Return of the Stats – Follow up

Last week I posted an article called Playing the Odds – Return of the Stats, which was about the various techniques I was using to improve my use of stats within my trading.

One of the areas, I have worked on, is looking at the various statistics and when they worked / completed and then looking at the how far the maximum adverse exclusion (MAE) was for each instance.  I then compiled these into a sample, where I could then work out the most common (mode) and the Standard Deviation of the MAE. I then used this to help me identify potential areas of entry and defined my risk and reward.

I use RT Investor to calculate these stats and the various MAE results. David asked me to post a one of my charts with the text definition, as he was having struggling to write the code.

I mentioned in the previous article, that I got the idea for this from a webinar that Tom Dante and Hugh did called Playing the Odds.

Whilst I initially said I would post the chart definition, I have realised that each of my charts definitions use the code from one of Hugh’s charts from the webinar. Therefore I do not feel comfortable posting my definitions, as this is a paid webinar and the original code is not mine to give away.

The chart in question is Breakout Failure Part 2

There are a couple of solutions to this

1) Ask Hugh to post the definition for this chart.

2) Buy the Play the Odds webinar. It is 14.99 GBP, for that you will get the webinar and 15 templates that are compatible with MD or RT Investor. Of course if you only want the above chart, then it might sound expensive, but you could look at this way. I had put at least 5 hours in, trying to implement this, before I reverse engineered Hughs code. The question I would ask myself would this amount of money be worth the time saved.

To be totally clear. If you buy this webinar, I receive no monies from Tom or Hugh. If you want to go directly to the site then use Google search and the keywords Tom Dante Playing the odds, it is the 4th entry on the 1st page.

Hugh is much better than me at writing the code, and I would pay 14.99 for a webinar from him, on how to do these sort of charts covering various stats, as I sure that I would learn from that experience, as I have from watching his previous webinars.

But the choice is yours. If anyone has questions on the additions I made to Hugh’s original chart then I will do my best to answer them.

Playing the Odds – The Return of the Stats

Playing the Odds – The Return of the Stats

I had an interesting conversation (short but interesting) with James from about stats.

I have / had a stat that says that if price opens in value then there is an 83% chance of it testing yesterday’s vpoc. He asked whether I had considered whether that stat is more influenced by the distance from vpoc when it opens rather than fact it opens in value.

This has been something I had noticed before, that distance / size of various could be an influencing factor in the probabilities. For example, the 83% also includes all the times it opens just a few tick from the vpoc which certainly skews the results. Many of my stats have certain filters that take in account this sort of behaviour.

I have now applied that filter to majority of my stats that certainly alters the results. For example the open in value and retest of the previous days vpoc drops to 60% , if the open is more than 20 ticks away from the vpoc.

One of the problems with stats is that how do we use them practically to gain an edge. We could have a stat for fading a certain move but how do we find entry and how do we define our risk.

One of the techniques I have been trialling is an idea, I borrowed from @trader1906 from his and Tom Dante‘s webinar on market statistics. My review of that webinar is here.

After I find a high probability (70%+) stat, I then look at all the times this stat has completed (thanks to RT Investor this is pretty simple) and looked at maximum distance it went in the opposing (adverse) direction. I then worked out the Mode distance and the 1 Standard Deviation of this maximum adverse distance.

What this allows me to do, is that when a stat becomes valid but open, I can look at the chart for valid levels within the max adverse distance to take a possible trade with a target of completing the stat. I then have a way of defining my risk and knowing that if price trades beyond this standard deviation that whilst the stat still have a chance of completing, that the probability of this has dropped to where it is now not a high probability setup.

I have been using this technique on a couple of stats and these have now turned into my most frequent stat trades and my most profitable stat trades.

This is the chart of my filter and the MAE profile, for the days where it only breaks one side, how far did it go back inside the IBR when the extreme is outside the IBR.

Practical Trading Statistics

Practical Trading Statistics

It is coming to the end of the month; it is time for me to update my Trading Statistics.

I am also using this opportunity to simplify my use of trading statistics. Currently I have the majority of my stats on a cheat sheet for each market I trade.

Below is a part of my current cheat sheet for the Bund Market.

Practical Trading Statistics

The simplify process is going to be in three steps

1) Check each stat on how practical it is to use and whether I use this on a regular basis. On each stat that I do not use regular or is not practical, I will then look at whether I should to do more research to make this practical or should I drop it.

For example

The Open In Range and Breaks the previous day’s High, has a 79% chance of closing above previous day’s vpoc, 71% chance of that days open and a 68% chance of yesterday value high. Whilst this is helpful in determining a direction, it is not that practical to use in determining targets or places to get in and place stops.

I will do more research on this stat to use if I can make it more useful. I will do a study on where the low of the day is after breaking the high. This might make this particular stat more useful.

Any stat that is not practical I will drop.

2) I will drop any stat that is under 70% (or the inverse greater than 30%), I will focus only on the higher probability stats.

3) Chunking

In the Mental Game of Poker 2, there is an interesting section on Chunking

While the size of working memory is limited, “chunking” is a way to get more out of it.

Chunking is when the brain compresses a lot of information into one small piece, kind of like a zip file or a cue card. Chunking can be illustrated in the way football teams name their plays.

Each football team has a unique language for plays that helps them communicate large amounts of data very quickly, for example, “Right Double Gun Montana Screen Left.”

Just these few words allow the quarterback to instantly give detailed instructions to the other 10 offensive players. Pages could be written about each player’s assignment during the play, however, intensive practice and study allows them to recall all of it with just a few words.”

from “The Mental Game of Poker 2: Proven Strategies For Improving Poker Skill, Increasing Mental Endurance, and Playing In The Zone Consistently” by Jared Tendler

I am going to work on improving my cheat sheet so that I can remember the stats easier during the day.

I am going to remove the percentages, I do not need to know the exact percentages, as they can be summarised with words, HP = High Probability and MP = Medium Probability.

And I will look at summarizing the whole stat play in a few words.

One of the things I have done is to have a tool on my charts, where I can draw the bands for standard deviation and 2 standard deviation of the MAE on a breakout and retest of previous day high. This helps me to keep the stat in front of my mind and gives a visual reference to take into account when looking at context and potential trades.

Practical Trading Statistics

I will look at each stat to see if I can visually replicate that stat on the chart rather than just reply on the hypo or mini plan.

This is all part of my process of simplifying my trading without making it too simple.

We should do our own trading statistics.

We should do our own trading statistics.

When I hear other traders mention statistics on a market, I always do the research myself, before I use that trading statistic, as it gives me confidence in the stat and also a better understanding of what makes the stat tick, which then helps me implement into my live trading.

Trading statistics
Bund – How often is there an inside day after a day where volume and range is above 1 SD.

The trading statistic I have heard a couple of times over the past week or so, from a trading buddy, was how often is the previous day’s high or low is broken, or how uncommon an inside day is, and the effect that volatility has on this stat, i.e. that an inside day is less common (the high or low is more likely to be tested) after volatility.

My research shows on the markets I trade Bund and Stoxx, that an inside day happens about 15% of the time, which confirms the stats that I heard from my friend.

But when I added volatility, defined by range and volume, that within normally volatility (equal or under 1 Standard Deviation) then the inside day happens about 10% of the time, and when volatility is outside 1 SD, then an inside days happens about 20% of the time, so volatility increases the chances of an inside day, not decreases the chances.

So whilst there is still a low percentage chance of inside day whether there is volatility or not, that is not the point. We must confirm our statistics ourselves if we are going to use them as an edge within our trading.

Please see the Bund on Friday for an example of this.

How I am trying to stop being so easily manipulated by my own trading statistics

How I am trying to stop being so easily manipulated by my own trading statistics


What we are communicating are simple statistical issues, such as underlying risk, standard errors, and variability. But they are extremely difficult to communicate clearly, even to people with some training in statistics. So we spend a lot of time with patient groups, changing wording after wording, such that we end up with something that is understandable without being technical or misleading.


There are. We know, for example, that “relative risks” can be used to look impressive. Twice a small number is still a small number. We know that talking in whole numbers—so many people out of 100—is clearer than talking in percentages or decimals. We know if done right, visual representation can often do a better job of explaining numbers, especially to those with low numeracy. “As a statistician, the perception of numbers is new to me. I thought people would know that 3 out of 100 = 3% = 0.03.”


We’ve used this knowledge, worked with psychologists around the world, to build guidelines for how people can best communicate risk. But there are still things that we haven’t got a good answer to. For instance, we know that people think 30 out of 1,000 is bigger than 3 out of 100. We know that we make numbers look bigger by manipulating the denominator.


The bottom line is that humans are very bad at understanding probability. Everyone finds it difficult, even I do. We just have to get better at it. We need to learn to spot when we are being manipulated. Changing axes on a chart is one way, but there are many other subtle ways to do it.


Read the full article here…

As someone that is not that natural with numbers, I find this article very interesting.  I am constantly looking to improve my use and understanding of trading statistics, but I have also noticed that I suffer at times at not completely accepting what my own statistics are telling me.

To aid me in my understanding, I have over time been adding more graphical representation to trading statistics in my own journal.

The Edgewonk trading journal does an excellent job of doing this.

But what I am also doing, is changing the way the trading statistics are displayed. Typically my percentages were displayed in decimals i.e 0.72 is 72%. I have changed my journal so all these trading statistics are displayed in full percentages so that it is easy for me to digest these figures.

As I have mentioned before, that one of the areas I working is self belief in myself and my system. For example, if I take 3 losers in a row, the default state in the past has been to doubt my own ability or that my system is losing its edge.

To help combat that, in the trade entry part of my journal, I have a system health mini dashboard, which displays my win rate and exp for all my trades and also for the last 30 trades and looks at various factors as time between trades, errors, trade quality, average win and average loss over the last few trades.  This is to give me a heads up when I about to go on tilt and when I am not on tilt, it is there as added mental re enforcement that when I have a string of trades not working out, the the bigger picture is still healthy and these losing streaks are within my standard distribution of my system.

I have added recently an indicator to the Health dashboard which displays my minimum required win percentage to be profitable, taking in account my historical average win compared to my historical average loss (BM%), and then does the same on a rolling 30 trade average(BM.30%). This is then plotted on a simple bar chart against my current historical win rate and 30 trade rolling win rate.






These minimum win rates are not a target, but a mental crutch.  When the inevitable losing streak comes along, I find it natural for me to start doubting my process and system. But a check of my System Health Dashboard will remind me that this is within the normal distribution. Which allows me to re focus on context and process rather than worrying about the mechanics of my system.


Improving the use of statistics in my day trading plan

Improving the use of statistics in my day trading plan

A while ago I wrote an article on how I use statistics in my trading. Over time the list of stats I use has grown into a list of 32 items, whilst some can be grouped together, this still was a list of 24 stats to keep an eye and incorporate into my trading.

As I had noticed in a previously in Repeating the Best Practice, the way I calculated my stats has changed over time. I also had noticed that the list of stats had got more complex and I was having trouble including these into my trading plan.

Or to be more precise, I was focusing only on a few stats and not using the others as it was too time consuming and complex.

As part of my Best Practises Manual, I decided to simplify the whole process.

Step 1 – Schedule and Settings

I have written a schedule of when I update these stats and what settings I am using each time. This will introduce some consistency in when and way I calculate the stats.

Step 2 – Make it easy to do

Some of my stats where calculated in Excel and some using the RT Investor Charting Software.

My next step was to convert all my stats in excel to RT Investor. I did this on Thanksgiving whilst the market was quiet, and it took me a good 5 hours but in the future it will save me time and will make it easy to update my list.

Some of the stats charts I downloaded from RT Investor Homework section, I then re purposed these to give me the stats I wanted and some of them I wrote from scratch. I am no expert with the RT Investor RTL, but by examining the downloaded charts I managed to learn enough to write by own stats charts.

My point is that if you are familiar with doing stats on excel, then a bit of time spent looking at Chad charts should give enough information to make your own charts in RT Investor / Market Delta.

I then rename all my charts and remaining excel documents that are stat based with the prefix STATS, thus making these charts and documents easy to find.

Step 3 – Incorporating my stats into my day trading plan

Previously, I would look at the chart, see where price opened or where price is trading and then check my stats document and then write out the relevant stat into my day journal and then incorporate into my intraday plan.

As my list of stats increased, this document expanded and ended up at 3 pages long. The result was I would occasionally miss a relevant stat or on some days, where I would have a few stats in play and I would have trouble incorporating all this information into an intraday plan that dove tailed into my overall daily hypo.

My observation is that if a practice is too complex then the tendency is that either it does not get implemented or not implemented correctly every time. Something has to change if I am to bring consistency to all of my best practises.

Instead of a 3 page document listing all my stats, I reduced this to an one page cheat sheet with only the relevant information in shorthand and divided into sections split between open, high, lows, etc.

I went further and numbered the most important intraday stats, so that I can refer to them by number in my day journal and I will now note whether the stat is positive or negative for the bias.

An example of how I enter this into my day journal.

From this I have found it easier to work these stats into my areas of interest and overall plan.

This has been all updated into my Best Practises Manual, and will be the routine going forward.

How I use statistics in my trading

How I use statistics in my trading

Statistics have been part of my trading since I started, but at the beginning my understanding of probability was rough to stay the least and over time my extend I use them day to day has increased.


Whilst I tweet some of my statistics every day, I will not be giving a list of all the statistics I use in this post. This is not because statistics give me some secret edge in the market, they are certainly no holy grail, but they do take time to research, correlate and keep updated, and it must be said that the majority of the time invested into researching statistics will result in dead ends.

At the end of this post, I will post some links to various resources which will provide walk throughs on how to compile various statistics in excel and many ideas for statistics  that you might want to research in your own market.

Statistics can give an edge in the market, but it is not always an tradeable edge. For example, a 7 tick gap within yesterdays range has a 75% chance of closing before the end of the day. But the market can go offside all day and then close the gap a few minutes before then end of day. This has completed the statistic but I would argue that tieing up capital all day to earn 7 ticks is not the best use of emotional and trading capital.

Questions I ask myself

The first question I ask myself, does this statistic give me room for my trade to reach its first couple of scales, and can I use it  to shape my profit target for any runners.

Taking the example of the gap inside range, I would be looking for one of my step ups to allow me take a trade in the direction of filling the gap, if no setup then no trade and if the gap closes, so be it. If price has moved away from the gap and my initial targets drop short of the gap close, then any runners would target the gap close.

I will not take a trade that fades a statistic that is on the verge of being completed. For me this means that if price is within 10 ticks of a level which my statistics say has over a 60% chance of being closed, then I am not going to trade opposing this stat.

The next question comes when price reaches an area that statistically is unlikely to go further, unless there is a very good reason / context then I see a little point in taking a trade or putting targets past a price which statistics have show that over a large sample that it is unlikely to hit. If there is a good reason or my context model which takes into account the various stats says that there is more room then my trades tend to move to scalp scales.

Of course it is worth pointing out that what ever the statistic may say, that there is 50% that this time it could go further, and as I do not know the sequential order of which moves will fall within the curve of the statistic and which will not.

My trading revolves around taking the “easy” ones and trying not to do anything stupid in between these trades, so unless there is a valid reason I will use statistics to help me decide if there is room for the trade and to shape scales and targets.

Once price has reached an area that statistically is likely not to go any further, then depending on context , I will start stalking a trade to fade the move.

I will not take a trade fading a move just because it is past its rotation length, daily range, individual day range or day type range, but it will start a process of looking for a setup or level to get into a trade that will fade the move. Of course context will help decide the scales and targets of this trades.

For example, if price rotation has reached a distance without any pullbacks that my statistics have show that rotations of these lengths in the past, have a 90% chance of pulling back a certain length before continuing. Then I would be looking for as setup to scalp fade the move back towards the pullback end point  and would be looking for a setup to join the move at around or beyond the price of the statistical minimum end point of the pullback, taking into account any stats that could limit the continuation of the move.

The primary use of statistics for me is to provide context to my trades and tradeplan, they help decide whether a trade is worth taking, and when I decide to take a trade they can often boost the win rate and overall expectancy of these setups.

I am sure there are many different ways of incorporating statistics into a trading plan, this is just the way I use them. If other traders have any suggestions then please feel free to leave a comment.


How to draw a simple histogram in Excel


Alternative way of calculating rotations

Ideas and walk through s  for various stats