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No my general point is not that more data is relevant--like I said, a statistically relevant amount of data is what is need. They are two very different things and should not be confused. It may seem like one and the same, but trust me in the world of statistics they are chalk and cheese.
MA has the view that all data is the equal so that :Your general point is that more data is better than less data. I am saying that that is simply not the case. We agree that specific samples of data are required.
jog on
duc
We all remember the black swan zillion of lifetime probability not to happen..which happened in the last crash.quite interesting article at the time so always remember stats can tell you whatever you want them to say ..based on the data selection...Statistical confidence increases with the number of observations of the variable under observation, which is statistically relevant data, to which I am referring. I am not advocating adding in data re. hemlines etc.
So let us get a little less abstract and more specific.
Take Mr Skate's various systems: these have been designed with the 'exit' as the (or one of the prime) variables. The premise being: money is made on the exit.
Now, return to last year when the markets crashed. Did the exits save Mr Skate? To a point, yes, he still exited the market holding profits. But essentially the profits were decimated.
How did other mechanical systems hold up last year? They didn't.
Now they were backtested, and all gave the illusion of having a max drawdown of 'X'. They were all wrong. They were all wrong because of the very issue highlighted.
If, they had 'recognised' the signs as they were building, they would have exited much earlier. That is the value of studying, backtesting, whatever, the outliers that we have had far more frequently than the relevant statistical data has forecast in terms of probabilities.
jog on
duc
Now they were backtested, and all gave the illusion of having a max drawdown of 'X'. They were all wrong. They were all wrong because of the very issue highlighted.
I'm kind of saying that. What I saying is that if you test and refine your system over a period 1990 to 1995 and your then perform out of sample testing (not optimizing of system parameters) over the discrete period of 1996-2000, 2001-2005, 2006-2010, 2011-2015, 2016-2020 and all sim results across those years yield acceptable results then I would say your system appears to work well on future data. That is why a say old data (in this case 1990 to 1995) is not irrelevant. Maybe it isn't always the case but to discount old data outright just because times were different might be doing you a disservice.MA has the view that all data is the equal so that :
what was true a generation ago is still true.
I would say
Yes and no, true as to human psyche, false as to its implementation and so day to day influence on the market
This is where we differ, and as a consequence i am in the view that "too old" zillion data testing is useless.
But yes if you have relevant data, the more the better.we all agree
I will have to go but.I'm kind of saying that. What I saying is that if you test and refine your system over a period 1990 to 1995 and your then perform out of sample testing (not optimizing of system parameters) over the discrete period of 1996-2000, 2001-2005, 2006-2010, 2011-2015, 2016-2020 and all sim results across those years yield acceptable results then I would say you system appear to work well of future data. That is why a say old data (in this case 1990 to 1995) is not irrelevant. Maybe it isn't always the case but to discount old data outright just because times were different might be doing you a disservice.
I like having a respectful disagreement...after all if everyone traded the same we'd have no market to tradeI will have to go but.
If you do so, can I genuinely ask: do you use price as a realm selection? for example to filter out 2c stocks etc..?
If you do then how do you reconcile the fact a 2c worth in 1992 is probably a 5c worth or more today?
Sure possible to use a cpi indexed price as a tgreshold but outside the beginner coding usage.
So i just want beginners to think about what their code backtest is supposedly testing with 30y old data.
Nice discussion.i agree we disagree?
One trap about out-of-sample testing that's easy to fall into is repeatedly using the same data sets. You test your system on in-sample data, looks great. You test it on out-of-sample data, it falls apart. What do you do? You go back to the system and make some adjustments. Test it again on the same data and it looks much better this time. But really, you've just made your out-of-sample data part of the in-sample data set. You can't keep making adjustments until the same out-of-sample test comes good.The other thing that a lot of people don't do is out of sample testing. It's one thing to refine and tune your system over certain data, but you better test that on data that you didn't test and refine your system on. In other words, this will give you some insight in your systems ability to perform on future data. This is one of the biggest oversights a lot of system traders make.
If you do proper MC analysis, and I'm not talking about that crappy MC testing in Amibroker, but proper statistical analysis you will have a far more insightful understanding of what to expect from your system.
1. I am glad you have highlighted this, but it illustrates one of the major issues I have with a lot of people that trade mechanical systems and not the systems per se. I say I have a major issue with the people deliberately but a lot of people trading mechanical systems do not have sufficient understanding of how to properly analysis system that deal with time series discrete data.
2. Most people will do a single backtest across 20 years of data and to further add to the system they will over optimize (or curve fit). They get a fantastic looking system that averages a CAGR of 35% and a drawdown of 20%. Guess what, then they jump straight into live trading that system and as you allude to they encounter a tough market and experience a 35% drawdown and maybe after a few years they're at a CAGR of 8%.
3. Well based on your comment I'm guessing you would blame the system--but I don't because that person didn't comprehensively test that system to full understand the systems bounds and perhaps that 8% CAGR and 35% DD was well within the systems behavior. Anyone that runs a single simulation of 20 years of data and then goes live is seriously asking for trouble but I don't blame the system.
4. If you do proper MC analysis, and I'm not talking about that crappy MC testing in Amibroker, but proper statistical analysis you will have a far more insightful understanding of what to expect from your system. The reality is that a system will have a range within which key parameters will fall if correctly tested. Proper MC analysis (not Amibroker MC rubbish) will yield a distribution (or range) within which you can expect your system to perform. For example, proper statistical analysis will reveal that the DD will have a mean of 18% with a certain SD from the mean at 95% confidence interval. This applies to any system parameter you wish to track. In other words, proper statistical analysis will give you a mean and what you can expect in terms of extremes from that mean.
5. The other thing that a lot of people don't do is out of sample testing. It's one thing to refine and tune your system over certain data, but you better test that on data that you didn't test and refine your system on. In other words, this will give you some insight in your systems ability to perform on future data. This is one of the biggest oversights a lot of system traders make.
6. Anyway, the point I'm making is that in the scenario you provide--I don't blame the system I blame the person for not having engaged in proper MC testing of their system.
Hey Quanta,Hey Guys,
Quick question MovingAverage, when you state:
How do you go about this? Do you use Amibroker and import your results into Excel for analysis?
I'm on the learning curve, most of the stuff discussed to test robustness I do as it just made sense, next step is expanding data analysis.
Thanks in advance.
This should NOT be a big dealIf you're comfortable with AFL then this should be a big deal.
FixedThis should NOT be a big deal.
6. And this should be rectified.
jog on
duc
A mechanical system is an automated strategy. Certain strategies will do well in certain market environments and potentially outperform their initial code. Markets change. Sometimes that change is subtle and the system still returns a profit, sometimes it is radically different and a period of underperformance will ensue. Sometimes the monkey wrench gets tossed in and s**t blows up. Now you could have multiple systems (strategies) and chop and change them as market conditions change. That however is 'discretionary', which (possibly) systems traders decry?
The issue that I have with mechanical systems and I suppose by extension the people that design them is that they, for the most part, seem to be blind to the intrinsic flaw that lies at the heart of mechanical based trading. Certain strategies will do well in certain market environments and potentially outperform their initial code.
Possibly Mr Skate can expand on this criteria and whether the scans are or could be (made) to be discriminating enough.
Back to trading
Like most "trading became an obsession" after a period of time. Returns from trading started to become the scorecard. The measures of personal development as the results provided the motivation to keep going & the motivation to improve.
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