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In immediately’s article, I wish to share with you one fascinating research, which can reply to you one vital query – what number of optimization inputs are acceptable for a method and what are the boundaries we must always not cross? Let’s check out it.
The research is predicated on a wonderful work of my colleague in our hedge fund, who takes care of an automatic technique improvement course of, the database and performing of analytical duties on this database. Let’s first check out the methodology how was this research carried out.
In our hedge fund, there may be at present over 700 methods, most of them on futures markets – intraday and swing ones. All of those methods have already handed our checks, so all of them meet our minimal high quality necessities and are usable for dwell buying and selling.
That is the place all of the work would not finish, however the place all of it begins. All our methods are repeatedly monitored and up to date, so we obtain new vital details about all methods and their efficiency. So we’ve obtainable not solely the 3-month interval of further out of pattern, however even the true out of pattern – i.e. knowledge that have not even existed when the technique was developed. This provides us distinctive risk to watch the true OOS efficiency and evaluate it to the earlier efficiency.
Additional on, as a part of our workflow we’ve created an index, that displays the true OOS efficiency to all earlier knowledge we had obtainable (due to incredible work of my colleague) and this index (which is sadly personal and I will not share any extra details about it) helps us to research what elements affect the true OOS efficiency. Right this moment we analyze actually large amount of various features and parts (plus we use Python Jupiter).
One in all these research was carried out to be able to present us what’s the relationship between the OOS efficiency and the variety of optimization inputs.
And that is the research I wish to share with you immediately.
The outcomes are actually easy for interpretation – merely, the upper avgDhidx worth, the higher is the efficiency of methods with given variety of parameters. Now we have in contrast 1-7 enter parameters (none of our methods have greater than that) and listed below are the outcomes:
Enter Parameters: Index Dhidx
1: 4.38
2: 33.36
3: 36.30
4: 0.38
5: 32.40
6: 43.26
7: -2.67
Though the research shouldn’t be excellent as but, as we do not have sufficient samples for sure variants (we’re growing new methods daily, so the pattern measurement is repeatedly rising), it’s doable to state some normal outcomes:
- Plainly having greater than 6 optimization inputs is harmful.
- It’s the solely stage in our take a look at that has detrimental index worth.
- The vary from 2 to six appears affordable (quantity 4 is a sure anomaly, which we have to examine additional afterward).
- Having only one optimization appears not ample, which is comprehensible when trying on the complexity of markets in lately.
- If you wish to hold the variety of optimization inputs low (for instance as a result of variety of iterations), it’s higher to decide on 2-3 optimization inputs.
For me personally, crucial is to see and to verify, whether or not a method with fewer optimization inputs, is extra sturdy. Our research hasn’t confirmed that. You’ll be able to have even 5-6 optimization inputs and so long as the technique passes all our robustness checks, it may be as sturdy because the technique which has 2-3 optimization inputs.
Completely satisfied Buying and selling!
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Source by Tomas Nesnidal