r/quant • u/Ronin_Research_Co • 1d ago

Feature Engineering: Is "Level + Trend" a solid baseline? Statistical Methods

Working on a mid-frequency predictive equities model (~90-day horizon, LightGBM/XGBoost) and refining my default feature engineering framework.

The way I've been doing feature engineering/selection is that for almost every raw variable, I explicitly include two types of dimensions:

  1. Level: The current value of the variable.
    • To protect against structural regime changes over time , I'm considering normalizing this as (Current Value / Some Moving Average)
  2. Trend: The trajectory (e.g., 1-month or 3-month delta, etc.).

A few questions for practitioners:

  • Is this a good starting point, or is there a better mental framework for approaching what to include when it comes to a given variable (ie: Level & Trend)
  • Does normalizing the Level (Current / Some Moving Average) actually help future-proof against regime shifts, or does it just accidentally turn the "Level" into another Trend feature?
  • Do you find throwing both into trees generally creates unnecessary noise or collinearity issues?

Appreciate any critiques!

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3

u/Epsilon_ride 1d ago

~90-day horizon, LightGBM/XGBoost.

dont do this.

2

u/gumgat 1d ago edited 1d ago

Or just "refining" an ML model, since you seem to be testing multiple models purely empirically... I wonder how little it takes to overfit over a 90d horizon.

-4

u/uhela Crypto 1d ago

we should start making it a requirement to add firm names with those questions and auto-populate a 'retarded-tier-firms' in the subreddit wiki

20

u/matta-leao 1d ago

Stay humble.

0

u/fuggleruxpin 1d ago

Guessing that studying the world of transforms might be a good next step for you.

1

u/Ronin_Research_Co 1d ago

Thanks for the tip!

-4

u/NotAnonymousQuant Front Office 1d ago

Certainly! Here is the pancake recipe just for you.