In today’s article, I will show you, step by step, how effective working with Market Internals can lead to a reduction reduction of up to 70%, with just seven minutes of work (on a normal PC, using a laptop it would take a little longer). weather). Let’s go for it.
In this example, we will work with an extremely simple idea, which is to buy EMD.D (15-minute chart), when the first bar closes above yesterday’s close (that is, breakout at 8:45 trading time or more. well at 3:45 pm local time for me in Spain).
This simple idea doesn’t look bad at all; It appears to have decent potential, although there is still a long way to go to achieve a complete and successful system.
There is definitely a need to decrease the number of trades, filter out the “bad” ones, increase the average profit per trade, and most importantly, reduce the drawdown substantially. It is not even important to present complete statistics: the first look at equity already shows what we are talking about. And how do we achieve such improvement? That is exactly the job of Market Internals!
First of all, I have put the above code in my Market Internals smart code and have prepared a special box / workspace for that purpose.
This smart MI code contains some of my own MI conditions (it took me 6 months to put them all together) and now I let TradeStation execute all these conditions and let it find the most suitable one. To avoid the danger of over-optimization from the outset, we must apply this process to 70% of the data within the sample and keep the remaining 30% as out-of-sample data.
Now we run the optimization, which will take about 2 minutes on an average PC (about 6 minutes on a laptop).
As soon as the optimization is complete, I organize the sample data using the fitness function. In this case, it was a TS index.
Now is the time to choose just one of the TOP solutions. Most of the time there is more than one usable solution. One that we can find roughly in the top 5-10 of the best results (this is not a classic system optimization, but rather a search for the most suitable switches, i.e. internal market conditions), there are many of these terms. in smart code therefore more than one can work really well). In this case, the solution that I liked the most this time appeared in row number two. Therefore, I will choose this solution and take a look at the sample data.
The result looks great, so I’ll check it on the Out of Sample data.
Everything looks good here too, so I’ll quickly check the overall fairness.
A look at general fairness tells me that OOS is not much different from IS. This means that everything is perfectly fine. Of course, you could run more robustness tests, etc. – but that depends on each and every merchant individually.
The whole procedure took less than 7 minutes, and I’m done.
Below are the results AFTER I applied the MI condition.
The reduction improved by 69%, the NP / DD ratio improved by 120%, the AVG Trade improvement was 65% – what more could you want after only 7 minutes of work?
This is just another demo of the app and the power of Market Internals.