https://lean.quantconnect.com/docs#topic1.html - is this the best option at the moment ?
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A proposal for testing black-box systems
- Thread starter botpro
- Start date
Sorry, but IMO that's a silly statement, because the system can be judged only after feeding it with new (ie. previously unknown) data.Most black boxes are over-fitted systems.
And if the system accepts only single datapoints then you can be sure that it works correct, as it has no chance to know the data in advance.
It has to work on single datapoints, ie. bar by bar (or tick by tick) and give its trading decisions immediately.
Then you have no reason for not to believe its result.
No black box system will work ( or is expected to work ) on any random set of data.Sorry, but IMO that's a silly statement, because the system can be judged only after feeding it with new (ie. previously unknown) data.
And if the system accepts only single datapoints then you can be sure that it works correct, as it has no chance to know the data in advance.
It has to work on single datapoints, ie. bar by bar (or tick by tick) and give its trading decisions immediately.
Then you have no reason for not to believe its result.
It is not wild random data one uses, it's either real market data or mathematically modelled data that is similar to real market data.No black box system will work ( or expected to work ) on any random set of data.
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Random data would be this for example: 100, 60, 10, 90, 140, 190, 275, 15, 90, 150
Normal stock data would look like this for example: 100, 102, 99, 96, 98, 101, 104, 104, 102, 103One can compute that even exactly: it's called the "historical volatility", ie. the statistical standard deviation from the mean price.
Currencies and Indices have an annual volatility of about 10% to 20%, stocks have about 20% to 40%. Of course there are also some outliers.
Usually stocks of big companies are less volatile than stocks of startups and small cap companies.
The higher the volatillity the more riskier it is, but higher risk can also mean higher reward if you can correctly predict the right direction.
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Are you saying that you expect it to work equally excellent on any random stock? Whether it's a winner or a loser?Can you prove what you say? ;-)
It is not wild random data one uses, it's either real market data or mathematically modelled data that is similar to real market data.
The difference is only in standard deviation.It is not wild random data one uses, it's either real market data or mathematically modelled data that is similar to real market data.
Code:Random data would be this for example: 100, 60, 10, 90, 140, 190, 275, 15, 90, 150 Normal stock data would look like this for example: 100, 102, 99, 96, 98, 101, 104, 104, 102, 103
And what do you mean by that statement?The difference is only in standard deviation.
Of course not. I don't mean random data, I rather mean realistic data paths.Are you saying that you expect it to work equally excellent on any random stock? Whether it's a winner or a loser?
Read my updated posting again.
How can you believe I would mean random data? That is nonsense. Why should anybody mean random data in such a context? This is far from and beyond reality.