I wonder how these magic numbers get picked (4.66719, 5.38375, etc) -- I guess there is some optimization solver which attempts to find the most profitable variables for a given alpha formulation, but isn't this approach also very vulnerable to overfit?
Yup, it's probably just the output of an optimizer and then tested on held-out future data. Not overfitting is the key here and what's really hard. You need to be careful about the number of parameters and the amount of validation data you have.
These alphas will likely be only profitable for a short time period as long as the market data distribution (i.e. strategies of other market participants) doesn't change. So you would need to continually optimize and update them.
The way I think about it is that you are essentially finding the right parameters to "exploit" the combination of algorithms of all other participants, where algorithm could also be a human looking at charts and following certain rules, with a lot of random noise from retail traders thrown in.
I believe they may have used something on the lines of genetic-programming to create this equation - not sure about the high precision constants. The search space is compute intensive. Many years back, I used that technique to generate a profitable strategy. These things work and are different depending on the timeframe/sampling, stock, trend and money management.
> Alpha#90: ((rank((close - ts_max(close, 4.66719)))^Ts_Rank(correlation(IndNeutralize(adv40, IndClass.subindustry), low, 5.38375), 3.21856)) * -1)
I wonder how these magic numbers get picked (4.66719, 5.38375, etc) -- I guess there is some optimization solver which attempts to find the most profitable variables for a given alpha formulation, but isn't this approach also very vulnerable to overfit?