My ML knowledge is somewhat rusty... does overfitting occur more often on models with many input parameters (ie.. neural networks).
His algorithm seems very simple, without really using ML at all, it's more of just a procedural 1-2-3 step thing, with no actual learning.
Can you explain how overfitting works into his algorithm?
On {day/week/month} n, determine which {n} product(s) of {product brand}'s {product type} of {underlying asset type} gave the {highest/lowest/some of each} return.
At n + {a number} {days/weeks/months} go {short/long/some of each} at {market price/limit price} at {market open/market close/time in day} the previously identified securities. Close your position on day n + {a number} afterward at {market price/limit price} at {market open/market close/time of day}.
This particular attempt seemed to have succeeded. But how many were tried that didn't? If you torture the data long enough it will confess.
I specifically asked how overfitting applies to a simple procedural technique, rather than a multi-dimensional method like a neural network.
Trying myself, and losing money, doesn't explain how overfitting applies to procedural steps (as I said, my ML is rusty)
For momentum, Jegadeesh-Titman paper is much of the foundation for these types of strategies, if you're interested. But as others have pointed out, the "smart money" saturated this trade decades ago.
> Implementing this on any kind of scale would be expensive to trade since it rebalance's daily.