Machine learning in trading: theory, models, practice and algo-trading - page 246
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1)We train a neural network with N inputs on some area (input data cloud) in N-dimensional space. Neural network has no idea what to do with data that lie outside this cloud. But we feed it with this data and expect it to produce some result.
2) Dear experts, is there any mention of such things in the MO literature and does it seem useful to you? Comments are welcome.
1) this is called retraining, how to solve this problem is described in every literature on networks
2) I am NOT an expert, but I know how experts do it... it is better to immediately replace the price function with something else but very approximate with more suitable for you characteristics ... this is called a function approximation.
2) I am NOT an expert, but I know how experts do it... it is better to immediately replace the price function with something else but very approximate with more suitable for you characteristics ... this is called a function approximation
Yes, by the way, even 0.5 is already very good.
0.5 is random, it's a plum, even I know, so what's good here?
Are you sure? It's a flop at roulette. If at probability 0.5 you win 3-4 units, and lose one - it's a flop?
Read this forum, many profitable strategies work fine with probability 0.5.
FYI, in poker 1/6 and even 1/9 odds are good to win.)
Is it even possible to calculate that some time series can be predicted in any way or not?
Are you sure? It's a flop at roulette. If at probability 0.5 you win 3-4 units, and lose one - it's a flop?
Read this forum, many profitable strategies work fine with 0.5 probability.
Is it even possible to calculate that some time series can be predicted in any way or not?
With 0.5 probability you'll average zero if you don't count transaction costs. 3-4 against one is about 70% (0.7) There are no winning strategies with 0.5 in principle.
You are confusing the market with roulette or flipping a coin. The 0.5 probability is the probability of a correct entry, not the profit/loss ratio.