Here is why:
- There were transactions costs, low enough for about 40 transactions, but still were.
- There were execution price slippages - sometimes it's hard to execute order at given price
- Some initial errors in systems gave me some loses too (about 10% of initial capital).
- I've made cross-validation procedure, but testing data set was too small to get good statistical meaning.
- Tested gain values were low (but still positive) and comparable to values without losses caused by "external" errors.
Statistics showed that the system was performing better in training period, and worse in testing and then in real account execution period. Underlying model was extremely simple and had above 100 transactions per year to avoid perfect fitting to one or few best price movements. But that is not excluding potential over-fitting on some kind of higher level longer horizon component of price movements, like up/down trend during sub-periods.
Results are definitely worse than gains from risk free return at a bank account. But it was relatively low cost exercise and I have learnt something new about practical details of investing, trading systems and statistical validation. Maybe next approach would be better.
So, I'm doing research on new simple model with higher gains/lower drown downs in test set. This time I have to make better validation tests to be more sure that system has generalization capability, and in future it could keep assumed risk/gain. That means more testing data and implementing better search procedure.