System Development with acrary

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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.
 

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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
 
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:
 

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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 :)
 
Quote from axeman:

I may have missed it...but what is the starting capital for these results?

peace

axeman
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The models are trading SP, doesn't matter :) I'm assuming 1 contract
 
Well....I trade equities.... percent return on capital is what I need
to compare to acrarys results fairly, (along with other factors).

peace

axeman


Quote from ig0r:

The models are trading SP, doesn't matter :) I'm assuming 1 contract
More...
 
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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Steve, the number of winners or losers in a row has no importance to me as long as the trades are independent. If I see numbers outside of normal bounds then I search for dependency (loss begets loss, etc.). To know if the number of wins or losses is outside of the normal bounds I created a formula to estimate the number of winners or losers in a row I should expect to see.

The basic formula for figuring the expected maximum losing streak is:

S = ln(1/T)/ln(L) where:
L = % losers
S = Streak
T = # trades

Ex.
T = 500 trades
L = .6 or 60% losers

S = ln(1/500)/ln(.6)
S = -6.21461/-.51083
S = 12.16581 or a expected max. losing streak of 13 trades

If you increase the number of trades to 1,000 then:

S = ln(1/1000)/ln(.6)
S = -6.90776/-.51083
S = 13.52273 or a expected max. losing streak of 14 trades

confidence level is 1 - (1 / number of trades)
ex. 500 trades = 1 - ( 1/ 500) = .998 confidence level

In this case if I were to see 20 losers in a row from a test sample of say 400 trades, then I'd check other streak levels such as the number of 3 in a row or 4 in a row to see if they are also outside the bounds. If so, I'd look for dependency.

As you'll see from this series on consistent results it would very rare for me to see a 10% drawdown. As long as the model is operating with a consistent edge I would not pull it. The range of drawdowns is somewhat predicatable using the Monte Carlo tests. Those, combined with these other ideas should ensure that I'll have few and far between losing months (at least they have up until now).
 
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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No, they aren't curve fit. I've done some work on model 1 last year because it has an edge and I wanted to use that part of it for another model. The other two have been around for ages but I'm not doing anything with them because they have no edge. The results are based on 1 unit size (not 1 contract). A unit is determined by largest market volatility divided by the current market volatility so the number of contracts varies to keep the results consistent with market volatility. No money management was applied to any of this (if I did, it'd be obvious).
 
acrary,

Are your systems entering orders themselves or do they require someone to do it?

I'm working on my own system and obviously, automated order entry is a big step in terms of "letting go."

Do you see a benefit to one way or the other?

--opm8
 
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