| Greg, Thanks pointing me to Bayesian theory .......it reminds me of how my mentor Alan Andrews..........described trading to me. My programmer has decided to try Convolutional Neural Networks, in a way that allows the incorporation of Bayesian theory. In a few weeks will know more. Ron -------Original Message------- Date: 2/7/2016 6:34:33 PM Subject: Re: [amibroker] Artificial intelligence Hi Howard, Here's the general framework family: Essentially, I have a classification of any security into three states, corresponding to attractors in a phase space. These correspond to two universal memory states (different shapes) and a random walk state. The two memory states take on specific shapes governed by certain parameters. I have an inference network which can assign a confidence level to the proposition that a given security is currently in a given attractor, for each attractor, or if it is in a phase transition. I have a price series for cash which varies with the specific distribution of confidence -- the lower the confidence in any attractors or the higher the confidence in a phase transition, the higher the value of cash relative to the security. I can get an aggregate price for cash for an entire portfolio by looking across all the element-specific price-of-cash estimates. |
This is a model of the market as a dynamic system, with changes to memory being the unobservable variable which drives it.
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Posted by: "Ron J" <ronj@san.rr.com>
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