I was able to find the optimal values for those variables by doing side by side comparisons of results achieved with varying values. This wasnt to hard to implement but it did mean that if I was curve fitting multiple indicators at the same time I had to be careful; changing one would effect the predictions of another. The variables used in this step were all subject to optimization. But how can an algorithm identify these areas? Otherwise you might be testing for a long time. Coming up next: Machine Learning Gone Wild - Using the code! Seeing an offer did not guarantee that I could buy. In the beginning I did this curve fitting manually but I soon wrote up some code to automate this process. (My algorithm treated up and down exactly the same.). My algorithm would need to come up with this prediction moment-by-moment throughout the trading day. After you have your set of data you need to read them and clean them.
The idea is that this algorithm will let me partition my data (forex ticks) into areas and then I can use the "edges" as support and resistance lines. This was done in the exact same way as I optimized variables in the price move indicators except in this case I was optimizing for bottom line. Ladies and gents (and robots let me introduce you. These included one level above the inside bid (for a buy order) and one level below the inside offer (for a sell order). I found this struck the right balance between capturing recent market behavioral trends and insuring my algorithm had enough data to establish meaningful patterns. Being successful meant being fast, being disciplined, and having a good intuitive pattern recognition abilities. With each indicator now giving us its additional price prediction I could simply add them up to produce a single prediction of where the market would be in 10 seconds. I can only describe what I was doing as akin to playing a video game / gambling with a supposed edge.