Strategy Development: Modelling

[ruthlessimon]

Since there was quite a bit of interest in relation to the process of strategy development (mainly modelling), I thought it was certainly worthy of its own thread. & if this receives continued interest, could become a focal point of future BA education.

I have a feeling traders are simply unaware of when to correctly use a ‘modelling’ or ‘backtesting’ methodology (when testing a strategy; especially pre-race traders). This mundane, overlooked problem, is actually a key reason for a lack of consistency & poor returns for a trader.

Shaun provided a great example in a previous thread.

A trader unaware of the results of previous shortie races, will miss vital context going into the next races. This is something that is missed when backtesting. However, a trader who understands ‘modelling’, will be able to spot the pattern, act faster & with more confidence.

To conclude, I think the majority of traders are underdeveloped in the technique of modelling. And more information on the topic could go a long way to getting users who are negative, positive. And those who are positive, exponential.

If you’re interested in learning more about modelling or have any thoughts on the technique, drop a post

[iambic_pentameter]

Whilst I am fairly happy with the way I approach 'shorties' on my "How I can become a better trader" list, modelling is on there.

Slightly off topic but still important - you need to have the patience to watch as many 'shorties' as you can, to get a feel for how the market moves and not be too disheartened if you get it wrong when you start.

Tacked on to the getting it wrong part, is getting out quickly.

[mjmorris335]

This is something I'd be very interested in. I'm one of those people that learns by doing, but if you don't know where to start...

Mike

[northbound]

Interested.

In fact, having started six months ago to trade preplay horses manually every day and NOT having become profitable yet, I’m in the process of building models to learn how certain preplay market setups traded in the past and see if I can find patterns that repeat.

Early signs are encouraging.

I gotta say that I worked as a software developer for 10+ years. Not sure how easy it would be for a non-developer to build models.

[ShaunWhite]

Yep +1 here of course.

I think it will be possible to have a chat about the basic concepts without giving away any specific edges, we can keep it fairly hypothetical without straying into exactly what we're trying to look for, or any lightbulb moments we each have. This could be interesting, but a minefield of tip-toeing around.

I'm supposed to be off today but just checked in to see what's going on, so I'll chip in more later.

[ruthlessimon]

I gotta say that I worked as a software developer for 10+ years. Not sure how easy it would be for a non-developer to build models.

That is one of the vital things that only someone such as Peter can clarify. I'm sure many of us think of modelling as an extremely complex task - which only someone with a strong physics or computer science background can handle. Yet, it's these people that will struggle with trading the most - as any sort of sentiment based market doesn't have "laws". I think modelling is far simpler than we think, Peter prides himself on not being an academic. Or maybe he does

[northbound]

I gotta say that I worked as a software developer for 10+ years. Not sure how easy it would be for a non-developer to build models.

I'm sure many of us think of modelling as an extremely complex task - which only someone with a strong physics or computer science background can handle.

Not at all.

In fact, even if I worked as a software programmer for a long time, I never went to university and know little about maths and physics. Always struggled to understand formulas.

My way of programming and modelling is all about basic logic and creativity. This is the way I approach Betfair modelling. So, not really rocket science. But still, although not complex, the process of gathering data, polishing it, writing model algorithms, testing them under different conditions, etc... it's all very time consuming.

[CallumPerry]

Very interested in learning something new. I think this could very much help me with my trading!

[northbound]

The first hurdle when building a model is how to obtain historic data to backtest your models.

Betfair historical 1-sec data costs £99/month for horse racing. Quite expensive, considering how in the financial markets there are companies (Quantopian, Quantiacs, etc) that let you write models and backtest them on many years of data, for FREE.

So, first question: how do you go about gathering past Betfair preplay horse racing data?

[Euler]

Nearly everything I have modelling I've done using the same method. I start at random then vary the strategy to see what impact it has on the results. I then to the opposite to test that my assumption on the first point was correct. I then vary again and repeat. Eventually, you build up a detailed picture of what happens and why.

I then try and describe why that is the case. If I can't then I start digging much deeper.

[ShaunWhite]

Thanks, that's useful.

I've got a question which I hope I can explain, the difference seems subtle but it's quite key....

So would you say you are ...
1. Looking for biases/truths/oddities (or however you want to describe them), in the market type in general, in a set of historical data. To arrive at a general conclusion such as X happens Y% of the time when Z condition is true. Perhaps that might be, if horses steam x% in the last y minutes then they drift z% in the final 10s, in handicaps, lefthanded. Or long duration drifts end at overvalued prices. What you'd call with no disrespect 'sweeping statements'

Or
2. Are you modelling to build a picture of the actual mechanics of a that market type. Perhaps something which says 'if 2fav is shortening and wom on fav is 80% then the price of 3fav is likely to drift...blah blah' .....like a Phillips machine if you've ever come across one, they're an Economic Analog Computer. In theory a 'Phillips' style model could be built with no historical data whatsoever, just a knowledge of how the entities interact. The biggest issue with this model is the number of different variables, internal variables such as volume and external ones such as a news feed. Bad weather on a fav that likes it rock hard would affect it's upper drift limit more than one that slightly misbehaves.

https://www.youtube.com/watch?v=rVOhYROKeu4

3983645714_379e726a40_b.jpg

#1 is basically the output from #2 run many times with different external variables and I see #1 would give you a heads-up about what might happen in certain special situations, if you can remember them all ! Absolutely has it's value.

But #2 really is the holy grail for live trading. I'm not sure you can even describe a pre-off horses in a Phillips machine style way without a brain the size of a planet...but maybe that's what we're building subconsciously when we watch 10,000 markets!


btw Tennis Trader and Soccer Mystic are so close to a Phillips machine style model.
The "Player A breaks" tap opens, "player A price" falls to x and "Player B price" rises to Z. If you add a few taps for "Player A sweatiness" and "Player B limp amount"....it would be damn near perfect


Are we saying the Strategy Development has two threads, Backtesting and Modelling, And modelling has two threads, Data Analysis and Physical Modelling?

I've strayed but my main point is about which type of modelling you mean, #1 or #2. I think you get #1 from graft but #2 is the craft.

[northbound]

My current approach is closer to your #1:

- Gather historical DATA

- Come up with MODEL ideas, basically patterns which I feel repeat often enough

- Build algorithms to BACKTEST each model, simulating entry/exit in the market and resulting profit/loss

- Keep models that backtested profitably

- Build BOTS that enter the markets automatically when one of the models' situation occurs

[PeterLe]

If all else fails, a good guess is better than a bad measure
If all else fails, a good guess is better than a bad measure

I’ve wrote that twice as I really believe in it

My best strategies have never really stemmed from hours of reviewing data (in truth I’m not that good at it anyway..
My advice would be to try and do someth8ng counter intuitive and buck the trend.
I’m working on something completely new and so different than I’ve ever done before. I have no real evidence to suggest that it will work, but my gut feeling is that it will, so what better way test it than try it?
I just wanted add a different perspective for consideration and throw that into the mix

[ShaunWhite]

Despite my interest in more formal approaches I'm pretty much in your camp PeterLe. Partly because I don't have a lovely huge set of data and partly because, I can't see me finding anything the boffins haven't seen and exploited. The feeling I've got before on the forum is that this type of edge has a habit of disappearing overnight too.

So I've been focusing much more on live trading and trying to absorb how the machine works in all its glory. My interest in this mysterious 'modeling' lingo comes from feeling I need a way to record and describe what I'm seeing. But perhaps I'm trying to be too clinical and it's best left to the fuzzy logic autopilot?

'Live trading' is pretty much what it says on the tin. What's the name given to trading that's based on models and backtesting etc? Would you call it algorithmic?

Would it true to say that generally, pre start markets suit the live trading approach, ie reactionary, and post start or final result markets benefit from the algo approach? Ie more prescriptive?

[northbound]

The problem with manual trading, for a beginner like myself, is that I’m not experienced enough YET to trust that the odds will go in a certain direction.

Peter made a brilliant video about this a while ago:
https://m.youtube.com/watch?v=v7VIxbvrmfY

That’s the main reason why I’ve turned to backtesting. To get some ststistical confirmation from past data that, for example, if I see a certain setup 3min before race start, the odds end up in a certain range by the start, 70% of the time.

This would give me the confidence to stick with a trade even though initially goes the other way. But also not panic those 30% of times I make a losing trade, because statistically there’s a good chance I’ll be in profit by the end of the week/month.

So, not modelling and backtesting for the sake of it, but to get a better understanding of the markets.