S&P 500 stock selection using machine learning classifiers: A lookinto the changing role of factors

[Jrinne]

Whycliffe's,

TL;DR: With the API you will be able to sort predictions to create ranks and also be able to output (e.g., print) the predicted returns for dynamic weighting. In addition, you can use this any discretionary decisions (or algorithmic decision) as to whether to buy or sell a stock. More precise slippage calculations could be added to any discretionary or algorithmic decision-processes. Your algorithms for making trades and weighting holdings would improve with time--based on learning from previous results--and would not have to be perfect to begin with.

I think P123 will sort the predicted returns and create a rank although I am not aware that they have confirmed this. I think you will end up using this rank exactly as you have used ranks before if you use P123's upcoming AI/ML alone.

@Whycliffes

How do you choose the weight in each stock

So you are not the first to see that using ranks is not the only way to make buy/sell decisions or that the predictions could be used to determine the weight of holdings:

@yuvaltaylor

Fascinating. If the predicted returns are based on some ML algorithm, why not use them instead of ranking?

This is probably already being done by someone using the API as it is a pretty obvious thing to do. I do not know what Pitmaster is actually doing but he obviously thought about using machine learning to determine the weight of his holdings. Now you have have the idea too--as did I and probably many others before us. Maybe Yuval has considered multiple potential uses for predictions (other than replacing ranks)—although they may not include dynamic weighting:

@pitmaster

I download data from p123, using 'Download Factors' or API, train model using python, and download most recent data again when I want to infer predictions from the model. After the model in trained, everything is automated - one click and I can see top 20 stocks. I can create dynamic weights, or use more complex transaction cost estimate.

I don't expect to see this with P123's first release of AI/ML I think you will have to continue your research into the API to implement any ideas of using the predicted returns for weighting the holdings in the near future.

But that would NOT BE HARD. You should be able to know the model and hyperparameters of a model you have trained using the P123 AI/ML. You could use this exact same model in Python using data from a download of data using the data download tool and then create predictions of returns with each rebalance using the API. You could use a spreadsheet (or Python) to determine the weights (and even the slippage) of the new purchases. Maybe doing most of it with manual transactions once you have the data and calculations.

Maybe P123 will provide the predicted returns (and maybe a calculation of the slippage at some point) with the rebalances so you that you can skip the need for the API if you want to use machine learning to determine the weight of your holdings (or whether to buy or sell a ticker in the first place).

Assuming that is not a lot more expensive to do rebalance use P123 to rebalance this it is using the API alone to rebalance.

The predicted returns are in memory somewhere (on the processor or on a hard drive) at some point and could, perhaps, be retrieved to be printed with the P123 rebalance—making using the API for dynamic weighting unnecessary . Maybe that is already in the works.

Jim