[ozi]

The problem isn't coming up with an algorithm that works (i.e. more wins than losses).

The difficulty is gaining confidence in your algo and determining when to move from paper trading to actual trading.

You run into counter-intuitive things while training a neural net, for example. You'd think more training data would be good, but when training neural nets, you actually want to use as little data as possible while still creating an ideal ROC curve.

[0xmohit]

> The problem isn't coming up with an algorithm that works (i.e. more wins than losses).

An algorithm that works would also include the ability to limit losses. An algorithm might be correct 9 out of 10 times, but may lose more in a single transaction than what it earned in those 9 winning transactions.