Quote from fundjunkie:
Data mining is a means of searching for patterns and relationships in data. A fitness function is used to highlight those parts of the data that are fit (of interest). Te term is most often used in conjunction with wide ranging searches numbering in the tens of thousands.
Neural networks are a type of data mining algorithm (regression) amongst many. But be clear data mining isn't just a case of setting a program loose on some data, sitting back and letting the good times roll with the results. it's way, way harder than that and the results and benefit when appled to the markets are debatable and far more etheral. Indeed, the consesus view is that neural networks, for example, are of no value in trying to model markets.
Without wishing to discourage you i think you'll be wasting your time by even considering that as an avenue to be looking down when you're only thinking about automation and have little to no coding background. But be mindful of it for different reasons. Data mining is a process. Data mining apps indistrialize and scale up that process. System oriented research is a process of mining on a smaller scale which brings it's own dangers of curve fitting and data mining bias (being fooled by the data). I can imagine you might produce dozens of versions of a trading concept before finding something that looks good. that is data minging...
So what you need to concern yourself with more (if results look good) is mitigating any data mining bias and avoiding curve fitting (methodological rigour).
Thx
D
p.s. I am not an expert either on data mining or data mining apps.
