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Integration

ml4t-models integrates with the rest of the ML4T stack at boundaries. It does not try to absorb execution or evaluation logic.

Boundary Design

This Library Owns

  • model estimation
  • typed input contracts
  • typed result objects
  • prediction and weight frames

This Library Does Not Own

  • execution simulation
  • portfolio diagnostics
  • statistical validation reports

Those belong in:

  • ml4t-backtest
  • ml4t-diagnostic

Long-Frame To Batch Adapters

Use:

  • persistent_panel_batch_from_long_frame
  • cross_section_batch_from_long_frame
  • resolve_dataset_schema

These help when your data starts as:

  • a pandas frame
  • a polars frame
  • a long-format table with ML4T-style schema metadata

If ml4t-specs is installed, the adapters accept FeedSpec objects directly. Without that optional dependency, they still accept explicit column names and plain metadata mappings. The library does not require users to source data through ml4t-data.

Frame Adapters

The frame helpers normalize model outputs into standard long-format tables.

Predictions Frames

  • predictions_frame_from_asset_forecast
  • predictions_frame_from_asset_signal

Output columns:

  • timestamp
  • asset
  • prediction_value

Weight And Signal Frames

  • signals_frame_from_portfolio_weights
  • signals_frame_from_asset_weights
  • weights_frame_from_portfolio_weights
  • weights_frame_from_asset_weights
  • context_frame_from_weights

Backtest Handoff

Use:

  • backtest_datafeed_inputs
  • backtest_inputs_from_asset_forecast
  • backtest_inputs_from_asset_signal
  • backtest_inputs_from_weights

These construct:

  • standardized signal frames
  • optional context frames
  • FeedSpec-compatible metadata for ml4t-backtest

The handoff payload is intentionally shallow: it prepares frames and metadata, then lets ml4t-backtest own execution simulation.

Diagnostics Handoff

ml4t-models emits prediction, signal, and weight frames with standard timestamp and asset columns. Use those frames as inputs to ml4t-diagnostic for cross-sectional IC, portfolio diagnostics, tearsheets, and validation reports.

This library does not compute IC summaries or diagnostics itself. Keeping diagnostics in ml4t-diagnostic prevents model implementations from carrying a second evaluation stack.

Artifact Writing

Use:

from ml4t.models import write_backtest_frames

to emit:

  • predictions.parquet
  • weights.parquet

in the artifact conventions expected downstream.

Example

from ml4t.models import (
    backtest_inputs_from_asset_forecast,
    predictions_frame_from_asset_forecast,
    write_backtest_frames,
)

frame = predictions_frame_from_asset_forecast(asset_forecast)
write_backtest_frames("artifacts/run_001", predictions=frame)

inputs = backtest_inputs_from_asset_forecast(
    asset_forecast,
    prices_path="prices.parquet",
    timestamp_col="timestamp",
    entity_col="asset",
    close_col="close",
)

Rule Of Thumb

If you find yourself computing:

  • IC summaries
  • tearsheets
  • execution PnL
  • trade analytics

inside ml4t-models, you are probably crossing the intended library boundary.