’t do this study or have never seen it before done this way.
Lets get into my procedure, I used data going back to 1993 - 2013 on the loonie. Using daily prices, I then calculated the average price of the month and the median price of each month, giving each month about 30 points of granularity, 120 if you count Open high low close. I don’t believe the closing price of the month as a single point is particularly representative, because a lot of price shocks can occur and resolve in that time. Then I analyses directional and volatility patterns, this is shown in the first chart on the upper left. The bar above or below the zero line shows direction, size of the bar is the % change, indication volatility. Basically an expanded study of what I had showed earlier, which is the same work behind it. I then created an average cumulative seasonal change, basically isolating those average movements to give us an idea of how they may build on themselves through out the year.
Its important to note the percentage of increasing months while looking ath the seasonal directional patterns. This is because the seasonal directional pattern may be caused by a large price shock, so if we can correlate that with the actual percentage and they line up we can take it as another step towards confirming the bias, as opposed to a straight average which can be skewed with a large outlier.
The bottom left chart shows the monthly medians, this chart gives us roughly where on average each month trades at, although I don’t put much weight on this one as a bias or where we should be trading. I am looking for things that are very much out of line, say take the month of April, its largely out of whack higher than most months. But <50% of the time April is an up month, and the seasonal directional pattern shows this. So I can be lead to believe there was a price shock somewhere in the past in april, which can be studied further.
IMHO the most important information comes from the middle right and bottom right. I created a linear model and then extracted the residuals. I then plotted the residuals over time to give a chart of the detrended price. This is highly important because i need to isolate the cyclical seasonal factor, I can not allow a sustained trend in the instrument to overwhelm that. So by using linear modeling I take the overall trend of the instrument and then remove it. As you can see these points oscillate around the zero line. Now I then reparse the residuals back into its respective time and dates then create an average of all the residuals from that month. This gives me the best representation of the cyclical oscillations of the instrument. It moves around the zero line, and in general follows the seasonal directional patter outline.