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Taking a Trading System Live


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Updated January 6th 2016 by tturner86
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Taking a Trading System Live

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  #111 (permalink)
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Very Interesting, thanks for doing that.


A couple of questions:

1. From your experience, is there a cutoff for the number of trades before you find it better to try a fitted model? You mention using discrete with mine is OK with 600+ trades, but if I had only 50 trades it would not be.


2. Note that due to my stop loss, my distribution of trades has a spike at (edit: -$885) or so. How do you account for this when doing a fitted model? A fitted model would likely take a way this spike, and that would be bad, since it is present (and so large) specifically because of the stop loss. Would making 2 distributions make any sense?


Your work to this point is definitely appreciated!

Kevin

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  #112 (permalink)
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Quoting 
1. From your experience, is there a cutoff for the number of trades before you find it better to try a fitted model? You mention using discrete with mine is OK with 600+ trades, but if I had only 50 trades it would not be.

That's tough to answer. It was a visual feeling. When you draw 50 trades from your data set and assume you don't have the whole data set, just the 50 trades. If you then plot for these 50 trades only the discrete distribution, then you can see that too much information is missing to do a reliable simulation. That's why I feel the need to make assumptions about the missing part of the distribution.

Now, for example if you are trading NGEC for several years, and you have then done let's say 2000 trades and plot them, then you'll will see, that the distribution chart of the 2000 trades won't differ much from that of 614 trades. It is just smoother, the shape is pretty much the same.

Currently I cannot provide a number that says you must have at least x trades, if not do a simulation with a fitted distribution model. Still have to do many tests on my side. I also think, much will depend on the strategy itself.


Quoting 
2. Note that due to my stop loss, my distribution of trades has a spike at -$450 or so. How do you account for this when doing a fitted model? A fitted model would likely take a way this spike, and that would be bad, since it is present (and so large) specifically because of the stop loss. Would making 2 distributions make any sense?

I see in your Data set, that 885 is your biggest lost, so I assume that trade consists of 2 contracts. You can see that lost in the chart below. It is at the most left of the blue distribution.


As you can see, fitting the data to Logistic distribution (red line), the spike is not removed. Indeed it can occure again, and even greater lost than that. Somehow this also reflects the reality, because there are events like black swans or other reasons, that you will exceed your loss despite your stopp loss. But the probaility exceeding lost of 1000 USD is very small, as you can see in the chart.

If you feel that this approach is not right, you can use other distribution models, where maximum and minimum profit or loss of each trade is limited. Actually, there is no limit to distribution models, you can build your own taking every details of your strategy into account. (For example there are only two outcomes: either 10$ lost or 15$ win)

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  #113 (permalink)
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Thanks for correcting my error : I said $425 max loss, but it should be $885.

That is good to know that you can tailor the distributions beyond a simple curve. I would bet that increases the accuracy a great deal, since it provides a better fit.

Good stuff!

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  #114 (permalink)
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swz168 View Post

Above you already know, it is a discrete simulation.

To Complete: Here are the results using a fitted logistic distribution for 100 Trades (with 10.000 Iterations)

Minimum: -2.359
Maximum: 32.038
25%. Perc.: 12.420
50%. Perc.: 15.265
75%. Perc.: 18.173

5% Perc.: 8.279
1% Perc.: 5.126

VaR-DD (95%): 45,9% (Standardtest)
VaR-DD (99%): 66,5% (Stresstest)

How to interprete these values and how to compare them with results from different methods, it is up to you, because you know the specific of your strategy.

With a fitted distribution described by mathematical functions, the simulations is quite fast. Above calculation took less than 30 seconds (first generation of i7-CPU). With discrete distribution it took about 3-5 minutes (maybe it is my fault due to inefficient implementation).

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  #115 (permalink)
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swz168 View Post
Above you already know, it is a discrete simulation.

To Complete: Here are the results using a fitted logistic distribution for 100 Trades (with 10.000 Iterations)

Minimum: -2.359
Maximum: 32.038
25%. Perc.: 12.420
50%. Perc.: 15.265
75%. Perc.: 18.173

5% Perc.: 8.279
1% Perc.: 5.126

VaR-DD (95%): 45,9% (Standardtest)
VaR-DD (99%): 66,5% (Stresstest)

How to interprete these values and how to compare them with results from different methods, it is up to you, because you know the specific of your strategy.

With a fitted distribution described by mathematical functions, the simulations is quite fast. Above calculation took less than 30 seconds (first generation of i7-CPU). With discrete distribution it took about 3-5 minutes (maybe it is my fault due to inefficient implementation).

Just so I am clear, is your 614 trades case (column 2) have $10K start equity, and then consists of 10,000 simulations of 614 trades each? Also, do you have a "quit" point - if the equity falls below $X, trading ceases?

Thanks!

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  #116 (permalink)
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kevinkdog View Post
Just so I am clear, is your 614 trades case (column 2) have $10K start equity, and then consists of 10,000 simulations of 614 trades each? Also, do you have a "quit" point - if the equity falls below $X, trading ceases?

Thanks!

Correct. I don't have build in any constraints. As for Monte-Carlo-Simulation, I wouldn't want to build in a quit point., because then you say for yourself stop trading if you loose for example 5000USD. That means you limit your risk. But I want to see the the real theoretical risk of the strategy. With limits, you don't see it. It is biased to better results or less risk due to "interferring". If the simulation shows that the risk exceeds in many cases your "quit point risk" then you know, that you either have set your limit too low or the strategy doesn't fit your risk appetite. With limits you won't see this.

Edit: Of course in real trading, you should limit your risk. But it should be in "harmony" with your strategies characteristik.

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  #117 (permalink)
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@kevinkdog

You are using WFO. In real trading, in which intervall (monthly, half yearly, yearly etc.) do you change and optimize your input variables for real trading? How do you determine the intervalls?

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  #118 (permalink)
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swz168 View Post
@kevinkdog

You are using WFO. In real trading, in which intervall (monthly, half yearly, yearly etc.) do you change and optimize your input variables for real trading? How do you determine the intervalls?


One thing I have noticed with WFO is that a lot of times ANY combination of IN/OUT periods will work. Most times, though, I tend to like to keep them to simple intervals (ie 4 years in, 1 year out). I'll use a varoety of tools to determine my IN/OUT (it is generally different for different strategies).

I generally like 3 months and up for re-optimizations, because reopting every month is a chore. Some people I know reopt every day (I don't know how successful they are).

For those of you confused by my terminology:


IN = your optimization period

OUT = the amount of time you keep parameters, before reoptimizing

For example:

IN/OUT = 252/63 = you optimize over the last 252 days of data, and you re-optimize every 63 days.


One caution: don't optimize IN/OUT periods without checking it on an out of sample period. IN/OUT parameters can be optimized like anything else, with the same bad consequences.

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  #119 (permalink)
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Value of Monte Carlo Simulation?

I am still looking how to incorporate results of Monte Carlo simulation in my trading. To me simplest way forecasting risk is to calculate average trade and standard deviation of the trade. Using these results for one of my strategy I have compared Monte Carlo Simulation with Normal Distribution Simulation(based on mean and stddev) and boundaries ( mean, mean+stddev,mean-stddev). They look very similar, predicted risk almost the same.

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  #120 (permalink)
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record100 View Post
I am still looking how to incorporate results of Monte Carlo simulation in my trading. To me simplest way forecasting risk is to calculate average trade and standard deviation of the trade. Using these results for one of my strategy I have compared Monte Carlo Simulation with Normal Distribution Simulation(based on mean and stddev) and boundaries ( mean, mean+stddev,mean-stddev). They look very similar, predicted risk almost the same.

Can you explain your chart a bit more? I don't quite understand:

1. why your average line is zero (edit: OK, it is not, it is just small)

2. what the Total Monte Carlo line represents

3. your SimTrade are your actual simulated results, correct?

4. For the std dev lines, I believe it is not a linear function (mean + X * stdev) where X is the number of trades. It should be square root of X. Unless your calculating something else.


Thanks for sharing!

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