At this point what I’ve developed is a prototype for a strategy curve fitting detector and thus far the technical indicators I’ve tried all just fit the curve of the price history. Basically I’m finding that while a backtest for a strategy utilizing an indicator with a given set of parameters for the indicator may show triple digit annualized returns, if the first year of results are compared to all of the time after the first year, there is very little correlation between the first year’s results and the following years results, such that if you were actually sitting back at the end of that first year, you would not be able to pick the parameter set for strategy based on the results it produced. If it couldn’t be done then, then its reasonable to assume it can’t be done now either. So no viable strategy, but at least now I’ve got a good way to reject strategies quickly (relatively speaking it still may take hours of CPU time per symbol depending on how big the parameter space is for the strategy).
Now I need to clean-up the mess of code I wrote getting to this point. First I want to create a plug-in to Personal Stock Screener that will dump historical daily and intraday data into a CSV flat file, which can then be used as input for a console app that will load the strategy from a DLL, so I can invalidate strategies in a process different then PSS, although I’ll still use PSS to update the flat files daily. I also need to get Sierra Charts as with the right subscription level it provides 6 months of historical intraday data. If that isn’t enough, then I’ll have to buy it from one of the few providers on a per symbol per month basis.
Before discounting technical indicators and moving on to Elliot waves, Fibonacci sequences, fractals, and perhaps neural nets I want to run them over intraday data as I got some very interesting results with an assumption that I couldn’t validate as correct with daily data, but will be able to validate or invalidate with intraday data.