Registered: Apr 2002
Posts: 700

This thread is about the process of developing systems/methods and the detail that surrounds them. I've been semi-retired for about three years now. I'm now 48 and the older I get, the less desire I seem to have doing trading related work. At this point I have about six looseleaf notebooks filled with material I've not found elsewhere. I think some of it would be useful to just about anyone. There is some material I don't want to divulge while I'm still actively trading, so what I post about will vary and not be all inclusive.

I have ADD, so this is probably the best way for me to post some material from some of my notebooks over time. In the past I've posted a idea or two when I had time and then moved on to other things. This has no doubt made others mad, so I hope to only hang out here and post followups. I'm not around all the time, so it may be days or weeks before I get back to this thread. I hope Magna won't close it without me requesting it.

As far as the material, I'll post stuff that I believe is somewhat unique and has some value to people wanting to improve their overall trading or models and methods.

I respect all opinions as they reflect the persons knowledge, values, and life experiences to that point. I don't respond to opinions as there's no way to argue objectively such matters. I try to post objective material so others can do the same calculations and come up with the same conclusions. If we differ on interpretations of the output, then so be it. I have no interest in participating in flame wars. Hopefully the differing opinions will not be edited out of this journal so others that share the viewpoint of the poster can PM them and do their own research.

Please do not PM me with questions. I've let my mailbox fill up several times because I get overwhelmed when I have 15-20 PM's since the last time I logged in. Anything worth discussing is worth posting to this journal. If you have some secret formula and want my opinion, please keep it to yourself or ask someone else's opinion.

Registered: Apr 2002
Posts: 700

I hope you guys aren't offended if I ignore your questions for now. I'd like to spend my limited time posting some new info. from my notes to get things started.

For the first topic, I tought I'd post on some work in the area of consistency. Any serious trader knows they are responble not only for profits, but also the minimization of drawdowns.

The first thing I think of when I think of consistency is timeframe. I want to be consistently profitable over x timeframe. The x could be each day, a week, a month, a year, 10 trades, 50 trades, etc. Each person needs to decide the timeframe that they need to be consistently profitable. It's pretty common to see posts like I want to be a daytrader and make y dollars per-day every day. That trader has decided that their timeframe is one day for consistent profitability.

Make a decison on a timeframe over which you want to be consistently profitable. In the coming posts I'll show you some tools to help reach your goal and show what it takes to reach it.

Before I go any further I wanted to make sure everyone knows these two concepts.

Expectation of a trade = (PW * AW) - (PL * AL)
Expectation of profit factor = (PW * AW) / (PL * AL)

where

PW = probability of a winning trade
AW = average size of winning trade
PL = probability of a loss
AL = average size of a loss

Just about everyone is familiar with the expectation of a trade or just plain "expectation". I've not seen anyone ever post about the expectation of the profit factor so I'm mentioning it here. This will be useful in the following posts.

Registered: Apr 2002
Posts: 700

Ok, now you have a timeframe for being consistently profitable. Now what does it take to achieve it? One more decision has to be made before continuing. What level of confidence do I need for consistent profitability. Some will be happy with 90%, others 95%, and some like me require 99%. So, to put it together in my case I require consistent profitability on a monthly basis with a 99% level of confidence. So in other words it's ok for me to have a losing month once every 8+ years.

One series of studies I did was to find out what were the important factors in consistency. I did tests on size of expectation, % wins, profit factor, number of trades within a timeframe, and the effect of dispersion of trades (std. deviation) has on the results. For instance, here's a run for a daytrader that does 10 trades a day with 70% wins and makes $500 on each win and loses $500 on each loss. As you can see this is quite profitable at the 50% level (the average over time). However you can also see that after the 80% level the trader actually loses money. So for a daytrader with these numbers they only have a 80% chance of achieving their goal of consistent profitability on a daily basis. If that level of confidence is ok, then they have a example to use going forward.

Attachment: daytrade.txt
This has been downloaded 2485 time(s).

Registered: Apr 2002
Posts: 700

Now what happens if that daytrader decides he wants 95% level of confidence so that he'll accpect one losing day per month. What changes would he make? If he doubles his expectation does that help? As you can see from this run, he still only has about 80% confidence level with double the expectancy on each trade.

Attachment: daytrade2.txt
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Registered: Apr 2002
Posts: 700

How about if he increases the winning percent to 80% and keeps the expectancy at $400, so that each win now becomes $600 and each loss now becomes $400.

Attachment: daytrade3.txt
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Registered: Apr 2002
Posts: 700

Obviously after the last test this daytrader would know that with him winning 80% of the time and having a higher win size than loss size he'd be rich in record time. He'd now know that he has more than a 99% chance of making a profit each and every day he trades. What do think his psychology might be like. How about ..."time to trade can't wait to see how much I make today".

Notice the expectancy didn't change from this test to the last one. I did this to show that expectancy really isn't a critical component of being consistently profitable. Remember a couple of posts ago I posted the expected profit factor. In this case it worked out as:

Epf = (PW * AW) / (PL * AL)
Epf = (.8 * 500) / (.2 * 400)
Epf = (400) / (80)
Epf = 6 or the 50% level described in the test

Now that we've seen the expectancy didn't improve the likelihood of achieving consistency, what effect did changing the expected profit factor. We can use the equation to keep the same win % and win size to solve for the old PF of 2.33 at the 50% level as in the previous test.

2.33 = (.8 * 500) / (.2 * AL)
2.33 = 400/.2AL
2.33*.2AL = 400
.2AL = 400/2.33 or 171.67
AL = 171.67 / .2 or 858.35

When we plug in the new test of 80% winners, $500 win for each winner and 858.35 for each loser (to keep the profit factor at 2.33) we get these results.

Attachment: daytrade4.txt
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Registered: Apr 2002
Posts: 700

From this past test we can see that if we kept the profit factor the same but changed the win % and expectancy, we'd have the same confidence level as we started with 80%. From this we can tell the win % and expectancy are not critical to consistency. One of the keys that is important is the expected profit factor. The higher the profit factor, the more liklely we are to achieve consistent profitabilty.

What would have happened if instead of changing the win % we just changed from 10 trades per day to 20 trades per day. Then our daytrader would have a win% of 70%, win of $500, loss of $500 and twice as many opportunities per-day. Here's the results of that test.

Attachment: daytrade5.txt
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Registered: Apr 2002
Posts: 700

As you can see from this last test by increasing the number of trades from 10 to 20 and keeping the profit factor, win %, and expectancy the same we've improved the confidence level to above 95%. So if our daytrader wanted to be 95% confident that he'd make money every day he could have also increased the number of trades per-day to achieve his goal. Now we have two variables that have an impact on consistent profitability (profit factor and frequency of trades).

If our daytrader wasn't using strict targets and let the profits fluctuate and used a trailing stop in addition to the initial stop we'd increase the dispersion of the trades (std. deviation). For this example I'm using our 20 trades, 70% winners, $500 win, $500 loss, and letting the std. deviation of winners grow to $500 (100% of average) and std. deviation of losers grow to $250 (50% of average). This will let you see the effect of dispersion of outcomes on consistency. As you can see, by letting the winners ride a little and dragging a stop loss behind, the trade effect of the disperison of the trades on the overall results was there but not very important in impacting the consistency we're looking for.

Attachment: daytrade6.txt
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Registered: Apr 2002
Posts: 700

The main idea of this first series of posts was to show what is important in moving toward becoming a consistent winning trader. By choosing a timeframe and then working on both the profit factor and number of trades you can move toward the goal of consistent profitability.

Here's some rough estimates of the relationship between trade frequency and needed profit factor to achieve a 95% level of confidence that you'll be profitable within a timeframe for a single method.

# trades......profit factor needed

10............4.00
20............2.50
30............2.00
40............1.75
60............1.50

So, if you wanted to be profitable every week at the 95% confidence level (you'd still have about 3 losing weeks per-year), and all your method could produce was a 1.50 profit factor, then you'd need 60 trades out of it to achieve your goal.

From this you can tell that to achieve consistent profitability on a daily basis, either you've got some miracle system or you trade like a madman.

In the next series of posts I'll go over using multiple systems to improve consistency. That's all I'm going to post today. I'll be in and out at least through the first hour of trading for the SP today, so if anyone has any questions on this topic I'll try to answer them.

Have a good day!

Registered: Apr 2002
Posts: 700

Attachment: dsc389.txt
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Quote from nov_trader:

Acrary,

Amazing stuff.

"
Model name daytrade
# of trades in series 10
% of trades that are winners 70
Mean of winning trades 500
Std. Dev. of winning trades 0
Mean of losing trades 500
Std. Dev. of losing trades 0


Outcome Profit Factor Max DD
1% level 5,000.00 10.00 0
5% level 4,000.00 9.00 -500
10% level 4,000.00 9.00 -500"

Could you please tell us, how did you get the probability of $5000 with 1% level.? Did you use some kind of Montecarlo analysis.

Thx

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Registered: Apr 2002
Posts: 700

From the test you can see it should win 70-80% of the time each month, have a monthly max drawdown of about 16k and a max profit of about 33k. Here's the graph of the monthly results for the past 5 years. The red indicates losing months and the green are winning months. As you can see there were 12 losing months out of 60 for 80% win rate, the actual max profit month was about 34k and the largest losing month was about 24k. I attribute the large losing month outside of the norm to the fat tail effect in the markets and is one of the reasons why I came up with another version to simulate the fat tails.

Attachment: dsc389.gif
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Registered: Apr 2003
Posts: 1745

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Quote from acrary:

Epf = (PW * AW) / (PL * AL)
Epf = (.8 * 500) / (.2 * 400)
Epf = (400) / (80)
Epf = 6 or the 50% level described in the test

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Registered: Apr 2002
Posts: 700

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Quote from sprstpd:

Just trying to follow the math and got stumped on these equations til I noticed you used AW = 500 rather than AW = 600. If you use AW = 600, Epf = 6. But if AW = 500, Epf = 5.

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Registered: Apr 2002
Posts: 700

The next series of posts will be about some of the methods I use to make sure I achieve my goal of consistency. In my case the goal is 99% chance of profitability per-month. If your goal is daily, weekly, or yearly profitability then just think "daily or yearly" for everything I say about the month.

From the first set of posts you can see it's nearly impossible to reach a goal of 99% profitability every month based on trades or profit factor for a single method. Most of my models trade no more than 15 times per-month and the best Profit Factor I've developed that's consistent is around 2.50. With that in mind I knew I was going to have to use more than one method to achieve my goal. When I started out I had lots of questions like "How do I know if I should use more than one model?", "Since the numbers are different for each model how much should I trade of model x for every contract of model y?". I'll show you how I came up with some ideas about using more than one model to improve performance.

I spent some hours going through my notebooks to pull out the couple of items to share. I think it'll be easier to understand if it's done through with examples so I've pulled the monthly performance numbers for 3 models and put them into a text file. If you want to work through the examples in a spreadsheet you'll need to save this file. To import it to Excel just use Tab delimited, and then for the first field change the format to Text. It should import correctly.

Attachment: models.txt
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Registered: Apr 2002
Posts: 700

The 3 models all trade the same market (SP). I don't want to share the exact details so I'll post some of the overview on each.
The file contains the monthly numbers for each model from 1/1999 - 5/2004. I chose the 3 models because they give a wide spread of numbers like you might find in different methods. Things like win rate, profit factor, number of trades, and total profit are spread out so it'd be tough to just guess at whether to trade 1,2, or all 3 of the models and how much to trade of each one (before money management).


Model 1

Trades about 120 times per-year or about 10 trades per-month
The profit factor is 1.99 and 41.72% of the trades have been profitable.
The total profit over the time period is $1,551,005.
The Max drawdown during the period was $54,095.
10 of the 65 months were losers.

Model 2

Trades about 145 times per-year or about 12 trades per-month.
The profit factor is 1.70 and 59.37% of the trades have been profitable.
The total profit over the time period is $1,365,415.
The Max drawdown during the period was $90,170.
18 of the 65 months were losers.

Model 3

Trades about 90 times per-year or about 7.5 trades per-month.
The profit factor is 1.57 and 48.78% of the trades have been profitable.
The total profit over the time period is $495,365.
The Max drawdown during the period was $97,000.
21 of the 65 months were losers.

This is what the speadsheet should look like after importing the text file.

Attachment: models.gif
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Registered: Mar 2003
Posts: 3575

Hello:
I wonder how important consecutive losers and winners is to your evaluation of a system. I assume you that the drawdowns we see are acceptable, but how far from these figures would you allow it to go before withdrawing the system from trading? For my own systems, I look at standard deviation of profits on a monthly basis as a warning signal. I look forward to your reply. Steve46

Registered: Apr 2002
Posts: 700

The first of the important concepts is to avoid trading correlated methods. You've probably read in some system development books that you should use negatively correlated methods. That's nice to hear but how do you achieve it? In most cases you don't, however you can achieve non-correlation by avoiding using the same stop methods, or same entry methods from one model to the next. I'll go over creating negatively correlated methods in another series but right now I just want to show the benefits of non-correlation.

Using the 3 models as a example, I've added the monthly numbers for each model into column E. Then at the bottom I computed the Monthly average, Monthly Min, Monthly Max, and Total net profit for all the methods. Below the numbers is the correlation coefficient for each of the models versus the other. For example, C74 is the correlation between the monthly numbers for model 1 and model 2. D74 is the correlation for model 1 and model 3, and D75 is the correlation for model 2 and model 3. From my experience a number between -.2 and +.2 usually means a random correlation between the numbers. As you can see each of these has a slightly negative correlation which to me means they aren't correlated.

In columns E68-E71 you can see the benefit of using the systems together. In this case the total monthly profit jumps up and the largest monthly loss is actually lower than 2 of the 3 models individually. Imagine if you could trade model 1 and then add the other 2. When the methods aren't correlated, the profits go up without increasing drawdowns. When combined, the number of profitable months grows to 58 out of 65 for the entire period, or about 89% of the months. Compare this with model 1 (55/65 or about 85% profitable), model 2 (47/65 or 72% profitable), and model 3 (44/65 or 68% profitable). Just from this technique you can see there is some benefit to trading multiple models.

Here's what I'm looking at:

Attachment: sum.gif
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Registered: Apr 2002
Posts: 700

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Quote from steve46:

Hello:
I wonder how important consecutive losers and winners is to your evaluation of a system. I assume you that the drawdowns we see are acceptable, but how far from these figures would you allow it to go before withdrawing the system from trading? For my own systems, I look at standard deviation of profits on a monthly basis as a warning signal. I look forward to your reply. Steve46

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Registered: Apr 2002
Posts: 700

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Quote from ig0r:

are those systems curve-fit or are those forward-tested numbers? not that it really matters in the context of the lesson but they have some pretty spectacular results and I was just curious

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Registered: Apr 2002
Posts: 700

One other thing about correlation I wanted to post was that even if we found a negative correlation, do a couple checks.
If we had say -.4 between two models you might think you found a great negative method to complement your primary model. Another way I found to check it is to create a table of correlations with rolling periods (in the case of monthly 12 periods). If the correlation isn't consistent and smooth then you're probably looking at fools gold (expect the correlation to revert to random over time in the future).
Using our 3 models I've put in columns for the correlations between each of the models. You can see by inspection that the 12 month rolling correlations are moving around wildly. This is a pretty clear example of random correlations between the models.

Attachment: rolling.gif
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Registered: Apr 2002
Posts: 700

Correlation cont'd.

Here's a screenshot of crude versus unleaded gas using 30 day average correlations between the two. Over the past 5 years the correlation was greater than 95% so you'd expect them to be highly correlated. This is what it looks like when they are.

Attachment: gas.gif
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Registered: Apr 2002
Posts: 700

Correlation cont'd.

Ok, so far most of this is probably old news to most of you. I needed to post it as background for those just getting started.

The whole idea of combing models is to improve the consistency of the results. We've seen in a macro way how they can provide some benefit. Now, how do we know which models to combine? Also how much of model 1 should we trade with how much model 2 and how much of model 3? Also if I had a model 4 how could I tell if I should add it in as well?

Those are questions I wanted to answer when I started down this path. Hopefully it won't be too difficult to follow.

If you've taken a elementary stats class you'll know that std. deviation measures dispersion from the norm. Norm being defined as the median or average. You'll also note that the level of std. deviation measures the distance away from the median. We also know that if we can estimate the number of std. deviations away something is, we can look it up as a Z score in a normal distribution table to see how far away from the norm we are.

One of the nice tools in trading are the sharpe ratios which are designed to measure consistency. One of them is the modified sharpe ratio. It's defined as average return / std. deviation. For example, if the average return is $100 and the std. deviation is $50 then the sharpe ratio is 2.0. So in other words for me to breakeven or start to lose money I'd have to have a return that was 2 std. deviations worse than the average return. If you look up 2 std. deviations in a stats book under z score you'll note that it equates to 95.44%. So if my returns are normally distributed then I have a 95.44% chance of breaking even or making a profit.
The modified sharpe ratio can be thought of as a z score. The higher the number, the closer we are to achieving consistent profitability. In my case I want to be 99% sure of making a profit each month. So if I were to look it up in a normal distribution table I'd know the Z score I need is approx. 2.58 or a modified sharpe ratio of 2.58.

We also know that the returns are not going to be normally distributed. There will be fat tails so whatever sharpe ratio we come up with, it will be higher than we should expect in normal trading. This is where using the modified sharpe ratio and non-correlated methods pays off. I don't have a way to measure directly the benefit of using non-correlated methods but I know it improves the smoothness of the equity curve. If I combine that with a high modified sharpe ratio I can have a high degree of confidence that my models will be consistently profitable.

Registered: Apr 2002
Posts: 700

Correlation cont'd.

In this example I've taken our 3 models and applied the modified sharpe ratio in column F. I believe the normal method of computing the modified sharpe ratio is to use 36 periods. In this example I've only chosen to use 12 periods. If I used 30+ periods of the 65 total I'd have very little information to evaluate. I included the 36 period numbers in column G. As you can see the numbers in column F are all over the place. I think is because the sample size of 12 periods is too small to get reliable numbers. You can see it's much smoother in column G. In a sec. I'll post how I try to use most of the data and adjust it so we can get a feel for the overall modified sharpe ratio.

Attachment: modified.gif
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Registered: Apr 2002
Posts: 700

Correlation cont'd.

To come up with a single modified sharpe ratio what I've done is simply average all of the 12 period modified sharpe ratio's. In this way I've smoothed it and used most of the data so the results are more likely to represent what is going on. On this screen I've highlighted it as 1.44 in column F. In column G I've posted the average of the 36 period number. As you can see they are resonably close. What does 1.44 represent? It means we'd have to have a month that was more than 1.44 std. deviations away from the average before we'd expect to lose money. If we look it up as a z score we'd see 85.02%. So 85% of the time we'd expect to have a winning month. 85% of 65 months is 55 1/4 months, so we'd expect to see about 10 losing months. In our case we have only 7 losing months. I have no way to measure it but my guess is the other 3 months are improved by the 3 methods being non-correlated.

Attachment: ave.gif
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Registered: Apr 2002
Posts: 700

Correlation cont'd.

With the one number I now have a way of doing comparisons. If I combine say model 1 and model 2 and run the same tests I'll get one result. If I combine model 2 and 3 I'll get another. And if I combine 1 and 3 still another. Then if I do many runs in which I weight each pass say 1 unit of model 1 and 2 units of model 2 I'll get more results. In the end what I found I had to do was develop a program to do all these passes. It weighs each model from 1 - 100 units and determines the optimal modified sharpe ratio. Then it determines the ratio between each of the methods to determine how much of each should be traded. If I have 4 models it'll do tests on all four of them, five, six, 10, 50, etc. All it takes is compute time. For 3 models it takes about 15 sec. for 30 it takes about 1 1/2 hours. If I choose to do all my models, I leave for a couple of days. In the end it gives me the optimal balance for the best modified sharpe ratio. For my trading I had to use 8 very good models to get the number up to 2.58 so that's why I trade against 8 models.

In our 3 model example I ran my program and here were the results:

combine model 1 & 2

Best modified sharpe ratio 1.395 using 1 unit of model 2 to 1.5 units of model 1

combine model 1 & 3

Best modified sharpe ratio 1.666 using 1 unit of model 1 and 1.333 of model 3

combine model 2 & 3

Best modified sharpe ratio 1.057 using 1 unit of model 2 and 2.04 units of model 3

combine model 1 & 2 & 3

Best modified sharpe ratio 1.461 using 1 unit of model 2, 1.26 units of model 1, and 1.08 units of model 3

From this test I can see I should be trading only models 1 and 3 in the ratio shown to achieve the maximum consistency. Adding model 2 actually reduces the consistency. At 1.66 I should see 90.3% of the months being winners. Not up to the 99% level but a definite improvement.

If I had a fourth model I could do the same test and see if it should be included, and if so, what the optimal ratio should be.
It might seem silly to use a lower profit factor, lower win %, and lower total profit model but I'll show the comparison and why I'd do it.

Registered: Apr 2002
Posts: 700

Correlation cont'd.

Well it didn't turn out as I had expected. I applied the weightings as spit out by the program and did the same totals as was done earlier. In column E are all 3 models combined with the optimal weighting. In column F is the combination of models 1 & 2 and in column G is the combination of models 1 & 3. It's pretty easy to see that using 1 & 3 is superior to 1 & 2 mainly because the largest losing month for 1 & 2 is more than double the largest loss for 1 & 3. So if you were to trade at twice the size using 1 & 3 you'd have a lower max losing month and a higher average profit per-month than 1 & 2.

However this shows that if you used 1,2, and 3 in the ratios provided it would kick some serious butt over using 1 & 3 by itself.
Also 1 & 3 have 11 losing months as compared to just 7 for all 3 models combined. In digging through the data I noted that model 3 didn't have very consistent results so the 1.57 profit factor probably isn't representative of it's overall results. In fact model 3 lost money for all of 1999 (a year in which the combined results for 1 & 3 show 6 of the 11 losing months). This just shows once again that the concept is only as good as the underlying models it's built on.

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Registered: Apr 2002
Posts: 700

Correlation cont'd.

Well, here's the snapshot of model 3's annual results. As you can see the win % ranges from 35 - 60 and the profit factor ranges from .79 - 2.89. Obviously this was a bad choice of a model to include in the tests. If the models are inconsistent, then everything else that flows from them will be suspect.

One thing I meant to point out was look at the largest losing month for all 3 models combined with the weightings. Notice it's now lower than any of the models individually. I love this stuff.

I hope I was able to convey the general concept without getting too deep. I spent hours on this trying to figure out how to boil it down to these few posts. In reality this took more than one full notebook of work over a long period of time. If you have any questions I'll try and answer them tomorrow morning. I'm going out to dinner now and I'll be around then.

Enjoy the weekend!

Registered: Dec 2001
Posts: 702

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Quote from acrary:

Correlation cont'd.

Well, here's the snapshot of model 3's annual results. As you can see the win % ranges from 35 - 60 and the profit factor ranges from .79 - 2.89. Obviously this was a bad choice of a model to include in the tests. If the models are inconsistent, then everything else that flows from them will be suspect.

One thing I meant to point out was look at the largest losing month for all 3 models combined with the weightings. Notice it's now lower than any of the models individually. I love this stuff.

I hope I was able to convey the general concept without getting too deep. I spent hours on this trying to figure out how to boil it down to these few posts. In reality this took more than one full notebook of work over a long period of time. If you have any questions I'll try and answer them tomorrow morning. I'm going out to dinner now and I'll be around then.

Enjoy the weekend!

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Registered: Apr 2002
Posts: 700

Sorry I was away yesterday.Weather forecast for yesterday was great and today was supposed to be bad so I went flying. Today I'll be around for most of the day.

In my haste to get finished with last topic I made a mistake. I went back this morning to try and understand how models 1 & 3 couldn't do better than 1,2, and 3 combined. With the difference in the two numbers being about .2 I expected different results. It's also been my experience that a .2 improvement shows in the overall performance numbers. When I re-ran the tests, I found the correct number for 1 & 3 was 1.166 not 1.66. Sorry for the mistake. I'm including a edited copy of that post for anyone cutting and pasting this stuff.

Correlation cont'd.

With the one number I now have a way of doing comparisons. If I combine say model 1 and model 2 and run the same tests I'll get one result. If I combine model 2 and 3 I'll get another. And if I combine 1 and 3 still another. Then if I do many runs in which I weight each pass say 1 unit of model 1 and 2 units of model 2 I'll get more results. In the end what I found I had to do was develop a program to do all these passes. It weighs each model from 1 - 100 units and determines the optimal modified sharpe ratio. Then it determines the ratio between each of the methods to determine how much of each should be traded. If I have 4 models it'll do tests on all four of them, five, six, 10, 50, etc. All it takes is compute time. For 3 models it takes about 15 sec. for 30 it takes about 1 1/2 hours. If I choose to do all my models, I leave for a couple of days. In the end it gives me the optimal balance for the best modified sharpe ratio. For my trading I had to use 8 very good models to get the number up to 2.58 so that's why I trade against 8 models.

In our 3 model example I ran my program and here were the results:

combine model 1 & 2

Best modified sharpe ratio 1.395 using 1 unit of model 2 to 1.5 units of model 1

combine model 1 & 3

Best modified sharpe ratio 1.166 using 1 unit of model 1 and 1.333 of model 3

combine model 2 & 3

Best modified sharpe ratio 1.057 using 1 unit of model 2 and 2.04 units of model 3

combine model 1 & 2 & 3

Best modified sharpe ratio 1.461 using 1 unit of model 2, 1.26 units of model 1, and 1.08 units of model 3

From this test I can see I should be trading all the models in the ratio shown to achieve the maximum consistency. At 1.461 the zscore translates into 85.56% winning months (about 1 3/4 losing months per-year). Not up to the 99% level but a definite improvement over any combination of two systems.

If I had a fourth model I could do the same test and see if it should be included, and if so, what the optimal ratio should be.

Registered: Apr 2002
Posts: 700

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Quote from bdixon619:

How does one go about finding markets for these systems? Is each system you develop market specific? Or, was a system design inspired by experience with one market or another?

What individual market tendencies do you look for before selecting a system for trade in that market? How do you characterize a market?

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Registered: Apr 2002
Posts: 700

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Quote from EricP:

Thank you very much for the ongoing sharing of your thoughts on systems development. It has been very helpful and is very much appreciated. I'm hoping you might have thoughts on the following question.

I use automation to trade individual stocks, and use systems on many different stocks. At any given time, I might be paper trading 300+ stocks, and have 100+ stocks active for live trading. I use a 93% confidence level criteria over the last 120 paper trades to determine whether to activate a security for live trading. In this way, I activate strictly based upon the risk/return of a specific security's past profit performance. However, I really like the way you activate based upon the actual diversification that the new security (or system) adds to your overall profitability.

The issue I face is liquidity. For individual stocks, I run into liquidity issues for the less active stocks (<1M shares per day) that I trade which can reduce or eliminate system profits as my trading size increases in that stock. As a result, my 'optimal' combination of securities (i.e. systems) may only be accurate to trade will a smaller level of capital due to deteriorating performance caused by liquidity at larger position sizes. Do you have any suggestions on how I might consider improving my security (or system) activation routine based upon balancing both the liquidity constraints and diversification value?

Thanks for your thoughts.
-Eric

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Registered: Apr 2002
Posts: 700

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Quote from ig0r:

acrary, can you post the algorithm you use to adjust the number of contracts traded to volatility? Is it similar to the one used in the turtle trader method?

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Registered: Apr 2002
Posts: 700

Quote from onelot:

acrary,

regarding the correlation of multiple instruments versus model correlations: after finding no correlation between instruments should we look at the instruments as separate models if we are using multiple instruments to one model in order to carry on the sharpe work? i'm not sure if that's what you were implying here in response to virgins post:

so for instance, instead of the spread sheet comparing model1>model2>model3 to one instrument it could compare instrumentX>instrumentY>instrumentZ to one model, no?... as opposed to only measuring say the correlation of the 30 day price average between instruments.



Yes, that's the idea. You'd just have to make sure the periods were identical (in this case monthly). If you were doing long term trendfollowing you might have to switch to quarterly or longer timeframes for comparison due to the fewer trades.



just to make sure i understand the big picture, this is all being done to increase frequency in the desired profitable timeperiod, correct? so if i'm understanding, sub-par models tested individually with low frequency can be morphed into an above-par model when combined with other non-correlated sub-par models (assuming they're not too sub-par), thus increasing frequency, consistency, and lowering the need for a higher profit factor? thank you for presenting the information the way you did, i would not have made that connection otherwise (assuming i'm on the right track). fascinating.



Yes, you understand it perfectly. If you ever trade money for others the first thing they want to know is that you've done everything possible to avoid losing periods. If you tell them "my performance period is one month and I've planned my trading so that there is a 95% chance of a profit within any single period", they'll be happy to hear what else you have to say. You'll also see by working hard on consistency that you have better money management options available to you than "risk no more than 2% of capital on any trade".

Registered: Apr 2002
Posts: 700

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Quote from mind:

i also tried out the edge test. i used shapre ratio of my system and compared it with randomly takekn daily returns from the makret itself. systems i liked where on top of the list and made the effort useless.

peace

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Registered: Apr 2002
Posts: 700

Thank you Magna for re-opening this thread.

Here's a table that I've used with single systems to get an idea of profitability for a period. It uses profit factor and number of trades during a period to estimate the percentage of time the period will be profitable.

For example, if I have a system with a profit factor of 1.50 and it trades 50 times per-month then I'd expect this system by itself to be profitable about 89% of the time. If I wanted the system to be profitable 95% of the time I can see I'd have to increase the trading frequency to about 90 trades per-month.

I printed it out in wordpad in landscape mode for reference.

Attachment: baseline.txt
This has been downloaded 859 time(s).

Registered: Jul 2005
Posts: 88

Acrary, thank you for providing the table. Im interested in the math behind. Can you explain the calculation or point me to a page where i can find further information?

Registered: Apr 2002
Posts: 700

It's just a Monte Carlo sim using 100,000 passes per table entry and plugging in the profit factor as a multiplier for the winning trades. Sum the winners and losers until you've reached the number of trades in the pass. Then see if the pass is a winner or loser. The percentage in the table is the number of winners in relation to the 100,000 passes.

Registered: Apr 2002
Posts: 700

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Quote from bulat:

Would you consider this to be a tradeable pattern, or would you avoid such patterns that work as a combination of many identifiers that fail when tested separately?

-bulat

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Registered: Apr 2002
Posts: 700

Ok, I've given up on the whole word processing "professional looking" project. I don't have the desire to become a expert on the Microsoft products. I also noticed on the work I've completed that I kept going through notes and pulling out more and more related material. I think I could probably create a 3 book series based on the material. I also realize most people won't go further than one or two systems and then either bank some coin or give up and realize it's too hard.

So, for part 2 of this journal I decided to work towards a goal.
The goal is to replicate the performance of Monroe Trout in the New Market Wizards book. In the beginning of the interview Jack Schwager described Trout's 5+ year performance numbers. A 67% annual return, 87% of all months profitable, and a max drawdown of just over 8%. This should be fun (at least for me).
I'll do this with 5 models or less and try to explain some stuff as I go along.

Registered: Apr 2002
Posts: 700

From the first part of this journal I posted some about weighting different systems using the modified sharpe ratio. Here's a run using the first model. I'm using only monthly performance numbers to come up with the weights. Obviously if only used one model the weights would be unimportant. The modified sharpe ratio posted is the monthly modified sharpe ratio.

Attachment: 1modcor.txt
This has been downloaded 469 time(s).

Registered: Apr 2002
Posts: 700

The weighting between the models is then passed into a money management program. To make the process easier to understand I'm using a fixed per-cent risk model for the selected portfolio. Here is a run using just the one model and projecting the performance out 12 months. For the first pass I'm just using 1% per-trade risk. I'm doing 100,000 passes in the Monte-Carlo run.

Looking at the numbers you can see we project a return of 36.8%, a average drawdown of 10.9%, and 69.5% of the months were expected to be profitable. None of the criteria were met using one model so obviously more than one will be needed.

Near the top of page one is the minimum funding to trade the model in the portfolio and here's how it's calculated.

How much do I need to fund a account?

M = Min. Required Capital
A = Average loss
P = Per-cent risk
DD = Drawdown to recover at 95% of Monte Carlo Sims in decimal ex. .2 = 20%

M = (A/P) * (1/(1-DD))

A = average loss of 1,000
P = 1% risk per-trade
DD = 19% drawdown at 95% level

M = (A/P) * (1/(1-DD))

M = (1000/.01) * (1/(1-.19))
M = 100,000 * (1/.81)
M = 100,000 * 1.236
M = 123,600

I use the 95% level as a cutoff because I'd stop trading a system at that point.

The projected per-cent months profitable is from the sheet I previously posted showing the effect of number of trades with profit factor to figure out the expected percentage of profitable months.

On page 2 are the Monte-Carlo runs. From this you can see there is about a 94% chance of this system being profitable at the end of 12 months.

Since the first model didn't get close to our goals I'll add a second model and you'll see how that works.

Attachment: 1modmmg.txt
This has been downloaded 346 time(s).

Registered: Apr 2002
Posts: 700

We're back to the weighting program this time using two models.
You can see the modified sharpe ratio was improved by adding a second model (better than either of the single models by themselves). In the two model results there are a couple of things I'll go over. The first is the weighting. The position in the weighting is reflective of the position of the model under model #. In this case the best weighting to achieve the optimal modified sharpe ratio is 3 units of model 1 for every 1 unit of model 2. I call them units because they aren't contracts (they are volatility normalized results). Notice in the two model result there is a 12 month rolling correlation. To do this I use a minimum of 48 months of results. The rolling correlation starts after the first 12 months and then keeps figuring them for each month until the end of the data. The total of all the rolling months is then divided by the number of months tested. The number in the report is the average correlation. I also figure the standard deviation of the correlation for each two model pair and add/subtract it from the average to figure out how stable the correlation is. In this case the two models are negatively correlated with a pretty low standard deviation. It would take a more than 3 standard deviation month for them to be highly correlated. This would be a very good candidate for trading with the first model.
Under the money management contract multiplier notice everything is related to 1.0. I did this to improve the processing in the money management software. You'll see these numbers plugged in there.

Attachment: 2modcor.txt
This has been downloaded 323 time(s).

Registered: Apr 2002
Posts: 700

If you want to create a program here's how I calculated the correlation between two systems.

Attachment: corr.txt
This has been downloaded 486 time(s).

Registered: Apr 2002
Posts: 700

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Quote from makosgu:

The correlation calculation here is straight forward. With correlation tho, I presume that the objective would be to have n systems with 0 correlation. The pairwise correlation (N^2) would be desired to be zero...

However, given the sample size of returns (several months), is it introducing any sample errors? What I mean is that if I were to assess the correlation, should I be taking a random sample from the population of the systems returns and then evaluating the correlation or does it all just collapse to this simple case calculation...

Very interesting stuff... For sure! I have considerable quant background but have kept much of this stuff separated from trading (well actually all aspects separate from trading). It is a knowledge and application weakness...

MAK!

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Registered: Apr 2002
Posts: 700

Here's the money management sim for the two combined models.
I ran the report once and it hit the profit objective so I cut back the % risk per-trade to 3/4%. Notice the contract multiplier is the same as on the weighting report. With the addition of the second system the expected return moved up to 49.6%, the expected drawdown dropped to 9.8%, and the profitable months moved up to 74.9%. The combination of two uncorrelated systems has helped.

One thing to note about Monte-Carlo tests is they tend to offer pessimistic results as compared to the real-world trades. This happens because when two models are structurally non-correlated the chances both are in drawdown at the same time are virtually nil. Monte-Carlo assumes both are just as likely to be in drawdown at the same time as any other outcome.

Since the objectives aren't met, it's on to using 3 models.

Attachment: 2modmmg.txt
This has been downloaded 352 time(s).

Registered: Apr 2002
Posts: 700

Here's the 3 model weighting report. The third model I added is the same as the first one except it is run on the NQ market instead of ES. It also is at a higher timeframe so it trades less often. I added it to show that by changing markets and timeframe but using the same methodology you can sometimes get good results. Notice the reduced modified sharpe ratio on model 3. This is what happens when the trading frequency declines. Also notice that the overall correlation between 1 and 3 is slightly correlated. Also notice the standard dev on the correlation moves the correlation +1 standard dev. up to +.32. While it's ok, if I was looking to trade in realtime, I'd look to see if I have another model with better correlation stats. In the big picture the 3 model test shows the modified sharpe ratio is moving up, so I expect the number of winning months to go up as well.

Attachment: 3modcor.txt
This has been downloaded 293 time(s).

Registered: Apr 2002
Posts: 700

With the 3 models the projected profits moved up past the profit target using .75% risk per-trade but the max dd was above the goal. I reran the mmgt test dropping the fixed % to risk per-trade to .6%. The result was all three of the performance goals came closer. The projected average return moved up to 58.9%, the average drawdown dropped to 9.1%, and the per-cent of profitable months moved up to 79.4%.

Notice how every time I hit the performance goal I reduce the risk per-trade. This is done to lower the expected drawdown. In testing I've found if you use fixed % risk per-trade the best way to lower the drawdown is to lower the average losing trade. At this level the only tool is the % risked.

I'll need a fourth model to see if we can meet the goals. I'm going to get something to eat and be back.

Attachment: 3modmmg.txt
This has been downloaded 232 time(s).

Registered: Apr 2002
Posts: 700

Now we're up to 4 models. Here's the weighting report. I love to look at these reports when they start getting up in numbers of systems because it tells you so much about how your portfolio is being managed. Once again the test reveals the best combination of models is using all 4 models. I like to look at the 12 month rolling correlation with +1 std. dev. in the two model results and also the two model modified sharpe ratio's to get ideas on what I need to work on to improve my performance.

Attachment: 4modcor.txt
This has been downloaded 248 time(s).

Registered: Apr 2002
Posts: 700

Once again, here's the 4 model mmgt report. I once again lowered the % risked on each trade. This time down to .5%.
The projected returns stayed about the same with lower drawdowns and more months projected to be profitable.
The projected return was 58.1%, with expected average drawdown to be 7.7%, and expected per-cent months profitable to be 83.6%.

Attachment: 4modmmg.txt
This has been downloaded 226 time(s).

Registered: Apr 2002
Posts: 700

I realize not everyone will have the talent or desire to develop software to do these types of tests. Because I do want to help individual traders, I'm willing to have these tests run on your stuff on up to 5 models (I have a helper that's pretty bored right now). To do this I'd need the following for each model.

1). A monthly total profit/loss for at least 48 months.

2). A trade list with date followed by a comma and the total closed profit.

I'll setup a email account somewhere where anyone that wants this done can send the files as attachments. If I get too many requests, I'll let you know.

I'll post the files from model131 as examples.

Here's the monthly file.

Attachment: model131.txt
This has been downloaded 310 time(s).

Registered: Apr 2002
Posts: 700

And here is the individual trades for model131.

Attachment: model131.txt
This has been downloaded 294 time(s).

Registered: Apr 2002
Posts: 700

Here's the 3 model weighting report. The third model I added is the same as the first one except it is run on the NQ market instead of ES. It also is at a higher timeframe so it trades less often. I added it to show that by changing markets and timeframe but using the same methodology you can sometimes get good results. Notice the reduced modified sharpe ratio on model 3. This is what happens when the trading frequency declines. Also notice that the overall correlation between 1 and 3 is slightly correlated. Also notice the standard dev on the correlation moves the correlation +1 standard dev. up to +.32. While it's ok, if I was looking to trade in realtime, I'd look to see if I have another model with better correlation stats. In the big picture the 3 model test shows the modified sharpe ratio is moving up, so I expect the number of winning months to go up as well.

Attachment: 3modcor.txt
This has been downloaded 293 time(s).

Registered: Apr 2002
Posts: 700

With the 3 models the projected profits moved up past the profit target using .75% risk per-trade but the max dd was above the goal. I reran the mmgt test dropping the fixed % to risk per-trade to .6%. The result was all three of the performance goals came closer. The projected average return moved up to 58.9%, the average drawdown dropped to 9.1%, and the per-cent of profitable months moved up to 79.4%.

Notice how every time I hit the performance goal I reduce the risk per-trade. This is done to lower the expected drawdown. In testing I've found if you use fixed % risk per-trade the best way to lower the drawdown is to lower the average losing trade. At this level the only tool is the % risked.

I'll need a fourth model to see if we can meet the goals. I'm going to get something to eat and be back.

Attachment: 3modmmg.txt
This has been downloaded 232 time(s).

Registered: Apr 2002
Posts: 700

Now we're up to 4 models. Here's the weighting report. I love to look at these reports when they start getting up in numbers of systems because it tells you so much about how your portfolio is being managed. Once again the test reveals the best combination of models is using all 4 models. I like to look at the 12 month rolling correlation with +1 std. dev. in the two model results and also the two model modified sharpe ratio's to get ideas on what I need to work on to improve my performance.

Attachment: 4modcor.txt
This has been downloaded 248 time(s).

Registered: Apr 2002
Posts: 700

Once again, here's the 4 model mmgt report. I once again lowered the % risked on each trade. This time down to .5%.
The projected returns stayed about the same with lower drawdowns and more months projected to be profitable.
The projected return was 58.1%, with expected average drawdown to be 7.7%, and expected per-cent months profitable to be 83.6%.

Attachment: 4modmmg.txt
This has been downloaded 226 time(s).

Registered: Apr 2002
Posts: 700

I realize not everyone will have the talent or desire to develop software to do these types of tests. Because I do want to help individual traders, I'm willing to have these tests run on your stuff on up to 5 models (I have a helper that's pretty bored right now). To do this I'd need the following for each model.

1). A monthly total profit/loss for at least 48 months.

2). A trade list with date followed by a comma and the total closed profit.

I'll setup a email account somewhere where anyone that wants this done can send the files as attachments. If I get too many requests, I'll let you know.

I'll post the files from model131 as examples.

Here's the monthly file.

Attachment: model131.txt
This has been downloaded 310 time(s).

Registered: Apr 2002
Posts: 700

And here is the individual trades for model131.

Attachment: model131.txt
This has been downloaded 294 time(s).

Registered: Apr 2002
Posts: 700

Here's the 3 model weighting report. The third model I added is the same as the first one except it is run on the NQ market instead of ES. It also is at a higher timeframe so it trades less often. I added it to show that by changing markets and timeframe but using the same methodology you can sometimes get good results. Notice the reduced modified sharpe ratio on model 3. This is what happens when the trading frequency declines. Also notice that the overall correlation between 1 and 3 is slightly correlated. Also notice the standard dev on the correlation moves the correlation +1 standard dev. up to +.32. While it's ok, if I was looking to trade in realtime, I'd look to see if I have another model with better correlation stats. In the big picture the 3 model test shows the modified sharpe ratio is moving up, so I expect the number of winning months to go up as well.

Attachment: 3modcor.txt
This has been downloaded 293 time(s).

Registered: Apr 2002
Posts: 700

With the 3 models the projected profits moved up past the profit target using .75% risk per-trade but the max dd was above the goal. I reran the mmgt test dropping the fixed % to risk per-trade to .6%. The result was all three of the performance goals came closer. The projected average return moved up to 58.9%, the average drawdown dropped to 9.1%, and the per-cent of profitable months moved up to 79.4%.

Notice how every time I hit the performance goal I reduce the risk per-trade. This is done to lower the expected drawdown. In testing I've found if you use fixed % risk per-trade the best way to lower the drawdown is to lower the average losing trade. At this level the only tool is the % risked.

I'll need a fourth model to see if we can meet the goals. I'm going to get something to eat and be back.

Attachment: 3modmmg.txt
This has been downloaded 232 time(s).

Registered: Apr 2002
Posts: 700

Now we're up to 4 models. Here's the weighting report. I love to look at these reports when they start getting up in numbers of systems because it tells you so much about how your portfolio is being managed. Once again the test reveals the best combination of models is using all 4 models. I like to look at the 12 month rolling correlation with +1 std. dev. in the two model results and also the two model modified sharpe ratio's to get ideas on what I need to work on to improve my performance.

Attachment: 4modcor.txt
This has been downloaded 248 time(s).

Registered: Apr 2002
Posts: 700

Once again, here's the 4 model mmgt report. I once again lowered the % risked on each trade. This time down to .5%.
The projected returns stayed about the same with lower drawdowns and more months projected to be profitable.
The projected return was 58.1%, with expected average drawdown to be 7.7%, and expected per-cent months profitable to be 83.6%.

Attachment: 4modmmg.txt
This has been downloaded 226 time(s).

Registered: Apr 2002
Posts: 700

I realize not everyone will have the talent or desire to develop software to do these types of tests. Because I do want to help individual traders, I'm willing to have these tests run on your stuff on up to 5 models (I have a helper that's pretty bored right now). To do this I'd need the following for each model.

1). A monthly total profit/loss for at least 48 months.

2). A trade list with date followed by a comma and the total closed profit.

I'll setup a email account somewhere where anyone that wants this done can send the files as attachments. If I get too many requests, I'll let you know.

I'll post the files from model131 as examples.

Here's the monthly file.

Attachment: model131.txt
This has been downloaded 310 time(s).

Registered: Apr 2002
Posts: 700

And here is the individual trades for model131.

Attachment: model131.txt
This has been downloaded 294 time(s).

Registered: Apr 2002
Posts: 700

Here's the 3 model weighting report. The third model I added is the same as the first one except it is run on the NQ market instead of ES. It also is at a higher timeframe so it trades less often. I added it to show that by changing markets and timeframe but using the same methodology you can sometimes get good results. Notice the reduced modified sharpe ratio on model 3. This is what happens when the trading frequency declines. Also notice that the overall correlation between 1 and 3 is slightly correlated. Also notice the standard dev on the correlation moves the correlation +1 standard dev. up to +.32. While it's ok, if I was looking to trade in realtime, I'd look to see if I have another model with better correlation stats. In the big picture the 3 model test shows the modified sharpe ratio is moving up, so I expect the number of winning months to go up as well.

Attachment: 3modcor.txt
This has been downloaded 293 time(s).

Registered: Apr 2002
Posts: 700

With the 3 models the projected profits moved up past the profit target using .75% risk per-trade but the max dd was above the goal. I reran the mmgt test dropping the fixed % to risk per-trade to .6%. The result was all three of the performance goals came closer. The projected average return moved up to 58.9%, the average drawdown dropped to 9.1%, and the per-cent of profitable months moved up to 79.4%.

Notice how every time I hit the performance goal I reduce the risk per-trade. This is done to lower the expected drawdown. In testing I've found if you use fixed % risk per-trade the best way to lower the drawdown is to lower the average losing trade. At this level the only tool is the % risked.

I'll need a fourth model to see if we can meet the goals. I'm going to get something to eat and be back.

Attachment: 3modmmg.txt
This has been downloaded 232 time(s).

Registered: Apr 2002
Posts: 700

Now we're up to 4 models. Here's the weighting report. I love to look at these reports when they start getting up in numbers of systems because it tells you so much about how your portfolio is being managed. Once again the test reveals the best combination of models is using all 4 models. I like to look at the 12 month rolling correlation with +1 std. dev. in the two model results and also the two model modified sharpe ratio's to get ideas on what I need to work on to improve my performance.

Attachment: 4modcor.txt
This has been downloaded 248 time(s).

Registered: Apr 2002
Posts: 700

Once again, here's the 4 model mmgt report. I once again lowered the % risked on each trade. This time down to .5%.
The projected returns stayed about the same with lower drawdowns and more months projected to be profitable.
The projected return was 58.1%, with expected average drawdown to be 7.7%, and expected per-cent months profitable to be 83.6%.

Attachment: 4modmmg.txt
This has been downloaded 226 time(s).

Registered: Apr 2002
Posts: 700

I realize not everyone will have the talent or desire to develop software to do these types of tests. Because I do want to help individual traders, I'm willing to have these tests run on your stuff on up to 5 models (I have a helper that's pretty bored right now). To do this I'd need the following for each model.

1). A monthly total profit/loss for at least 48 months.

2). A trade list with date followed by a comma and the total closed profit.

I'll setup a email account somewhere where anyone that wants this done can send the files as attachments. If I get too many requests, I'll let you know.

I'll post the files from model131 as examples.

Here's the monthly file.

Attachment: model131.txt
This has been downloaded 310 time(s).

Registered: Apr 2002
Posts: 700

And here is the individual trades for model131.

Attachment: model131.txt
This has been downloaded 294 time(s).

Registered: Apr 2002
Posts: 700

Glad that worked!

Ok, with 4 average models I didn't achieve the goal. So of course I've got to add a fifth model. Just to make sure it achieves the goals I'm using one of my models that I trade. It's going to change the weights dramatically, but it should be interesting.

It's probably going to take about 20 min. for the weighting program to do it's crunching.

Registered: Apr 2002
Posts: 700

Anybody that's wants their stuff tested, here's the email address.

[email protected] (lol)

I put in dashes to prevent autobots from putting me on autospam (I've seen others do it so I'm guessing here). Remove the dashes to send stuff to me.

Registered: Apr 2002
Posts: 700

Here's the weight test for 5 models. Notice the modified sharpe ratio for model 5. Also notice how it boosts the overall sharpe ratio for the five systems dramatically. Now also notice how none of the 5 systems has a strong correlation. Even when taking into consideration the standard deviation none of them go above +.4.
Notice on the weighting for the money management how model 5 becomes the basis for all others to weight against. This is what happens when you bring in a great system.

Attachment: 5modcor.txt
This has been downloaded 315 time(s).

Registered: Apr 2002
Posts: 700

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Quote from bjs211:

Acrary,

First, thank you for doing this...very helpful to see simple examples done step by step.

Could you confirm for me that I understand the position sizing element properly...here is my take:

1. Determine the optimal relative position weights for each model by calculating the combined maximum mod sharpe ratio. (I use solver or goal seek in excel)

2. Take the largest weighting and set it equal to 1. All the other models will have position sizes that are a fraction thereof. So for example, if the weightings for 3 different systems came out to be 5, 3, and 1 respectively-- I would use a 1 "unit" position size for system 1, .6 "unit" position size for system 2, and .2 for system 3.

Assuming I was using 1% fixed percentage position sizing methodology, this would equate to 1% equity risked for each position put on for model 1, .6% equity risked for each position put on for model, and .2% for a position in model 3.

Am I looking at this the right way?

Thanks again,
bjs

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Registered: Apr 2002
Posts: 700

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Quote from bjs211:

Thanks for the quick reply. Also, if you don't mind, it would be valuable to hear your recommendations on selecting a money management approach. I realize that this would be getting a bit off topic at the moment, but if you'd come back to the topic when you think it fits, I'd appreciate it.

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Registered: Apr 2002
Posts: 700

Well here's the mmgt projected report for the 5 models.

The goals:

67% average return --- beat 72.1%
87% of all months profitable -- beat 92.6%
8% Max drawdown -- beat 7.6% at 95% probability (once in 20 years)

Notice how the addition of one really good model changed everything. The average annual drawdown is only expected to be 4.8%. Remember this is probably pessimistic because of the limitations of Monte-Carlo testing. The % risked per-trade was also reduced to just .3% so as you can see you don't need to risk alot to gain alot. Of course the proof is not in the model but what would have happened in historical trading. Next I'll post the historical report that uses the weights, and % risk to see how accurate the prediction model is to what have happened.

Attachment: 5modmmg.txt
This has been downloaded 297 time(s).

Registered: Apr 2002
Posts: 700

Here's the historical report for the same period with all of the weighting numbers plugged in.

Risk per-trade .3%
Annualized return 75.3% (slightly better than the model)
% winning months 94.0% (slightly better than the model)
Max DD -7.1% (slightly better than the model)

Going forward the returns are dependent on the stability of the individual models. One of the nice things about using non-correlated models is even if one starts falling apart at least one of the others will most likely pick up some of the losses.

Attachment: hist.txt
This has been downloaded 271 time(s).

Registered: Apr 2002
Posts: 700

The last post of the night. I wanted to just show how to use the model to get a boost in returns while reducing the drawdowns. I'm tired so this is going to be a little simplified.

I call this the free money ratio.

If you noticed on the minimum capital required to trade the 5 systems it only amounted to about $187,000. Since the account had $500,000 all the extra money isn't really working very hard.

The free money ratio is:

FR = account funding / minimum capital required

in this case:

FR = 500,000 / 187,000

FR = 2.673

To use this you multiply the original projected drawdown @95% confidence by the FR to come up with a new target drawdown.
In this case the 95% drawdown was 7.6% * 2.673 = 20.31%

Using this new drawdown target we re-run the mmgt report using a larger risk amount until it's close to the new target. We also use only the min. account required for the initial capital.

In this case I upped the risk per-trade to .75% and set the initial capital to $187,000. You can see from the report the 95% drawdown level is 18.2% so I could have upped the risk per-trade a little more.

What we're doing is saying of the original 500,000 most of it (313,000) is not going to be actively traded. Because of this it will have no risk and no return. The rest will be actively traded at the free money ratio.

The return at the higher risk is 287.2% on the $187,000. While the drawdown at the 95% level is 18.2%.

We then convert the %'s into dollar returns.

Return = 187,000 * 2.872 = 537,064 expected profit
Risk = 187,000 * .182 = 34,034 expected max dd at 95% confidence

we then use the return and risk with the total account

return = 537,064 / 500,000 = 1.074 or 107.4% expected return
risk = 34,034 / 500,000 = .068 or 6.8% expected drawdown @ 95%

As you can see by doing this the return was boosted and the drawdown reduced. You can do this everytime the account hits a new equity high and compound the return to much higher levels.

This works because returns do not grow at a linear rate.
I'm tired and I have to get up in a few hours to trade the DAX so I hope someone got some ideas from all this work.

Take care until the next session.

Attachment: free.txt
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Registered: Apr 2002
Posts: 700

Yes, the two subjects are mutually exclusive.

I wanted to show the benefits of building systems this way and then switching to the process of finding the models to plug in to achieve the results.

My style of development requires a paradigm shift away from tactical trading (buy when x crosses y, etc.) to a strategic vision.
Tactics are still there but only to supplement the strategic goals.

So far it doesn't look like there's much interest in this material apart from professionals so I may take a break.

Registered: Apr 2002
Posts: 700

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Quote from man:

alan

i wonder about one thing. i have never seen a sharpe of 1.6 with such a great sterling (return p.a./ mdd). from a 1.6 equity curve i would expect a sterling of 3, maybe 4 or 5. but 14?

hmm. i hope i did not get anything too wrong here through my fast cross-reading your material ...


peace

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Registered: Mar 2003
Posts: 3575

Alan:

I always enjoy hearing from you on this subject. My approach is similar in many ways, probably because I have listened to your prior comments.

I observe the following:

You look to minimize risk first, then you focus is on consistency of returns. This is what professionals do. Amateurs in contrast look for historical tendency, and throw money at it hoping that risk will "take care of itself". It never does.

On the system side, I have followed a rule that has helped me greatly and is probably of more interest to the retail folks. What I have done is to find repetitive market behaviors and incorporate both the behavior and "failure" into my systems. As you already know, if the repetition is frequent enough, and you have programmed correctly, you will then catch one or the other all the time. This works well IF both behaviors have significantly different results. The obvious example is a breakout after a test of a price level (like say a 50 period EMA) or a failure and move down to nearest support.

personaly, if I were looking to make a dollar trading, after reading Alan's comments I would be mobilizing all my resources to try to learn to use a Monte Carlo Engine (I like @Risk by the way).

Good luck guys,
Steve

Registered: Apr 2002
Posts: 700

It's pretty obvious some of you are expert model builders. Please contribute!

Here's a historical report that was run from one of the submissions risking only .4% per-trade. Pretty humbling to see someone on this site with this kind of talent.

Attachment: history.txt
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Registered: Apr 2002
Posts: 700

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Quote from slacker:

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Registered: Apr 2002
Posts: 700

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Quote from MarketDog:

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Registered: Apr 2002
Posts: 700

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Quote from rokafella:

Dang... thanks for the scare!
So what's in the next chapter?

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Registered: Apr 2002
Posts: 700

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Quote from Walther:

I would like to hear more about what do you mean by a strategic vision ? I do not want to jump to conclusion but do I smell some forward projecting around here ?

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