I've been playing with some models in matlab that I've been developing over the past few weeks based on high frequency data I've been recording... roughly 1gb/day on disk for each symbol/day recording all depth quotes and trades. No depth in my experiments so far but will be getting into that shortly. My datasource is BRUT and INET executions.. will be adding nasdaq TotalView next month. High frequency stat arb is what I'm after....deviations from historical correlations lasting only a few minutes.. short one long the other.. take a loss on the stop on one side and let the the other ride moving the trailing stop accordingly based on high frequency realized volatility estimates/projections so it doesn't get stopped out due to random flucuations. Attached is a screenshot of the 1-hr moving correlation for QQQQ/SPY (chosen somewhat randomly because I like ETFS, to hell with futures.. 0.25 tick increments are lame). on 2005-10-12. Calculations of moving averages are 5th order iterated EMAs. Moving correlation is calculating via moving standardization using textbook inhomogeneous high-frequency stuff(Olsen, etc) correlation is more predictable than direction.. does it matter where it moves as long as they revert to the longterm correlation? X-axis is hours since midnight CST.