Commitment of Traders Data Analysis- Data Meets the Chart

[Zack Zarr 5]

Commitment of Traders Data Analysis- Data Meets the Chart

 

As I mentioned in the previous two posts here and here, looking at the big traders positions on each currency and commodity for the previous week is the only tool I add to my supply and demand trading methodology. Since the first time I started analyzing the COT data, several changes have been added to make my analysis more optimized. The last step was to take the analysis right in the charts because it saves me time going back and forth between the tables and the chart.

 

The programming capability of the MT4 platform was the most useful tool I could use to insert the COT data onto the charts. Although I was able to put most of my calculated parameters on the charts, the MT4 platform was not the best one that could save me analysis time. Fortunately the entire charting dynamics in the world of trading changed when the tradingeview.com web-based charting became available. The first time I used it at around early 2014, I immediately stopped using the MT4 for my charting. Later on, I started coding in pine script which allows me to implement my analysis on their charts. Pine script is the coding language used in tradeingview. Switching to tradingview definitely changed the quality of my data analysis. It became even more convenient as they added direct access to multiple databases of COT reports from their platform.

 

There are several formats of calculations I perform on the COT raw data to come up with the best visual results. Among them, there are three separate analysis windows I add underneath my chart nowadays. The first window contains the Long% and Short% exposure percentage. As I mentioned in my previous post, these two related parameters are calculated using the following formula:

 

Long% = Long / (Long+Short) x 100

 

Short% = 100 – Long%

 

The other two windows are also related but they calculate the relative long % and relative short %. In short, the relative long % is the percentage difference in the long positions compared to a desired historical maximum number of long positions. Let’s take a period of 2 years which is 104 weeks or about 720 days. The relative long % is calculated as:

 

RelLongExposure % = Long_current/(MaxLong_720Days-Long_current) x 100

 

Similarly, the relative short % is

 

RelShortExposure % = Short_current/(MaxShort_720Days-Short_current) x 100

 

The two parameters above are in fact a way of graphical representation for the color coding of my tabulated excel template (see previous posts here and here) combined with a differential factor. I found out that during the long term trends in a currency or commodity, the institutional traders take profit or add to their positions at around certain points relative to their past historical number of positions.

 

To avoid excessive text, let’s look at each parameter window and compare it with what I had in the excel template. Figure below is a snapshot of the daily USDCAD and the related COT data extracted from the legacy report. The tabulated excel data is also shown in this image.

 

 

The first window underneath the chart shows the exposure percentage numbers. These are the same as the Long% and Short% numbers calculated in my excel template. As I highlighted on the table, the latest Long and Short% for the Non-Commercials or the Large Speculators match the values shown on the top data window indicated by the arrows. The Commercials percentages are also indicated by the two black arrows. The Non-Reportable group is what I don’t display in my tables but I have it shown on the data window.

 

The two other data windows are what I refer to the relative long and short exposure percentages. I have found these calculated values are sometimes very useful in identifying turning points in the price. The second window below the chart shows the RelLongExposure% parameter. Here, I use the largest number of long positions held by each group of traders in the last 350 days as my reference point. As you can see, the LargeSpecs reduced their long positions down to 20% of their largest long positions within the past year and then started to add to their longs.

 

At the same time, the LargeSpecs have been at their largest short position (based on the bottom window showing the RelShortExposure%) prior to the latest low in the price. The next time the price hit a new low, their short positions were not as large as the previous low. What does this mean? It means that they are taking profit off their shorts and starting to add to their longs.

 

These are some of insights I can easily get form the graphical demonstration of the COT data which is otherwise more difficult to grasp from the tabulated data. As I mentioned, there are more parameters that I can put on the charts if I find them more useful at certain times.

 

I finish my post here to keep it brief. In the next post, I will demonstrate the graphical illustration of the TFF format. That report has also sometimes more insights which I use to my advantage.

 

If you want to learn more about the coding in pine script and how to develop your own analysis tools on charts, stay tuned because I am planning to develop a short course on that topic.

 

If you need further information or want to clarify something, please do not hesitate to drop me a message at [email protected]

 

Zack