Sorry, but IMO that's a silly statement, because the system can be judged only after feeding it with new (ie. previously unknown) data. And if the system accepts only single datapoints then you can be sure that it works correct, as it has no chance to know the data in advance. It has to work on single datapoints, ie. bar by bar (or tick by tick) and give its trading decisions immediately. Then you have no reason for not to believe its result.
It is not wild random data one uses, it's either real market data or mathematically modelled data that is similar to real market data. Code: Random data would be this for example: 100, 60, 10, 90, 140, 190, 275, 15, 90, 150 Normal stock data would look like this for example: 100, 102, 99, 96, 98, 101, 104, 104, 102, 103 You just should analyse how much most of the stocks vary in their price daily, it is not much, for example just up to 2.5% up or down on a daily basis. One can compute that even exactly: it's called the "historical volatility", ie. the statistical standard deviation from the mean price. Currencies and Indices have an annual volatility of about 10% to 20%, stocks have about 20% to 40%. Of course there are also some outliers. Usually stocks of big companies are less volatile than stocks of startups and small cap companies. The higher the volatillity the more riskier it is, but higher risk can also mean higher reward if you can correctly predict the right direction.
Are you saying that you expect it to work equally excellent on any random stock? Whether it's a winner or a loser?
Of course not. I don't mean random data, I rather mean realistic data paths. Read my updated posting again. How can you believe I would mean random data? That is nonsense. Why should anybody mean random data in such a context? This is far from and beyond reality.
FYI: stock prices consist of 2 parts: the mean price plus a random component. The random component is the part from the annual volatility, for example just 1.5% up or down per day.