[Feature Request] Concise Factor-Based Algorithms

Expected Behavior

We can simply calculate factor scores for each asset in a universe, where LEAN manages the required dataset and rebalancing for us.

class FactorAlgorithm(QCAlgorithm):
    
    def __init__(self):
        universe_settings.resolution = Resolution.DAILY
        universe = self.add_universe(
            lambda fundamentals: [f.symbol for f in sorted(fundamentals, key=lambda f: f.market_cap)[-10:]]
        )
        universe.add_factors([InverseCorrelation(252, Field.OPEN)])


class InverseCorrelation(CrossAssetFactor):

    def update(self, data):
        factor_value_by_symbol = 1/data.dropna(axis=1).corr().abs().sum()
        return factor_value_by_symbol / factor_value_by_symbol.sum()

Actual Behavior

Not currently supported

Potential Solution

N/A

Reproducing the Problem

System Information

Checklist

• I have completely filled out this template
• I have confirmed that this issue exists on the current master branch
• I have confirmed that this is not a duplicate issue by searching issues

• I have provided detailed steps to reproduce the issue

Start by reading the QCAlgorithm, add_universe, add_factors, and CrossAssetFactor entry points shown in the example, then compare them with the current factor-calculation support. Done means users can calculate and normalize factor scores across a universe while LEAN manages the dataset and rebalancing.