Book Guide¶

ml4t-models is the library form of the model families developed manually in the book notebooks.

From The Factor Zoo To A Library Taxonomy

The goal is not to hide the teaching implementation. The goal is to:

show the architecture and mathematics clearly in the chapter notebooks
use the library for repeatable case-study execution and downstream integration

Chapter Mapping¶

Chapter 14: Latent Factors¶

The latent-factor chapter corresponds most directly to:

PCAModel
RPPCAModel
IPCAModel
CAEModel
StochasticDiscountFactorModel
SAEModel as supervised autoencoder direct prediction

The key conceptual transition from the notebooks to the library is:

notebook exposition may derive the math and architecture step by step
library code enforces the clean separation between:
structural extraction
factor forecasting
asset mapping

That separation matters most for IPCAModel and CAEModel. In the teaching notebooks, it is helpful to show the full architecture and fitted-return logic step by step. In the library, the corresponding production object is the two-step pipeline:

structural estimator -> factor-premium forecaster -> asset mapper

Chapter 17: Portfolio Construction¶

The end-to-end allocation family corresponds to:

LinearFeaturePortfolioModel
LSTMPortfolioModel
DeepPortfolioModel

These models are designed to connect naturally to:

Chapter 18 cost modeling
Chapter 19 risk controls
Chapter 20 strategy analysis

Why The Library Split Matters¶

The book often needs to compare multiple modeling ideas side by side:

latent-factor models
no-arbitrage SDF models
direct signal models
end-to-end allocation models

The library turns those into explicit families instead of treating them as one generic “deep learning model.”

Case Studies¶

The case studies are intended to act as:

integration tests
realistic pressure tests for the API
examples of how to hand model outputs into ml4t-backtest and ml4t-diagnostic

They should not define the public API by accident.