S&P Trading Model - Quite Technical - Meet the Competition

[mrbaseball]

[ QUOTE ]
Plus - sweeping generalizations by someone not expert in advanced genetically optimized artificially intelligent modeling technologies are often incorrect.

[/ QUOTE ]

I wanted to discuss your thread last week but it got locked very quickly for no apparent reason before I could comment.

First I was interested in exactly what that crossover and oscillator were? It looked like some variation of a 3-6 MA crossover system. I was also intersted in what kind of execution you use. Meaning do you use an automatic entry system? With sub one minute bars physical entry on those signals can be trying at best with the speed of the market moves meaning if it isn't the computer itself putting in the order you miss many of them. Also that section of chart seemed tailormade for such a crossover system yet those experienced in similar methods realize there are plenty of periods where such an approach is a chop shop. How do you avoid the mincemeat when the market is whipsawing in a narrower range as it often does?

[UrmaBlume]

Your are correct about order entry, with sometimes as many as 12 bars per minute, manual execution/order entry is impossible.

The chart below is a bit slow compared to what I/my software normally trades and yet still it only shows 25 minutes and includes 14 or more plainly indentifiable trades/28 transactions.

The zero phase adaptive moving averages on price are there only for reference.

The histogram based oscillator on the bottom of the chart is based on my own proprietary micro time frame volume analysis algorithms.

As you may have already read I put little stock in indicators based on price. It is my belief that it is buying and selling volumes that predict price and not price itself. Also, micro analysis of certain volumes can predict noise as well as trend in almost every time frame.

While the chart below shows remarkable organization even at this very low time frame this is not the output from any modeling technology but merely a couple of the inputs and demonstrated here as a reference.

Of course MA systems suffer greatly in noisy periods so one of the first responsibilities of any effective model is to recognize periods of noise and either avoid them or take advantage of them depending on the model's utilization.