How Do You Trade During Different Times of Day?

How do you trade during different hours of the day, and how does your P/L differ as the day progresses? Do you know? Have you identified certain hours or parts of the day when you are generally profitable, and others when you are not? Finding out such information can be a very powerful discovery, and successfully modifying your approach based on what you find can have immediate results on your bottom line.

If I look back at one week of my trading during which I traded everyday from 8am to 3 pm, I'll have 5 days of data to look at for some pattern. Let's just assume that my Hour of Day P/L analysis of the past 5 trading days shows that in every one of the 5 days, my losses were greatest from 8-10am. Well, that's intriguing, but may not bear much significance, statistically speaking.

Generally in small data sets (sample sizes), even very large relations cannot be considered reliable (significant), whereas in relatively large samples, much smaller relations between variables will be significant. Point being, the more data we have to measure (or trading days, in this example), the more significant our findings will be.

I'd be very happy if I were able to find out that out of the last 2 months (40 trading days), on 34 of the 40 days, I've lost money from 8-9am. Of course I wouldn't be happy about consistently losing money during that time of day, but rather I'd be pleased to have discovered this. Why? Because I can now do one of two things, both of which will likely improve my P/L: I could stop trading during this time altogether, or I could look into this hour in more detail and compare it to other hours to see exactly what I am doing differently and what is causing the losses from 8-9am.

The first choice above has its limitations. For example, if I stop trading altogether from 8-9am who's to say 9-10am wouldn't now become the new losing hour? Maybe it's more an issue of momentum, rather than how my system is interacting with the market during that specific hour. I want to find out.



In this example I've created a bar graph showing average P/L per trade, per the hour of day, shown in the top graph for only losing days, and the bottom graph for only winning days. The charts show data from 54 trading days (33 winning days and 21 losing days).

Creating one graph for each type of day allows me to sort the data depending on the outcome of the day, and in turn makes the analysis more specific. I'm able to see what generally happens during winning days, and what generally happens during losing days.

-The first thing I notice here is that on my winning days I am getting a good start to the day and generally trading well in the first 3 hours. As the winning day continues, each hour I’m able to keep my losses smaller than they are in the corresponding time frames in the losing days.

-I see that losing days generally get off to a poor start and tend to get worse as the day progresses, with the exception of the 2:00 hour.

-The 2:00 pm hour has been a good hour for me consistently. I might look into this in more detail and look at different measures in this hour compared to all other hours grouped together so I can understand why I am generally profitable at this time of day.

-What’s interesting is that in looking at the bar charts, my P/L trend looks very similar in both my winning days and my losing days: I get progressively worse (in terms of P/L) as the afternoon approaches and continue to suffer losses, despite my averagely good performance during 2:00-3:00pm.

- The main difference seems to be that I start the day well in my winning days and generally keep my losses smaller throughout the day.

© Copyright 2007 David Adler
All rights reserved

All analysis generated with the TraderDNA Analyzer.

Winning Days vs. Losing Days

What do I do differently on my winning days and losing days?

Ask yourself that question and then try to answer it.

Why did you answer the way you did? Did you think of recent winning and losing days and remember what stood out during those days? Did you remember a conclusion you had reached in the past about your trading? Or maybe you looked back at detailed notes you had taken over the course of numerous trading days.

To accurately answer this question you need to take an extensive and objective look at winning days, and losing days separately. The easiest and quickest way to do this is to group together all of your trade data from every winning day, and then group together all of your trade data from every losing day.

The more data you've collected/saved, the more statistical significance your analysis will have. That said, two weeks of trading consists of 10 days with which you can run analysis, and is at the very low end in terms of the sufficient amount of data needed. 2 months of trade data (40 days) is a lot better, and 4 months (80 days) is even better.

We're working on some very interesting things right now at TraderDNA. We've recently added a feature that can group all winning days together, and all losing days together from a designated time period, so we can run analysis on the two groups of days. I'll show you the value of this...




In this view we're showing the average P/L per losing trades on losing days, losing trades on winning days, winning trades on losing days, and winning trades on winning days. Here I can easily see that my losers are bigger on my losing days than they are on my winning days. My winners are larger on my winning days than on my losing days. Ok, fine. This is what I'd expect.

Here's a different trader...



This guy looks the polar opposite. His losers are actually slightly larger on his winning days than they are on his losing days. Conversely, his winners are smaller on his winning days. The point of bringing up this second example is to show that different traders might have different causes for winning and losing days.

Now I'd like to look at the first trader above again in more detail. I want to know WHY his losers are larger on losing days than they are on winning days. It'd also be nice to know exactly why his winners are larger on winning days.

I want to know more about the difference in these two types of days. Average P/L per trade can only tell me so much.

So let's look at other measures than P/L. I am searching for significant differences in these values (measures) between both types of days (winning, losing)...




Average Negative Drift represents the average amount of heat(risk, downside) incurred during every open position. Average Positive Drift represents the average profit potential seen in every open position. Average Lost Opportunity Drift represents the difference between Avg. Positive Drift and where I covered my trade.

An example is useful here. Let's say I took a long position in the market at the price of 100. The market traded down to 96 and I am still in my position. Then the market starts to come back and trades up to 108. I cover my position at 106. My Negative Drift is 4 (100-96), my Positive Drift is 8 (108-100), and my Lost Opportunity Drift is 2 (108-106). I will discuss these measures in more detail in future posts but for now let's continue with this example.

This guy's trading looks eerily similar with respect to the amount of risk and potential profit he sees in his open positions, and with respect to his amount of Lost Opportunity Drift. This tells me a couple things. First off, it tells me his reasons for getting in and out of trades, or the parameters that dictate his decision when and where to trade, and how much downside to take before taking a loss, are nearly identical on both winning days and losing days. From this I can conclude that the amount of Neg. Drift, Pos. Drift, and Lost Opportunity are not determining factors in whether he has a winning or losing day. It also indicates that his parameters are static and his style of trading does not vary from day to day.

So let's look further. We're searching for significant differences in these measures over the two types of days.




Here I can see the average number of consecutive losing trades, the average number of contracts traded per trade, and the average number of contracts on scratched trades (avg. size of scratched trades), separated by winning days and losing days.

The chart shows that he traded slightly larger size on my losing days, but by an amount that is probably not significant in the context of things (middle column).

The last column is the most interesting to me because it shows that he's scratching bigger trades on his winning days. This suggests that this might have some impact on the overall outcome of the day. I could drill down and look at the scratched trades in more detail but I'd like to keep this example general and continue on.

The fact that he generally has more consecutive losing trades on losing days (first column) is nice to know, but I’m more interested in knowing why.




Average Time Since Last Win, and Average Time Since Last Loss represent the amount of time spent out of the market after a winner and loser, respectively, before putting on a new trade of any kind.

The first column indicates that on losing days, after his losing trades he enters a new trade much sooner than he does on his winning days.

He takes more time before he trades again after a winner during his winning days than he does on his losing days (indicated in the second column).

He spends slightly more time in his losing trades on losing days than he does on winning days. He spends longer in his winning trades on winning days.

Going forward, if I'm him I would want to be conscious of the fact that if I rush to back into the market after a winner it might contribute (at least to some extent) to the day turning out in a loss. I would also want to remember that on losing days, for whatever reason, I spend less time out of the market after a losing trade than I do on winning days, for whatever reason. Let's have a look at a couple more measures...



This chart shows the average number of times I add additional contracts to a losing trade (Negative Stackup) and the average number of times I add additional contracts to a winning trade (Positive Stackup), during winning days, and losing days.

The first column tells me he adds additional contracts to winning positions more often during losing days than he does during winning days. This suggests he's performing better when he doesn't add additional contracts to winning positions, or when he adds additional contracts to winners less frequently.

To me, the second column reveals the most important piece of information of all: In losing days, he adds to losing positions at a rate of nearly twice as often as he does in winning days.

The conclusions he comes to might not be a very big surprise to him, but more importantly, the data brought to surface facts about his trading that likely contributed to the outcome of the day. His ability to remain conscious of the information learned above and apply effective changes to his trading based on what past data has told him should increase his bottom line, statistically speaking.



© Copyright 2007 David Adler
All rights reserved

All analysis generated with the TraderDNA Analyzer.