Security price time series clearly are causal. Prices are sufficiently correlated over all time frames to encourage the trader to act as if they are not random. Over day trading time frames both their signals and their noise are sufficiently statistically stationary to apply classical tools. The associated volume time series suggest a weakly non-linear first order differential equation relationship with price which is exploitable. The latencies and inaccuracies in internet reporting of price and volume are well within tolerable limits on day trading time intervals. The only serious issue is that third and higher order derivatives of price with time are significant. Therefore virtually any modern estimation theory approach should provide a viable approach to trading. For example, statistical estimation, Laplace frequency domain filtering, and Kalman filtering should work. But such approaches suffer from computational issues on PCs for real-time trading. Therefore my solution is to fall back on classical analog servomechanism theory. This is an appropriate tool for quasi-stationary statistics and for uncertain dynamic causalities. And it is an appropriate mental modality to work in because the objective of trading is to have the trader follow the market mechanically, objectively, and without passion. So I treat price as the raw signal and design feedback outputs to drive the trader to the desired state. Attached is an example system designed to identify and ride potential all-day holds in NQ on a five-minute time frame. Green in the helper pane is a long signal. Red is a short signal. Orange means to stand aside. The chart shows an example full day in U.S. West Coast time. Of course this was chosen because it worked so well, but the result is not unusual. The method is scalable to longer time frames, but is highly problematic for charting faster than 30 seconds. Later I will show intraday trades and the system I use for investing.

You are a fine one to speak of hypocorism, OddTrader! But as to complexity, I have no doubt that one of the earliest control systems (the automatic toilet tank) merited a dissertation in its day. In truth, what I do is not much more complex than a cruise control or a washing machine. For those who understand these things, it is merely an adaptive closed loop low bandpass position controller. The art is in the tuning and the noise filtering, not in the architecture.

I forgot to invite anyone who uses any related estimation theory method to post affimatively. I will from time to time post illustative trade examples. And BTW, like all really good systems, this one takes less than a half-page of code and is utterly unambiguous in its calls.

Based on no experience at all whatsoever, I think you are most likely in the right direction leading to nowhere about profitable trading philosophy. Keep going anyway, my friend! BTW, may I borrow some of your nicknames, in case of needs? No hurry!

The attached one day chart for the past year suggests the applicability of the method to investing, in this case for a fund which mimics the NASDAQ 100. All that is changed from the previously shown five-minute chart is the time-scale-sensitive adaptive constants. The gradual worsening at the end of last year suggested that you get into cash, as I did. I am by no means asserting that this is a perfect method, but in the case of long term investing, it is at least a quantitative method that would have helped you overcome any lingering affection for the market at that time.

LOL, the usual ET crap. Boy, a day without you and Trader28 is like a day where my stops don't get hit ...