Zack Zarr 5 Posted July 6, 2018 A Deep Dive into Commitment of Traders Data Analysis- Part 1 It has been while since the last post because I have been putting some of my data analysis techniques together in a format presentable here. As we saw in the previous post, the COT report is the ONLY source of data directly coming from the institutional traders. Beside the charts, COT is the major source of information I use on a weekly basis. Remember, news and hyped prices are only opportunities for the big players to fill their orders and nothing more. It is very rare that a news release changes the direction of institutions’ mindset. Rare, but not impossible. In this game of probability, I always go with the high probability trades not a single rare occasion. How do I organize the data? Short answer, in Excel. At the end of each week on Friday afternoon, I compile the data released on CFTC website in my excel template because it is the easiest way for me to see the long term directions and decision making points in time. Below is snapshot of what I started with about 6-7 years ago. In short, I simply copied and pasted the data from the CFTC website for each currency and performed some basic analysis on them. In particular, I was tracking what the non-commercial traders were doing each week. One of the factors I was interested in was the percentage of short and long positions, the exposure %. Its simple calculation is as follows: Long% = Long / (Long+Short) x 100 Short% = 100 – Long% I was looking for extreme points where these traders had the maximum or minimum exposure for over a period of time like the past year, or 2 years. I will discuss more about what time period to look at in later posts. Later on, I found easier ways to display my data using built-in excel features. I had to take such steps because looking at multiple data series for different pairs and two different formats (legacy vs. TFF format) was tiring. The current format I have on my excel is not that different from the old one except there are more coloring based on the historical values of the positions and extreme points in position sizing. Figure below is the most recent format that I have been using for few years now. I have not implemented more sophisticated excel features because I simply don’t need to. Besides, I have put my efforts on some time series coding on the charts directly. That will come in the coming posts. It looks nicer, in my opinion. Now, instead of looking for extreme exposure and position data, the color coding allows me to see them much faster. The legend shows how I applied my color coding. The smallest number in a range gets the green and the highest values gets the red. Numbers in between the two take a gradient color with white being right at the middle. Being too green or too red is extreme! Net positions! The net position number is the one you see in most websites that report the COT data in a graphical right below their charts. See this one for example https://www.barchart.com/futures/commitment-of-traders/technical-charts/E6*0 https://cotbase.com/ My personal experience with the net positions has not been satisfactory. For some reason, I want to see the actual long and short contracts rather than the net. I can get a better vision of what the traders did over the week using their actual long and short position changes. For that reason, you see me often not paying too much attention to the net positions. I thought I should clarify this here. Example of Analysis Figure below shows a snapshot of the EURUSD chart and the tabulated COT data during the year 2015. I have highlighted few datapoints on the chart and their corresponding number of short positions held by the institutional traders. Just tracking the short positions, we can see that during each swing low, the number of shorts has consistently decreased. The shading of the absolute short positions and the exposure % suggests that these traders where at extremely high short exposure prior to March 17th. Over the next few months, they have been taking profit off their short positions which is why the number was decreasing. Seeing this, looking for a long trade is not a bad idea. And as you can see, after the Jul 21st, the previous high was broken. The long trade after the May 26th or Jul 21st data would have been a really good trade. Another insight: since the Dec 2014, they have been adding to their shorts as the price was going down. Now, they are not adding shorts during each period that the price goes down. The is a change in behavior. And a SIGNAL. Please note that this is only an example of using one parameter to see into the mindset of the big players. The more we analyze the numbers including the % exposures and long positions, the better we understand their trades. Eventually, we want to trade with them. The data is delayed (and you hear everyone saying that about the COT reports), but these traders are also SLOW! It took them 5 months to get rid of 72,000 short contracts each worth EUR 125,000. How to survive the turmoil The process of decision making after you figure out some pattern in the data is still complex. Here, I provide some key notes that helps you get better entry points and also test out your analysis in real time. These are my experience over the years and of course, every situation demands its own decision making steps which depends on the market dynamics at the time. Once you see a pattern in the data, benefiting from it is a matter of how you place your orders. There will be ups and downs before the giants make their final decisions so:ALWAYS split your orders into smaller portions.Test out the idea with smaller orders and try to think as If you are working with the big players.Use the time of news to your advantage.There will be times when all your orders are in negative, but you need to learn to trust the data. You will build a sense of how to spread out your orders to get the best price for yourself.Mark up your supply and demand zones. Make sure you do that using at least three timeframes.These big players report their positions on weekly basis so allow yourself at least a week to test the idea and observe what they have done in the past week.Also remember, you are only seeing the data up until Tuesday so what they did in the three days after that day is not in your report. But you can always go back to the charts and observe the price action. Depending on the nature of each institutional trader’s behavior, you will learn if they will be adding shorts on the way up or down. Similar for the long positions. Overall, this is a quick review of your journey into the realm of data analysis and why you need to understand the positioning of the big players in the market. As I have been using these data series for a long time, I feel my analysis will be incomplete without them. More of this sort of data analysis will come. If you need further information or want to clarify something, please do not hesitate to drop me a message at [email protected] Fore access to more advanced materials and Forex Topics, please sign up below the Forex Trading page: https://www.bearbulltraders.com/forex-trading/ Zack Share this post Link to post Share on other sites
