Most automated trading systems have a number of embedded parameters such as the lookback periods, entry and exit thresholds, etc. This is like the moving average crossover system or any of the systems that rely on some kind of data window for calculations. For example, if I use a fast and slow exponential filter for an MA crossover system, then I need to figure out the best time values for each of these filters.
Finding these parameters can be difficult because there's only one history from the traded security. A single currency might have 200 million ticks or 2 million 1 minute data points. This is only one scenario of what could have happened and represents multiple trends and turning points in an evolving series. If I want to really pick parameters that would be best, it seems like I would want to use multiple samples to reduce overfitting. It's possible to use hold out data, but it seems like it would be better to use bootstrapping to get additional histories to optimize on.
Is there a problem using block, moving block or other bootstrap methods to find the optimal trading parameters or blackbox parameters? Seems like a good idea. What are the most effective bootstrap methods for nonstationary, evolving dependent time series?
Thanks in advance