From 53e92f4af36b7d3c6a7a0486b5a3f9b03331942a Mon Sep 17 00:00:00 2001 From: Stefan Jansen Date: Tue, 3 Mar 2026 12:49:40 -0500 Subject: [PATCH 1/8] docs: add mkdocs GitHub Pages deploy workflow and fix nav --- .github/workflows/docs.yml | 55 ++ .gitignore | 3 + docs/user-guide/ml-readiness.md | 4 +- mkdocs.yml | 11 +- pyproject.toml | 18 +- uv.lock | 875 +++++++++----------------------- 6 files changed, 327 insertions(+), 639 deletions(-) create mode 100644 .github/workflows/docs.yml diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..15ab1a2 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,55 @@ +name: Docs + +on: + push: + branches: [main] + workflow_dispatch: + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: "pages" + cancel-in-progress: true + +jobs: + build: + name: Build Docs + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Install uv + uses: astral-sh/setup-uv@v4 + with: + version: "latest" + + - name: Set up Python + run: uv python install 3.12 + + - name: Install dependencies + run: uv sync --dev --extra docs + + - name: Build docs + run: uv run mkdocs build --strict + + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + with: + path: site/ + + deploy: + name: Deploy to GitHub Pages + needs: build + runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/.gitignore b/.gitignore index c062218..c64e408 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,9 @@ __pycache__/ .hypothesis/ htmlcov/ +# MkDocs build output +site/ + # Claude Code (local development only) CLAUDE.md .claude/ diff --git a/docs/user-guide/ml-readiness.md b/docs/user-guide/ml-readiness.md index 2a4a11b..b715b32 100644 --- a/docs/user-guide/ml-readiness.md +++ b/docs/user-guide/ml-readiness.md @@ -174,5 +174,5 @@ result = result.with_columns( ## Reference -- See [Feature Registry](../api/registry.md) for full metadata API -- See [Preprocessing](../api/preprocessing.md) for scaler documentation +- See [API Reference](../api/index.md) for full metadata API +- See [Preprocessing](preprocessing.md) for scaler documentation diff --git a/mkdocs.yml b/mkdocs.yml index 90dc9f9..b98a0fd 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -3,9 +3,9 @@ site_name: ML4T Engineer site_description: High-performance feature engineering for financial machine learning -site_url: https://ml4t.io/docs/engineer/ -repo_url: https://github.com/stefan-jansen/ml4t-engineer -repo_name: stefan-jansen/ml4t-engineer +site_url: https://ml4t.github.io/engineer/ +repo_url: https://github.com/ml4t/engineer +repo_name: ml4t/engineer # Theme configuration - Picasso Blue Period palette theme: @@ -119,8 +119,13 @@ nav: - Quickstart: getting-started/quickstart.md - User Guide: - Features: user-guide/features.md + - Feature Discovery: user-guide/discovery.md - Labeling: user-guide/labeling.md - Alternative Bars: user-guide/bars.md + - Dataset Builder: user-guide/dataset-builder.md + - Preprocessing: user-guide/preprocessing.md + - Fractional Differencing: user-guide/fractional-differencing.md + - ML Readiness: user-guide/ml-readiness.md - API Reference: - api/index.md diff --git a/pyproject.toml b/pyproject.toml index e16f00e..462c261 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -149,11 +149,9 @@ dev = [ # Documentation generation docs = [ - "sphinx>=7.0.0", - "sphinx-rtd-theme>=1.3.0", - "sphinx-autodoc-typehints>=1.24.0", - "myst-parser>=2.0.0", - "nbsphinx>=0.9.0", + "mkdocs>=1.6,<2", + "mkdocs-material>=9.5.0", + "mkdocstrings[python]>=0.24.0", ] # Complete installation @@ -177,16 +175,14 @@ all = [ "ipdb>=0.13.0", "pre-commit>=3.3.0", "arch>=6.0.0", - "sphinx>=7.0.0", - "sphinx-rtd-theme>=1.3.0", - "sphinx-autodoc-typehints>=1.24.0", - 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true jobs: build: - name: Build Docs + name: Build & Deploy Docs runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -37,19 +35,16 @@ jobs: - name: Build docs run: uv run mkdocs build --strict - - name: Upload artifact - uses: actions/upload-pages-artifact@v3 + - name: Deploy to website repo + uses: cpina/github-action-push-to-another-repository@v1.7.2 + env: + SSH_DEPLOY_KEY: ${{ secrets.DOCS_DEPLOY_KEY }} with: - path: site/ - - deploy: - name: Deploy to GitHub Pages - needs: build - runs-on: ubuntu-latest - environment: - name: github-pages - url: ${{ steps.deployment.outputs.page_url }} - steps: - - name: Deploy to GitHub Pages - id: deployment - uses: actions/deploy-pages@v4 + source-directory: site/ + destination-github-username: ml4t + destination-repository-name: website + target-directory: static/docs/engineer/ + target-branch: main + commit-message: "docs(engineer): update from ml4t/engineer@${{ github.sha }}" + user-name: ml4t-bot + user-email: bot@ml4trading.io diff --git a/mkdocs.yml b/mkdocs.yml index b98a0fd..264accc 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -3,7 +3,7 @@ site_name: ML4T Engineer site_description: High-performance feature engineering for financial machine learning -site_url: https://ml4t.github.io/engineer/ +site_url: https://ml4trading.io/docs/engineer/ repo_url: https://github.com/ml4t/engineer repo_name: ml4t/engineer diff --git a/pyproject.toml b/pyproject.toml index 462c261..e09cc64 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -182,7 +182,7 @@ all = [ [project.urls] Homepage = "https://github.com/stefan-jansen/ml4t-engineer" -Documentation = "https://ml4t.github.io/engineer/" +Documentation = "https://ml4trading.io/docs/engineer/" Repository = "https://github.com/stefan-jansen/ml4t-engineer" Issues = "https://github.com/stefan-jansen/ml4t-engineer/issues" Changelog = "https://github.com/stefan-jansen/ml4t-engineer/blob/main/CHANGELOG.md" From 720b1fb7cdbe223e3e4cd823d6ec349b546061d8 Mon Sep 17 00:00:00 2001 From: Stefan Jansen Date: Sat, 14 Mar 2026 15:35:17 -0400 Subject: [PATCH 3/8] chore: raise python support floor to 3.12 --- .github/workflows/ci.yml | 4 +- .github/workflows/release.yml | 2 +- README.md | 2 +- api.yaml | 2 +- docs/getting-started/installation.md | 2 +- pyproject.toml | 8 +- src/ml4t/engineer/features/utils/ma_types.py | 4 +- uv.lock | 370 +------------------ 8 files changed, 16 insertions(+), 378 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 1711525..cca80aa 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -7,7 +7,7 @@ on: branches: [main] env: - PYTHON_VERSION: "3.11" + PYTHON_VERSION: "3.12" jobs: lint: @@ -59,7 +59,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.11", "3.12", "3.13"] + python-version: ["3.12", "3.13", "3.14"] steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index d0db487..fde91d6 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -5,7 +5,7 @@ on: tags: ["v*"] env: - PYTHON_VERSION: "3.11" + PYTHON_VERSION: "3.12" jobs: build: diff --git a/README.md b/README.md index 64618a2..2528010 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # ml4t-engineer -[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/) +[![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/) [![PyPI](https://img.shields.io/pypi/v/ml4t-engineer)](https://pypi.org/project/ml4t-engineer/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) diff --git a/api.yaml b/api.yaml index c6c5e9d..e39365f 100644 --- a/api.yaml +++ b/api.yaml @@ -1,6 +1,6 @@ name: ml4t-engineer version: 1.0.0 -python_requires: ">=3.11" +python_requires: ">=3.12" description: High-performance feature engineering for financial machine learning modules: diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md index aa300b1..fc58e61 100644 --- a/docs/getting-started/installation.md +++ b/docs/getting-started/installation.md @@ -2,7 +2,7 @@ ## Requirements -- Python 3.11 or higher +- Python 3.12 or higher - Polars 0.20+ ## Install from PyPI diff --git a/pyproject.toml b/pyproject.toml index e09cc64..fe92238 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -59,15 +59,15 @@ classifiers = [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", "Topic :: Office/Business :: Financial :: Investment", "Topic :: Scientific/Engineering :: Information Analysis", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Typing :: Typed", ] -requires-python = ">=3.11" +requires-python = ">=3.12" # Core dependencies dependencies = [ @@ -227,7 +227,7 @@ markers = [ [tool.ruff] line-length = 100 -target-version = "py311" +target-version = "py312" fix = true exclude = ["_version.py"] # Auto-generated by setuptools-scm @@ -254,7 +254,7 @@ ignore = [ "tests/*" = ["E402", "ARG002", "B017", "F841"] [tool.ty.environment] -python-version = "3.11" +python-version = "3.12" root = ["src"] [tool.ty.src] diff --git a/src/ml4t/engineer/features/utils/ma_types.py b/src/ml4t/engineer/features/utils/ma_types.py index 725b00a..38a078f 100644 --- a/src/ml4t/engineer/features/utils/ma_types.py +++ b/src/ml4t/engineer/features/utils/ma_types.py @@ -4,7 +4,7 @@ Maps TA-Lib MA type codes to ml4t.engineer implementations. 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data specs into engineer config --- src/ml4t/engineer/config/__init__.py | 2 + src/ml4t/engineer/config/spec_bridge.py | 59 +++++++++++++++++++++++++ tests/test_data_contract_config.py | 50 ++++++++++++++++++++- 3 files changed, 109 insertions(+), 2 deletions(-) create mode 100644 src/ml4t/engineer/config/spec_bridge.py diff --git a/src/ml4t/engineer/config/__init__.py b/src/ml4t/engineer/config/__init__.py index d9a7e4e..5bed404 100644 --- a/src/ml4t/engineer/config/__init__.py +++ b/src/ml4t/engineer/config/__init__.py @@ -25,6 +25,7 @@ ) from ml4t.engineer.config.labeling import LabelingConfig from ml4t.engineer.config.preprocessing_config import PreprocessingConfig +from ml4t.engineer.config.spec_bridge import data_contract_from_market_data_spec __all__ = [ # Base configs @@ -34,6 +35,7 @@ # Labeling and preprocessing configs "LabelingConfig", "DataContractConfig", + "data_contract_from_market_data_spec", "PreprocessingConfig", # Experiment config loading "ExperimentConfig", diff --git a/src/ml4t/engineer/config/spec_bridge.py b/src/ml4t/engineer/config/spec_bridge.py new file mode 100644 index 0000000..09b7ced --- /dev/null +++ b/src/ml4t/engineer/config/spec_bridge.py @@ -0,0 +1,59 @@ +"""Compatibility bridge from shared market-data specs to engineer contracts.""" + +from __future__ import annotations + +from collections.abc import Mapping +from typing import Any + +from ml4t.engineer.config.data_contract import DataContractConfig + + +def _get_field(source: Mapping[str, Any] | object | None, *names: str) -> Any: + """Return the first available field value from a mapping or object.""" + if source is None: + return None + + if isinstance(source, Mapping): + for name in names: + if name in source: + return source[name] + return None + + for name in names: + if hasattr(source, name): + return getattr(source, name) + return None + + +def data_contract_from_market_data_spec( + spec: Mapping[str, Any] | object, +) -> DataContractConfig: + """Build a dataframe contract from a shared market-data spec shape. + + The bridge accepts either a nested mapping produced by ``MarketDataSpec.to_dict()`` + or an object exposing a ``schema`` attribute with the same field names. + """ + + schema = _get_field(spec, "schema") + + timestamp_col = _get_field(schema, "timestamp_col") or _get_field(spec, "timestamp_col") + symbol_col = _get_field(schema, "entity_col", "symbol_col") or _get_field( + spec, "entity_col", "symbol_col" + ) + close_col = _get_field(schema, "close_col", "price_col") or _get_field( + spec, "close_col", "price_col" + ) + + return DataContractConfig( + timestamp_col=timestamp_col or "timestamp", + symbol_col=symbol_col or "symbol", + price_col=close_col or "close", + open_col=_get_field(schema, "open_col") or _get_field(spec, "open_col") or "open", + high_col=_get_field(schema, "high_col") or _get_field(spec, "high_col") or "high", + low_col=_get_field(schema, "low_col") or _get_field(spec, "low_col") or "low", + close_col=close_col or "close", + volume_col=_get_field(schema, "volume_col") or _get_field(spec, "volume_col") or "volume", + ) + + +__all__ = ["data_contract_from_market_data_spec"] diff --git a/tests/test_data_contract_config.py b/tests/test_data_contract_config.py index cf7956e..188470d 100644 --- a/tests/test_data_contract_config.py +++ b/tests/test_data_contract_config.py @@ -1,11 +1,11 @@ """Tests for DataContractConfig.""" import sys -from types import ModuleType +from types import ModuleType, SimpleNamespace import pytest -from ml4t.engineer.config import DataContractConfig +from ml4t.engineer.config import DataContractConfig, data_contract_from_market_data_spec class TestDataContractConfig: @@ -91,3 +91,49 @@ class _SchemaWithAliases: assert contract.close_col == "last_price" assert contract.price_col == "last_price" assert contract.volume_col == "volume_base" + + def test_from_market_data_spec_mapping(self): + """Shared market-data mappings should convert into engineer contracts.""" + contract = data_contract_from_market_data_spec( + { + "kind": "market_data", + "schema": { + "timestamp_col": "ts_event", + "entity_col": "asset_id", + "open_col": "open_price", + "high_col": "high_price", + "low_col": "low_price", + "close_col": "mid_close", + "volume_col": "trade_count", + }, + } + ) + + assert contract.timestamp_col == "ts_event" + assert contract.symbol_col == "asset_id" + assert contract.price_col == "mid_close" + assert contract.open_col == "open_price" + assert contract.high_col == "high_price" + assert contract.low_col == "low_price" + assert contract.close_col == "mid_close" + assert contract.volume_col == "trade_count" + + def test_from_market_data_spec_object(self): + """Object-based shared market-data specs should also convert cleanly.""" + spec = SimpleNamespace( + schema=SimpleNamespace( + timestamp_col="bar_end", + entity_col="symbol", + close_col="close_bid_price", + ) + ) + + contract = data_contract_from_market_data_spec(spec) + assert contract.timestamp_col == "bar_end" + assert contract.symbol_col == "symbol" + assert contract.price_col == "close_bid_price" + assert contract.close_col == "close_bid_price" + assert contract.open_col == "open" + assert contract.high_col == "high" + assert contract.low_col == "low" + assert contract.volume_col == "volume" From 9d4f0ed79872bfd3e9e0bb29192d44625b225834 Mon Sep 17 00:00:00 2001 From: Stefan Jansen Date: Mon, 23 Mar 2026 13:00:09 -0400 Subject: [PATCH 5/8] test: cover shared market data spec defaults --- tests/test_data_contract_config.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/tests/test_data_contract_config.py b/tests/test_data_contract_config.py index 188470d..137100b 100644 --- a/tests/test_data_contract_config.py +++ b/tests/test_data_contract_config.py @@ -137,3 +137,16 @@ def test_from_market_data_spec_object(self): assert contract.high_col == "high" assert contract.low_col == "low" assert contract.volume_col == "volume" + + def test_from_market_data_spec_defaults_when_schema_is_missing(self): + """Empty shared specs should fall back to engineer's canonical columns.""" + contract = data_contract_from_market_data_spec({}) + + assert contract.timestamp_col == "timestamp" + assert contract.symbol_col == "symbol" + assert contract.price_col == "close" + assert contract.open_col == "open" + assert contract.high_col == "high" + assert contract.low_col == "low" + assert contract.close_col == "close" + assert contract.volume_col == "volume" From 783e1de45d739ac858191f44a4f7b0f599f098f5 Mon Sep 17 00:00:00 2001 From: Stefan Jansen Date: Mon, 23 Mar 2026 20:15:04 -0400 Subject: [PATCH 6/8] feat: add engineered artifact specs --- pyproject.toml | 5 + src/ml4t/engineer/artifacts/__init__.py | 17 ++ src/ml4t/engineer/artifacts/features.py | 72 +++++ src/ml4t/engineer/artifacts/labels.py | 85 ++++++ src/ml4t/engineer/artifacts/predictions.py | 80 +++++ uv.lock | 340 +++++++++++++++++++++ 6 files changed, 599 insertions(+) create mode 100644 src/ml4t/engineer/artifacts/__init__.py create mode 100644 src/ml4t/engineer/artifacts/features.py create mode 100644 src/ml4t/engineer/artifacts/labels.py create mode 100644 src/ml4t/engineer/artifacts/predictions.py diff --git a/pyproject.toml b/pyproject.toml index fe92238..5d6ce60 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -71,6 +71,8 @@ requires-python = ">=3.12" # Core dependencies dependencies = [ + "ml4t-data>=0.1.0b7", + # Data processing (Polars-first) "polars>=0.20.0", "pandas>=2.0.0", @@ -287,3 +289,6 @@ exclude_lines = [ "pass", "except ImportError:", ] + +[tool.uv.sources] +ml4t-data = { path = "../ml4t-data", editable = true } diff --git a/src/ml4t/engineer/artifacts/__init__.py b/src/ml4t/engineer/artifacts/__init__.py new file mode 100644 index 0000000..d140565 --- /dev/null +++ b/src/ml4t/engineer/artifacts/__init__.py @@ -0,0 +1,17 @@ +"""Artifact contracts owned by ml4t-engineer.""" + +from .features import FeatureDefinition, FeatureSchema, FeatureSpec +from .labels import LabelDefinition, LabelSchema, LabelSpec +from .predictions import PredictionDefinition, PredictionSchema, PredictionSpec + +__all__ = [ + "FeatureDefinition", + "FeatureSchema", + "FeatureSpec", + "LabelDefinition", + "LabelSchema", + "LabelSpec", + "PredictionDefinition", + "PredictionSchema", + "PredictionSpec", +] diff --git a/src/ml4t/engineer/artifacts/features.py b/src/ml4t/engineer/artifacts/features.py new file mode 100644 index 0000000..5962941 --- /dev/null +++ b/src/ml4t/engineer/artifacts/features.py @@ -0,0 +1,72 @@ +"""Feature artifact contracts owned by ml4t-engineer.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +from ml4t.data.artifacts.base import ArtifactKind, ArtifactProvenance, ArtifactSpec, ArtifactStorage + + +@dataclass(frozen=True, slots=True) +class FeatureSchema: + """Column layout for a feature artifact.""" + + timestamp_col: str = "timestamp" + entity_col: str = "symbol" + feature_columns: tuple[str, ...] = () + + @classmethod + def from_mapping(cls, mapping: dict[str, Any] | None) -> FeatureSchema: + if mapping is None: + return cls() + feature_columns = mapping.get("feature_columns", ()) + return cls( + timestamp_col=str(mapping.get("timestamp_col", "timestamp")), + entity_col=str(mapping.get("entity_col", "symbol")), + feature_columns=tuple(str(item) for item in feature_columns), + ) + + +@dataclass(frozen=True, slots=True) +class FeatureDefinition: + """Definition metadata for a feature artifact.""" + + family: str = "financial" + join_keys: tuple[str, ...] = () + source_artifacts: tuple[str, ...] = () + + @classmethod + def from_mapping(cls, mapping: dict[str, Any] | None) -> FeatureDefinition: + if mapping is None: + return cls() + join_keys = mapping.get("join_keys", ()) + source_artifacts = mapping.get("source_artifacts", ()) + return cls( + family=str(mapping.get("family", "financial")), + join_keys=tuple(str(item) for item in join_keys), + source_artifacts=tuple(str(item) for item in source_artifacts), + ) + + +@dataclass(frozen=True, slots=True) +class FeatureSpec(ArtifactSpec): + """Shared specification for persisted feature artifacts.""" + + kind: ArtifactKind = field(default=ArtifactKind.FEATURES, init=False) + schema: FeatureSchema = field(default_factory=FeatureSchema) + definition: FeatureDefinition = field(default_factory=FeatureDefinition) + + @classmethod + def from_mapping(cls, mapping: dict[str, Any]) -> FeatureSpec: + return cls( + artifact_id=str(mapping["artifact_id"]), + version=int(mapping.get("version", 1)), + storage=ArtifactStorage.from_mapping(mapping.get("storage")), + provenance=ArtifactProvenance.from_mapping(mapping.get("provenance")), + schema=FeatureSchema.from_mapping(mapping.get("schema")), + definition=FeatureDefinition.from_mapping(mapping.get("definition")), + ) + + +__all__ = ["FeatureDefinition", "FeatureSchema", "FeatureSpec"] diff --git a/src/ml4t/engineer/artifacts/labels.py b/src/ml4t/engineer/artifacts/labels.py new file mode 100644 index 0000000..77bb147 --- /dev/null +++ b/src/ml4t/engineer/artifacts/labels.py @@ -0,0 +1,85 @@ +"""Label artifact contracts owned by ml4t-engineer.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +from ml4t.data.artifacts.base import ( + ArtifactKind, + ArtifactProvenance, + ArtifactSpec, + ArtifactStorage, + optional_str, +) + + +@dataclass(frozen=True, slots=True) +class LabelSchema: + """Column layout for a label artifact.""" + + timestamp_col: str = "timestamp" + entity_col: str = "symbol" + label_col: str = "label" + + @classmethod + def from_mapping(cls, mapping: dict[str, Any] | None) -> LabelSchema: + if mapping is None: + return cls() + return cls( + timestamp_col=str(mapping.get("timestamp_col", "timestamp")), + entity_col=str(mapping.get("entity_col", "symbol")), + label_col=str(mapping.get("label_col", "label")), + ) + + +@dataclass(frozen=True, slots=True) +class LabelDefinition: + """Definition metadata for a label artifact.""" + + family: str = "forward_return" + task_type: str = "regression" + horizon: str | None = None + buffer: str | None = None + source_artifact: str | None = None + reference_field: str | None = None + execution_delay: str | None = None + session_bounded: bool = False + + @classmethod + def from_mapping(cls, mapping: dict[str, Any] | None) -> LabelDefinition: + if mapping is None: + return cls() + return cls( + family=str(mapping.get("family", "forward_return")), + task_type=str(mapping.get("task_type", "regression")), + horizon=optional_str(mapping.get("horizon")), + buffer=optional_str(mapping.get("buffer")), + source_artifact=optional_str(mapping.get("source_artifact")), + reference_field=optional_str(mapping.get("reference_field")), + execution_delay=optional_str(mapping.get("execution_delay")), + session_bounded=bool(mapping.get("session_bounded", False)), + ) + + +@dataclass(frozen=True, slots=True) +class LabelSpec(ArtifactSpec): + """Shared specification for persisted label artifacts.""" + + kind: ArtifactKind = field(default=ArtifactKind.LABELS, init=False) + schema: LabelSchema = field(default_factory=LabelSchema) + definition: LabelDefinition = field(default_factory=LabelDefinition) + + @classmethod + def from_mapping(cls, mapping: dict[str, Any]) -> LabelSpec: + return cls( + artifact_id=str(mapping["artifact_id"]), + version=int(mapping.get("version", 1)), + storage=ArtifactStorage.from_mapping(mapping.get("storage")), + provenance=ArtifactProvenance.from_mapping(mapping.get("provenance")), + schema=LabelSchema.from_mapping(mapping.get("schema")), + definition=LabelDefinition.from_mapping(mapping.get("definition")), + ) + + +__all__ = ["LabelDefinition", "LabelSchema", "LabelSpec"] diff --git a/src/ml4t/engineer/artifacts/predictions.py b/src/ml4t/engineer/artifacts/predictions.py new file mode 100644 index 0000000..3d19968 --- /dev/null +++ b/src/ml4t/engineer/artifacts/predictions.py @@ -0,0 +1,80 @@ +"""Prediction artifact contracts owned by ml4t-engineer.""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +from ml4t.data.artifacts.base import ( + ArtifactKind, + ArtifactProvenance, + ArtifactSpec, + ArtifactStorage, + optional_str, +) + + +@dataclass(frozen=True, slots=True) +class PredictionSchema: + """Column layout for a prediction artifact.""" + + timestamp_col: str = "timestamp" + entity_col: str = "symbol" + prediction_col: str = "prediction" + + @classmethod + def from_mapping(cls, mapping: dict[str, Any] | None) -> PredictionSchema: + if mapping is None: + return cls() + return cls( + timestamp_col=str(mapping.get("timestamp_col", "timestamp")), + entity_col=str(mapping.get("entity_col", "symbol")), + prediction_col=str(mapping.get("prediction_col", "prediction")), + ) + + +@dataclass(frozen=True, slots=True) +class PredictionDefinition: + """Definition metadata for a prediction artifact.""" + + split_protocol: str = "walk_forward_oos" + label_artifact: str | None = None + feature_artifacts: tuple[str, ...] = () + training_hash: str | None = None + + @classmethod + def from_mapping(cls, mapping: dict[str, Any] | None) -> PredictionDefinition: + if mapping is None: + return cls() + feature_artifacts = mapping.get("feature_artifacts", ()) + if isinstance(feature_artifacts, str): + feature_artifacts = (feature_artifacts,) + return cls( + split_protocol=str(mapping.get("split_protocol", "walk_forward_oos")), + label_artifact=optional_str(mapping.get("label_artifact")), + feature_artifacts=tuple(str(item) for item in feature_artifacts), + training_hash=optional_str(mapping.get("training_hash")), + ) + + +@dataclass(frozen=True, slots=True) +class PredictionSpec(ArtifactSpec): + """Shared specification for persisted prediction artifacts.""" + + kind: ArtifactKind = field(default=ArtifactKind.PREDICTIONS, init=False) + schema: PredictionSchema = field(default_factory=PredictionSchema) + definition: PredictionDefinition = field(default_factory=PredictionDefinition) + + @classmethod + def from_mapping(cls, mapping: dict[str, Any]) -> PredictionSpec: + return cls( + artifact_id=str(mapping["artifact_id"]), + version=int(mapping.get("version", 1)), + storage=ArtifactStorage.from_mapping(mapping.get("storage")), + provenance=ArtifactProvenance.from_mapping(mapping.get("provenance")), + schema=PredictionSchema.from_mapping(mapping.get("schema")), + definition=PredictionDefinition.from_mapping(mapping.get("definition")), + ) + + +__all__ = ["PredictionDefinition", "PredictionSchema", "PredictionSpec"] diff --git a/uv.lock b/uv.lock index d9a93fd..3cbef2c 100644 --- a/uv.lock +++ b/uv.lock @@ -6,6 +6,15 @@ resolution-markers = [ "python_full_version < '3.14'", ] +[[package]] +name = "aiofiles" +version = "25.1.0" +source = { registry = 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docs/api/index.md | 67 +++++++ docs/book-guide/index.md | 151 ++++++++++++++++ docs/getting-started/installation.md | 12 ++ docs/getting-started/quickstart.md | 5 + docs/index.md | 169 ++++++++++++++---- docs/user-guide/bars.md | 19 ++ docs/user-guide/dataset-builder.md | 19 ++ docs/user-guide/discovery.md | 19 +- docs/user-guide/features.md | 21 +++ docs/user-guide/fractional-differencing.md | 19 +- docs/user-guide/labeling.md | 19 ++ docs/user-guide/ml-readiness.md | 13 ++ docs/user-guide/preprocessing.md | 16 +- mkdocs.yml | 32 +--- pyproject.toml | 8 +- src/ml4t/engineer/AGENT.md | 101 ----------- src/ml4t/engineer/AGENTS.md | 83 +++++++++ src/ml4t/engineer/__init__.py | 36 +++- src/ml4t/engineer/artifacts/AGENTS.md | 17 ++ src/ml4t/engineer/bars/AGENT.md | 87 --------- src/ml4t/engineer/bars/AGENTS.md | 72 ++++++++ src/ml4t/engineer/config/AGENTS.md | 26 +++ src/ml4t/engineer/core/AGENTS.md | 22 +++ src/ml4t/engineer/core/calendars/AGENTS.md | 16 ++ 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src/ml4t/engineer/logging/AGENTS.md create mode 100644 src/ml4t/engineer/relationships/AGENTS.md create mode 100644 src/ml4t/engineer/store/AGENTS.md create mode 100644 src/ml4t/engineer/utils/AGENTS.md diff --git a/AGENT.md b/AGENT.md deleted file mode 100644 index 33e17e7..0000000 --- a/AGENT.md +++ /dev/null @@ -1,86 +0,0 @@ -# ml4t-engineer - -Feature engineering for financial ML. 120 indicators, triple-barrier labeling, alternative bars. - -## Quick Start - -```python -from ml4t.engineer import compute_features -result = compute_features(df, ["rsi", "macd", "atr"]) -``` - -## Directory Map - -| Path | Purpose | Key Exports | -|------|---------|-------------| -| `features/` | 120 technical indicators | `compute_features()`, `list_features()` | -| `labeling/` | ML label generation | `triple_barrier_labels()`, `rolling_percentile_binary_labels()` | -| `bars/` | Alternative bar sampling | `volume_bars()`, `dollar_bars()`, `tick_imbalance_bars()` | -| `core/` | Registry, types, validation | `get_registry()`, `FeatureMetadata` | -| `config/` | Pydantic configurations | `LabelingConfig`, `PreprocessingConfig` | -| `preprocessing.py` | Scalers, transforms | `StandardScaler`, `RobustScaler` | - -## Entry Points - -```python -from ml4t.engineer import compute_features, list_features, describe_feature -from ml4t.engineer.labeling import triple_barrier_labels, atr_triple_barrier_labels -from ml4t.engineer.labeling import rolling_percentile_binary_labels -from ml4t.engineer.bars import volume_bars, dollar_bars, tick_imbalance_bars -``` - -## Feature Categories - -| Category | Count | Examples | -|----------|-------|----------| -| momentum | 31 | rsi, macd, adx, stoch, cci, mfi | -| trend | 10 | sma, ema, kama, tema, dema | -| volatility | 15 | atr, yang_zhang, bollinger_bands, garch | -| microstructure | 12 | kyle_lambda, amihud, vpin, roll_spread | -| ml | 11 | lag, fractional_diff, entropy, cyclical_encode | -| statistics | 8 | stddev, var, linear_regression | -| volume | 3 | obv, ad, adosc | -| price_transform | 5 | avgprice, typprice, medprice | -| math | 3 | max_, min_, sum_ | - -## Core API - -```python -# List available features -list_features() # All 120 features -list_features("momentum") # 31 momentum indicators - -# Compute with custom parameters -compute_features(df, [ - {"name": "rsi", "params": {"period": 20}}, - {"name": "sma", "params": {"period": 50}}, -]) - -# Get feature metadata -describe_feature("rsi") # Returns dict with params, formula, range -``` - -## Labeling Methods - -| Method | Use Case | -|--------|----------| -| `triple_barrier_labels()` | Path-dependent with PT/SL barriers | -| `atr_triple_barrier_labels()` | ATR-based dynamic barriers | -| `rolling_percentile_binary_labels()` | Adaptive percentile thresholds | -| `trend_scanning_labels()` | De Prado's t-stat method | -| `fixed_time_horizon_labels()` | Simple forward returns | - -## Performance - -- **Speed**: Polars-native implementation, ~1x TA-Lib for RSI, streaming at 11M rows/second -- **Validation**: 60 TA-Lib compatible features at 1e-6 tolerance -- **Throughput**: ~480K indicator calculations/second (batch mode) - -## Navigation - -See `src/ml4t/engineer/AGENT.md` for package-level detail with function signatures. - -## References - -- Lopez de Prado (2018). *Advances in Financial Machine Learning* -- Lopez de Prado (2020). *Machine Learning for Asset Managers* diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..f74ec34 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,84 @@ +# ml4t-engineer + +Feature engineering for financial ML: compute indicators, create labels, sample +alternative bars, and prepare leakage-safe datasets for model training. + +## Quick Start + +```python +from ml4t.engineer import compute_features, create_dataset_builder +from ml4t.engineer.config import LabelingConfig +from ml4t.engineer.labeling import triple_barrier_labels + +features = compute_features(df, ["rsi", "macd", "atr"]) +labels = triple_barrier_labels( + features, + config=LabelingConfig.triple_barrier( + upper_barrier=0.02, lower_barrier=0.01, max_holding_period=20, + ), +) +builder = create_dataset_builder( + features=labels.select(["rsi_14", "macd", "atr_14"]), + labels=labels["label"], + dates=labels["timestamp"], + scaler="robust", +) +``` + +## Directory Map + +| Path | Purpose | Key Surfaces | +|------|---------|--------------| +| `src/ml4t/engineer/features/` | Feature computation and discovery metadata | `compute_features()`, `feature_catalog`, `FeatureCatalog` | +| `src/ml4t/engineer/labeling/` | Supervised label generation and sample weighting | `triple_barrier_labels()`, `atr_triple_barrier_labels()`, `rolling_percentile_binary_labels()` | +| `src/ml4t/engineer/bars/` | Tick, volume, dollar, imbalance, and run bars | `TickBarSampler`, `VolumeBarSampler`, `DollarBarSampler` | +| `src/ml4t/engineer/dataset.py` | Leakage-safe dataset preparation | `MLDatasetBuilder`, `create_dataset_builder()` | +| `src/ml4t/engineer/preprocessing.py` | Train-only scalers and pipelines | `StandardScaler`, `RobustScaler`, `PreprocessingPipeline` | +| `src/ml4t/engineer/config/` | Reusable config objects | `LabelingConfig`, `PreprocessingConfig`, `DataContractConfig` | + +## Public Entry Points + +```python +from ml4t.engineer import ( + compute_features, + create_dataset_builder, + feature_catalog, + FeatureCatalog, + StandardScaler, + RobustScaler, +) +from ml4t.engineer.config import LabelingConfig +from ml4t.engineer.labeling import ( + triple_barrier_labels, + atr_triple_barrier_labels, + rolling_percentile_binary_labels, + fixed_time_horizon_labels, + trend_scanning_labels, +) +from ml4t.engineer.bars import ( + TickBarSampler, + VolumeBarSampler, + DollarBarSampler, + FixedTickImbalanceBarSampler, + FixedVolumeImbalanceBarSampler, +) +``` + +## Core Workflows + +- `compute_features()` appends indicator columns to an OHLCV DataFrame or LazyFrame +- `LabelingConfig` plus labeling functions create reusable supervised targets +- `MLDatasetBuilder` handles train/test splitting and train-only scaling +- sampler classes in `bars/` turn trade data into non-time bars for downstream use + +## Trust Signals + +- 120 features across 11 categories +- 60 TA-Lib validated indicators at `1e-6` tolerance +- ~50,000 labels/second for triple-barrier workflows +- shared book integration via `docs/book-guide/index.md` + +## Navigation + +See [src/ml4t/engineer/AGENTS.md](src/ml4t/engineer/AGENTS.md) for package-level +module orientation and subdirectory entry points. diff --git a/CHANGELOG.md b/CHANGELOG.md index dc0bb1d..0437e83 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -88,8 +88,8 @@ Versioning follows [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ### Added - `get_agent_docs()` for AI agent discoverability -- Hierarchical AGENT.md navigation files -- AGENT.md files included in wheel builds +- Hierarchical AGENTS.md navigation files +- AGENTS.md files included in wheel builds ### Fixed - `variance_ratio` Int64 bug diff --git a/docs/api/index.md b/docs/api/index.md index 960ec2e..f0de241 100644 --- a/docs/api/index.md +++ b/docs/api/index.md @@ -1,5 +1,9 @@ # API Reference +Use this page when you already know the workflow and need exact objects, method +signatures, and module locations. If you are still deciding which workflow to use, +start with the [User Guide](../index.md) or the [Book Guide](../book-guide/index.md). + ## Core Functions ::: ml4t.engineer.api @@ -22,8 +26,71 @@ options: show_root_heading: true +## Dataset Builder + +::: ml4t.engineer.dataset.MLDatasetBuilder + options: + show_root_heading: true + signature_crossrefs: false + members: + - set_scaler + - info + - split + - train_test_split + - to_numpy + - to_pandas + +::: ml4t.engineer.dataset.create_dataset_builder + options: + show_root_heading: true + signature_crossrefs: false + +::: ml4t.engineer.dataset.FoldResult + options: + show_root_heading: true + signature_crossrefs: false + +::: ml4t.engineer.dataset.DatasetInfo + options: + show_root_heading: true + signature_crossrefs: false + +## Preprocessing + +::: ml4t.engineer.preprocessing + options: + show_root_heading: true + signature_crossrefs: false + members: + - StandardScaler + - MinMaxScaler + - RobustScaler + - PreprocessingPipeline + - TransformType + - NotFittedError + +## Configuration + +::: ml4t.engineer.config + options: + show_root_heading: true + signature_crossrefs: false + members: + - LabelingConfig + - DataContractConfig + - PreprocessingConfig + - ExperimentConfig + ## Alternative Bars ::: ml4t.engineer.bars options: show_root_heading: true + +## Next Steps + +- Read [Features](../user-guide/features.md) for the main computation workflow. +- Read [Labeling](../user-guide/labeling.md) for supervised target construction. +- Read [Alternative Bars](../user-guide/bars.md) for information-driven sampling. +- Use the [Book Guide](../book-guide/index.md) to map these APIs back to the book and + case studies. diff --git a/docs/book-guide/index.md b/docs/book-guide/index.md new file mode 100644 index 0000000..a8c996d --- /dev/null +++ b/docs/book-guide/index.md @@ -0,0 +1,151 @@ +# Book Guide + +Use this guide to move between *Machine Learning for Trading, Third Edition* and +`ml4t-engineer` without guessing which notebook maps to which production API. + +`ml4t-engineer` appears in two distinct ways throughout the book: + +- pedagogical notebooks that build ideas step by step +- reusable library workflows that collapse those ideas into stable APIs + +The book teaches the method. The library is where you should go when you want to +reuse that method across assets, case studies, and production pipelines. + +## How to Use This Guide + +Start from the book if you want intuition, derivations, and plots. Start from the +library guides if you want reusable implementations, validated APIs, and pipeline +integration. + +Use this page when you need to answer one of these questions: + +- "Which chapter teaches the concept behind this API?" +- "Which notebook should I read before using this workflow in production?" +- "Which library guide replaces the manual code from the book?" + +## Recommended Reader Journey + +1. Read the relevant chapter notebook to understand the modeling idea. +2. Jump to the matching user-guide page for the production API. +3. Use the case-study pipeline files as the bridge from teaching code to reusable + workflows. +4. Use the [API Reference](../api/index.md) when you need exact signatures. + +## Chapter Map + +### Chapter 3: Market Microstructure + +| Book path | What the book teaches | Library entry point | Docs page | +|-----------|-----------------------|---------------------|-----------| +| `03_market_microstructure/code/08_itch_bar_sampling.py` | Why time bars are statistically weak and how tick, volume, and dollar bars improve sampling | `TickBarSampler`, `VolumeBarSampler`, `DollarBarSampler` | [Alternative Bars](../user-guide/bars.md) | +| `03_market_microstructure/code/10_itch_information_bars.py` | Imbalance bars and threshold dynamics | `TickImbalanceBarSampler`, `FixedTickImbalanceBarSampler`, `FixedVolumeImbalanceBarSampler` | [Alternative Bars](../user-guide/bars.md) | +| `03_market_microstructure/code/13_databento_bar_sampling.py` | Applying bar samplers to a modern vendor feed | Same sampler family with production input contracts | [Alternative Bars](../user-guide/bars.md) | + +What changes when you move to the library: + +- the book focuses on the statistical motivation and diagnostics +- the library gives you stable samplers, warnings, and reusable OHLCV outputs +- fixed-threshold imbalance bars are the recommended production path + +### Chapter 7: Defining the Learning Task + +| Book path | What the book teaches | Library entry point | Docs page | +|-----------|-----------------------|---------------------|-----------| +| `07_defining_learning_task/code/02_preprocessing_pipeline.py` | Leakage-safe preprocessing and split-aware scaling | `StandardScaler`, `MinMaxScaler`, `RobustScaler`, `PreprocessingPipeline` | [Preprocessing](../user-guide/preprocessing.md) | +| `07_defining_learning_task/code/03_label_methods.py` | Triple-barrier, percentile, trend-scanning, meta-labeling, and sample weighting | `LabelingConfig`, `triple_barrier_labels`, `rolling_percentile_binary_labels`, `trend_scanning_labels`, `meta_labels` | [Labeling](../user-guide/labeling.md) | +| `07_defining_learning_task/code/04_minimum_favorable_adverse_excursion.py` | Barrier behavior and excursion analysis | `LabelingConfig.triple_barrier()` | [Labeling](../user-guide/labeling.md) | +| `07_defining_learning_task/code/10_ml4t_library_ecosystem.py` | The library-oriented view of feature computation, discovery, and dataset building | `compute_features`, `feature_catalog`, `create_dataset_builder` | [Features](../user-guide/features.md), [Feature Discovery](../user-guide/discovery.md), [Dataset Builder](../user-guide/dataset-builder.md) | + +What changes when you move to the library: + +- manual notebook experiments become serialized `LabelingConfig` workflows +- feature discovery moves from ad hoc inspection to metadata-driven search +- dataset preparation becomes train-only scaling and splitter-aware folds by default + +### Chapter 8: Feature Engineering + +| Book path | What the book teaches | Library entry point | Docs page | +|-----------|-----------------------|---------------------|-----------| +| `08_feature_engineering/code/01_price_volume_features.py` | Momentum, trend, volatility, and volume feature intuition | `compute_features` with registry-backed indicators | [Features](../user-guide/features.md) | +| `08_feature_engineering/code/02_microstructure_features.py` | Microstructure feature construction and interpretation | Microstructure feature functions and registry metadata | [Features](../user-guide/features.md) | +| `08_feature_engineering/code/03_structural_cross_instrument_features.py` | Cross-asset and panel relationships | `ml4t.engineer.features.cross_asset` | [Features](../user-guide/features.md) | +| `08_feature_engineering/code/04_fundamentals_macro_calendar.py` | Lag features, calendar encodings, and ML-oriented transforms | ML feature utilities and preprocessing bridge | [Features](../user-guide/features.md), [ML Readiness](../user-guide/ml-readiness.md) | + +What changes when you move to the library: + +- the book derives and visualizes features individually +- the library lets you request validated feature sets through one computation API +- registry and catalog metadata help you choose features systematically + +### Chapter 9: Time-Series Analysis + +| Book path | What the book teaches | Library entry point | Docs page | +|-----------|-----------------------|---------------------|-----------| +| `09_time_series_analysis/code/03_fractional_differencing.py` | The memory-stationarity tradeoff and ADF-based search for `d` | `ffdiff`, `find_optimal_d`, `fdiff_diagnostics` | [Fractional Differencing](../user-guide/fractional-differencing.md) | +| `09_time_series_analysis/code/08_garch_volatility.py` | Volatility estimators and conditional volatility modeling | Volatility feature family | [Features](../user-guide/features.md) | +| `09_time_series_analysis/code/09_har_rough_volatility.py` | Multi-horizon volatility structure | Volatility features used downstream in pipelines | [Features](../user-guide/features.md) | +| `09_time_series_analysis/code/11_hmm_regimes.py` | Regime detection workflows | Regime feature family | [Features](../user-guide/features.md) | +| `09_time_series_analysis/code/13_regime_as_feature.py` | Turning regimes into model inputs | Regime features in reusable pipelines | [Features](../user-guide/features.md) | +| `09_time_series_analysis/code/14_panel_features.py` | Cross-sectional panel features | Cross-asset feature functions for multi-asset inputs | [Features](../user-guide/features.md) | + +What changes when you move to the library: + +- research notebooks stay focused on method validation +- library functions package the same transforms into repeatable feature pipelines +- the case studies show how to combine these transforms with labels and CV + +## Case-Study Pipeline Map + +Most case studies follow the same structure. `ml4t-engineer` is the handoff point +between raw market data and model-ready datasets. + +| Case-study step | Typical file | Library workflow | Docs page | +|-----------------|--------------|------------------|-----------| +| Labels | `case_studies//code/02_labels.py` | `LabelingConfig`, barrier labels, percentile labels, fixed-horizon labels | [Labeling](../user-guide/labeling.md) | +| Features | `case_studies//code/03_features.py` | `compute_features` plus feature-specific functions | [Features](../user-guide/features.md) | +| Temporal prep | `case_studies//code/04_temporal.py` | fractional differencing and leakage-safe preparation | [Fractional Differencing](../user-guide/fractional-differencing.md), [Dataset Builder](../user-guide/dataset-builder.md) | + +Examples called out in the current integration audit: + +- ETFs: percentile labels, production `compute_features`, fractional differencing +- US Equities Panel: triple-barrier labels, panel features, fractional differencing +- CME Futures: ATR-based barriers and futures-aware labeling workflows +- NASDAQ-100 Microstructure: manual pedagogical implementations with strong overlap to + production-ready microstructure features in this library + +## From Notebook Code to Library API + +Use this translation when moving from the book to reusable code: + +| Book pattern | Library equivalent | +|--------------|--------------------| +| Manually computing several indicators in sequence | `compute_features(data, feature_spec)` | +| Notebook-only feature browsing | `feature_catalog.list()`, `feature_catalog.search()`, `registry.get()` | +| Inline barrier parameters spread across a notebook | `LabelingConfig.triple_barrier(...)` or `LabelingConfig.atr_barrier(...)` | +| One-off train/test scaling | `create_dataset_builder(..., scaler=...)` | +| Manual stationarity experiments | `find_optimal_d()` then `ffdiff()` | +| Bar-construction experiments | sampler classes in `ml4t.engineer.bars` | + +## Maturity and Scope + +These are the workflows readers should prioritize: + +- production-ready: feature computation, labeling methods, alternative bars, + fractional differencing, feature discovery, dataset builder +- advanced: cross-asset feature workflows and pipeline orchestration +- experimental or low-priority: DuckDB store and any workflows not yet used in the + book or case studies + +This matches the current audit: the strongest value in `ml4t-engineer` is the +production API for feature engineering, labeling, and dataset preparation. + +## Where to Go Next + +- Start with [Quickstart](../getting-started/quickstart.md) if you want a working + example first. +- Read [Features](../user-guide/features.md) for the core computation API. +- Read [Labeling](../user-guide/labeling.md) if you are building supervised targets. +- Read [Alternative Bars](../user-guide/bars.md) for microstructure workflows. +- Read [Dataset Builder](../user-guide/dataset-builder.md) for leakage-safe model + inputs. +- Use [API Reference](../api/index.md) for exact signatures. diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md index fc58e61..e64e441 100644 --- a/docs/getting-started/installation.md +++ b/docs/getting-started/installation.md @@ -1,5 +1,8 @@ # Installation +Use this page when you want to install `ml4t-engineer`, verify the package surface, +and then move directly into the first workflow. + ## Requirements - Python 3.12 or higher @@ -11,6 +14,8 @@ pip install ml4t-engineer ``` +If you standardize on `uv`, `uv pip install ml4t-engineer` is equivalent. + ## Install from Source ```bash @@ -55,3 +60,10 @@ Expected output: Total features: 120 Categories: ['math', 'microstructure', 'ml', 'momentum', 'price_transform', 'regime', 'risk', 'statistics', 'trend', 'volatility', 'volume'] ``` + +## Next Steps + +- Read [Quickstart](quickstart.md) for a first working feature and labeling example. +- Read the [Book Guide](../book-guide/index.md) if you are coming from the book or + case studies. +- Use the [API Reference](../api/index.md) once you need exact object locations. diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md index e24dfda..4d254e4 100644 --- a/docs/getting-started/quickstart.md +++ b/docs/getting-started/quickstart.md @@ -2,6 +2,10 @@ Get up and running with ML4T Engineer in 5 minutes. +If you are coming from *Machine Learning for Trading, Third Edition*, pair this page +with the [Book Guide](../book-guide/index.md) to jump from notebook examples to the +matching reusable library workflows. + ## Basic Feature Computation ```python @@ -103,4 +107,5 @@ print(info) - [Feature Discovery](../user-guide/discovery.md) - Registry, catalog, and search API - [Fractional Differencing](../user-guide/fractional-differencing.md) - Memory-preserving stationarity - [Dataset Builder](../user-guide/dataset-builder.md) - Leakage-safe train/test preparation +- [Book Guide](../book-guide/index.md) - Chapter and case-study map for the book - [API Reference](../api/index.md) - Complete API documentation diff --git a/docs/index.md b/docs/index.md index 5772528..10de5b4 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,33 +1,141 @@ # ML4T Engineer -High-performance feature engineering library for financial machine learning. +Turn OHLCV and tick data into model-ready features, labels, and sampling schemes +without rewriting the same research code in every notebook and pipeline. + +`ml4t-engineer` is the feature-engineering layer in the ML4T stack. It sits between +`ml4t-data`, which prepares canonical datasets, and `ml4t-diagnostic`, which +evaluates signals and models. Start here if you want a working workflow quickly, use +the [Book Guide](book-guide/index.md) to map notebooks to production APIs, and use the +[API Reference](api/index.md) when you need exact interfaces. + +Chapters 7-10 of *Machine Learning for Trading, Third Edition* develop many of these +methods manually in notebooks. This library packages those computations as tested, +reusable functions. See the [Book Guide](book-guide/index.md) to map notebook code to +library calls. + +
+ +- :material-chart-line:{ .lg .middle } __120 Indicators, One Call__ + --- + Momentum, volatility, microstructure, trend, and 7 more categories through + `compute_features(df, indicators)`. + [:octicons-arrow-right-24: Features](user-guide/features.md) + +- :material-check-decagram:{ .lg .middle } __60 TA-Lib Validated__ + --- + Indicators tested against TA-Lib to `1e-6` tolerance so notebook and pipeline + outputs stay aligned. + [:octicons-arrow-right-24: Quickstart](getting-started/quickstart.md) + +- :material-label:{ .lg .middle } __Labels, Bars, and Leakage Control__ + --- + Triple-barrier labels, alternative bars, preprocessing, and dataset splitting in + the same workflow. + [:octicons-arrow-right-24: Labeling](user-guide/labeling.md) + +- :material-book-open-variant:{ .lg .middle } __Book to Production__ + --- + The book teaches the methods step by step. This library turns them into reusable + calls for research and scheduled pipelines. + [:octicons-arrow-right-24: Book Guide](book-guide/index.md) + +
-## Overview +## Quick Example -ML4T Engineer provides **120 technical indicators**, triple-barrier labeling, and alternative bar sampling with a Polars-first implementation that's 10-100x faster than pandas alternatives. +You have OHLCV data and need a feature matrix: -## Key Features +```python +import polars as pl +from ml4t.engineer import compute_features -- **120 Technical Indicators** across 11 categories (momentum, trend, volatility, etc.) -- **60 TA-Lib Validated** indicators with 1e-6 tolerance matching -- **Triple-Barrier Labeling** at 50,000 labels/second -- **Alternative Bar Sampling** (volume, dollar, tick imbalance bars) -- **Polars-First** implementation for maximum performance +df = pl.read_parquet("spy_daily.parquet") +features = compute_features(df, ["rsi", "macd", "atr"]) -## Quick Example +print(features.select("rsi_14", "macd", "atr_14").tail(3)) +``` + +That single call appends validated indicator columns to the same DataFrame you will +pass downstream into labeling, preprocessing, and model training. + +## Core Workflows + +### 1. Add supervised labels + +You have features. You need targets for a classification model: ```python -import polars as pl -from ml4t.engineer import compute_features +from ml4t.engineer.config import LabelingConfig +from ml4t.engineer.labeling import triple_barrier_labels + +config = LabelingConfig.triple_barrier( + upper_barrier=0.02, lower_barrier=0.01, max_holding_period=20, +) +labels = triple_barrier_labels(features, config=config) +``` + +This produces standardized label columns such as `label`, `label_return`, and +`barrier_hit` for supervised learning workflows. + +### 2. Build train/test data without leakage -# Load OHLCV data -df = pl.read_parquet("ohlcv.parquet") +You have features and labels. You need train/test splits with train-only scaling: -# Compute features with a single function call -result = compute_features(df, ["rsi", "macd", "atr", "bollinger_bands"]) +```python +from ml4t.engineer import create_dataset_builder + +builder = create_dataset_builder( + features=labels.select(["rsi_14", "macd", "atr_14"]), + labels=labels["label"], + dates=labels["timestamp"], + scaler="robust", +) +X_train, X_test, y_train, y_test = builder.train_test_split(train_size=0.8) ``` -## Feature Categories +This keeps preprocessing statistics on the training window only, which is the default +you want for time-series ML. + +### 3. Use non-time bars when time bars are the wrong abstraction + +You have trade data and need bars tied to market activity instead of clock time: + +```python +from ml4t.engineer.bars import VolumeBarSampler + +sampler = VolumeBarSampler(volume_per_bar=50_000) +volume_bars = sampler.sample(trades_df) +``` + +This turns raw trade prints into OHLCV bars that are easier to use in downstream +feature and labeling pipelines. + +### 4. Preserve memory while making series stationary + +You need a stationary series but do not want to erase signal with first differences: + +```python +from ml4t.engineer.features.fdiff import find_optimal_d, ffdiff + +result = find_optimal_d(df["close"]) +ffd_close = ffdiff(df["close"], d=result["optimal_d"]) +``` + +This is the standard bridge from Chapter 9’s fractional differencing workflow to a +reusable production transform. + +## Documentation Entry Points + +- [Quickstart](getting-started/quickstart.md) for a working feature and labeling run +- [Features](user-guide/features.md) for the core computation API +- [Labeling](user-guide/labeling.md) for supervised targets and sample weighting +- [Book Guide](book-guide/index.md) for chapter, notebook, and case-study mapping +- [API Reference](api/index.md) for exact interfaces + +## Feature Catalog + +Use this as reference once you know what kind of signal you want to build. | Category | Count | Examples | |----------|-------|----------| @@ -37,6 +145,7 @@ result = compute_features(df, ["rsi", "macd", "atr", "bollinger_bands"]) | Statistics | 14 | Variance, Linear Regression, Correlation | | ML | 14 | Fractional Diff, Entropy, Lag features | | Trend | 10 | SMA, EMA, WMA, DEMA, TEMA, KAMA | +| Cross-Asset | 10 | Beta, Correlation, Cointegration | | Risk | 6 | Max Drawdown, Sortino, CVaR | | Price Transform | 5 | Typical Price, Weighted Close | | Regime | 4 | Hurst Exponent, Choppiness Index | @@ -49,27 +158,15 @@ result = compute_features(df, ["rsi", "macd", "atr", "bollinger_bands"]) pip install ml4t-engineer ``` -## Next Steps - -- [Installation Guide](getting-started/installation.md) - Detailed setup instructions -- [Quickstart](getting-started/quickstart.md) - Get running in 5 minutes - -### User Guides - -- [Features](user-guide/features.md) - 120 technical indicators across 11 categories -- [Labeling](user-guide/labeling.md) - 7 labeling methods for supervised learning -- [Alternative Bars](user-guide/bars.md) - Information-driven bar sampling -- [Feature Discovery](user-guide/discovery.md) - Registry, catalog, and search -- [Fractional Differencing](user-guide/fractional-differencing.md) - Memory-preserving stationarity -- [ML-Readiness](user-guide/ml-readiness.md) - Normalized features and preprocessing -- [Preprocessing](user-guide/preprocessing.md) - Scalers and leakage prevention -- [Dataset Builder](user-guide/dataset-builder.md) - Leakage-safe train/test preparation -- [API Reference](api/index.md) - Complete API documentation +If you use `uv`, `uv pip install ml4t-engineer` is equivalent. See +[Installation](getting-started/installation.md) for environment details and optional +TA-Lib setup. ## Part of the ML4T Library Suite -ML4T Engineer integrates seamlessly with other ML4T libraries: - -``` +```text ml4t-data → ml4t-engineer → ml4t-diagnostic → ml4t-backtest → ml4t-live ``` + +`ml4t-engineer` is where raw market data becomes reusable research inputs: features, +labels, alternative bars, and leakage-safe training datasets. diff --git a/docs/user-guide/bars.md b/docs/user-guide/bars.md index 52b0615..bc3c78c 100644 --- a/docs/user-guide/bars.md +++ b/docs/user-guide/bars.md @@ -2,8 +2,14 @@ Transform tick data into information-driven bars instead of time-based bars. +Use this page when you want to replace time bars with sampling schemes that better +match market activity and microstructure dynamics. + > **Book**: *ML for Trading, 3rd ed.* — Ch3 `08_itch_bar_sampling.py` constructs tick, volume, and dollar bars from ITCH trade data. `10_itch_information_bars.py` builds imbalance bars with threshold analysis. `13_databento_bar_sampling.py` demonstrates bar sampling on Databento data. +Use the [Book Guide](../book-guide/index.md) for the chapter-level map from the +microstructure notebooks to the production sampler classes. + ## Why Alternative Bars? Time bars (1min, 1h, daily) have problems: @@ -399,6 +405,19 @@ inner loops for optimal performance. For very large datasets, consider: 2. Using the streaming API for out-of-memory datasets 3. Caching computed bars rather than recomputing +## See It In The Book + +- Ch3 `08_itch_bar_sampling.py` for tick, volume, and dollar bars +- Ch3 `10_itch_information_bars.py` for imbalance-bar intuition and diagnostics +- Ch3 `13_databento_bar_sampling.py` for a modern market-data workflow +- [Book Guide](../book-guide/index.md) for the full chapter-to-API map + +## Next Steps + +- Read [Features](features.md) if bar outputs feed a feature pipeline next. +- Read [Labeling](labeling.md) if you are constructing labels on bar data. +- Use the [API Reference](../api/index.md) for the full sampler surface. + ## References - López de Prado, M. (2018). *Advances in Financial Machine Learning*. diff --git a/docs/user-guide/dataset-builder.md b/docs/user-guide/dataset-builder.md index 5c349cb..03d18bc 100644 --- a/docs/user-guide/dataset-builder.md +++ b/docs/user-guide/dataset-builder.md @@ -2,8 +2,14 @@ `MLDatasetBuilder` provides leakage-safe dataset preparation for ML training. It handles train/test splitting, automatic scaling (fit on train only), and cross-validation integration with proper fold-level preprocessing. +Use this page when you already have features and labels and want a reusable bridge +from engineered data to train/test or cross-validation folds. + > **Book**: *ML for Trading, 3rd ed.* — Ch7 `10_ml4t_library_ecosystem.py` demonstrates `MLDatasetBuilder` with triple-barrier labels: features + labels in, scaled train/test split out. Ch7 `02_preprocessing_pipeline.py` covers the underlying preprocessing concepts. +Use the [Book Guide](../book-guide/index.md) if you want the full bridge from the +Chapter 7 teaching notebooks to reusable dataset workflows in the library. + ## Basic Usage ```python @@ -201,3 +207,16 @@ builder = create_dataset_builder( # 4. Train/test split with automatic scaling X_train, X_test, y_train, y_test = builder.train_test_split(train_size=0.8) ``` + +## See It In The Book + +- Ch7 `10_ml4t_library_ecosystem.py` for the end-to-end dataset-builder workflow +- Ch7 `02_preprocessing_pipeline.py` for the preprocessing logic that underpins it +- [Book Guide](../book-guide/index.md) for the surrounding chapter and case-study map + +## Next Steps + +- Read [Preprocessing](preprocessing.md) for scaler behavior and transform choices. +- Read [Labeling](labeling.md) if you still need to create supervised targets. +- Use the [API Reference](../api/index.md) when you need exact builder objects and + module paths. diff --git a/docs/user-guide/discovery.md b/docs/user-guide/discovery.md index 100bd9c..498c608 100644 --- a/docs/user-guide/discovery.md +++ b/docs/user-guide/discovery.md @@ -2,6 +2,13 @@ ML4T Engineer provides two complementary discovery APIs: the **Feature Registry** for programmatic metadata access, and the **Feature Catalog** for interactive exploration with filtering and search. +If you are arriving from Ch7 `10_ml4t_library_ecosystem.py`, the +[Book Guide](../book-guide/index.md) shows where discovery fits relative to feature +computation, labeling, and dataset preparation. + +Use this page when you are choosing features, validating metadata, or building +registry-driven workflows instead of hardcoding indicator names. + ## Feature Registry The registry is the metadata backbone — every feature registers its name, category, parameters, input requirements, and validation status. @@ -159,4 +166,14 @@ feature_catalog.tags() | `tags` | `list[str]` | Searchable tags | | `lookback` | `Callable` | Function returning minimum lookback period | -> **Book**: *ML for Trading, 3rd ed.* — Ch7 `10_ml4t_library_ecosystem.py` explores the registry metadata for RSI, ATR, and Garman-Klass, then demonstrates `feature_catalog.search()` and filtered listing. +## See It In The Book + +- Ch7 `10_ml4t_library_ecosystem.py` for registry inspection and catalog search +- [Book Guide](../book-guide/index.md) for how discovery connects to feature + computation, labeling, and dataset preparation + +## Next Steps + +- Read [Features](features.md) to turn chosen metadata into actual feature pipelines. +- Read [ML Readiness](ml-readiness.md) if feature selection depends on normalization. +- Use the [API Reference](../api/index.md) for exact object locations. diff --git a/docs/user-guide/features.md b/docs/user-guide/features.md index 7a2ebf2..058296a 100644 --- a/docs/user-guide/features.md +++ b/docs/user-guide/features.md @@ -21,6 +21,9 @@ ML4T Engineer provides 120 technical indicators across 11 categories, built on P > **Book**: *ML for Trading, 3rd ed.* — Ch8 notebooks (`01_price_volume_features.py` through `04_fundamentals_macro_calendar.py`) build features manually to explain the economics. Case studies (ETFs, US Equities Panel, CME Futures) then use `compute_features()` in production pipelines. +Use the [Book Guide](../book-guide/index.md) for the full notebook-to-API map +across Chapters 7-9 and the case studies. + ## Computation API `compute_features()` accepts three input formats: @@ -384,6 +387,24 @@ Invalid parameters raise `ValueError` with the valid parameter names. - **TA-Lib parity**: RSI computed at ~1x TA-Lib speed via Polars native implementation - **Dependency ordering**: `compute_features` resolves feature dependencies via topological sort +## See It In The Book + +- Ch8 `01_price_volume_features.py` through `04_fundamentals_macro_calendar.py` for + the main feature-engineering concepts +- Ch7 `10_ml4t_library_ecosystem.py` for the config-driven `compute_features` API +- Case-study `03_features.py` workflows for production usage +- [Book Guide](../book-guide/index.md) for the full chapter and case-study map + +## Next Steps + +- Read [Feature Discovery](discovery.md) to choose features through metadata and + search instead of hardcoding. +- Read [ML Readiness](ml-readiness.md) to separate normalized from non-normalized + outputs. +- Read [Dataset Builder](dataset-builder.md) when features are ready to move into + training workflows. +- Use the [API Reference](../api/index.md) for exact function and module locations. + ## References - Lopez de Prado, M. (2018). *Advances in Financial Machine Learning*. Wiley. diff --git a/docs/user-guide/fractional-differencing.md b/docs/user-guide/fractional-differencing.md index de1840a..6b7c224 100644 --- a/docs/user-guide/fractional-differencing.md +++ b/docs/user-guide/fractional-differencing.md @@ -2,6 +2,12 @@ Fractional differencing (FFD) produces stationary time series while preserving long-range memory — the key insight from De Prado (2018, Chapter 5). Standard first-differencing (d=1) achieves stationarity but destroys predictive signal; fractional differencing finds the minimum d that passes stationarity tests. +Use the [Book Guide](../book-guide/index.md) for the surrounding Chapter 9 workflow +and the case-study files that use FFD in production pipelines. + +Use this page when you need stationary inputs but do not want to destroy the +predictive structure in price series with full differencing. + ## The Memory-Stationarity Tradeoff | Differencing Degree | Stationarity | Memory Preserved | ML Utility | @@ -180,7 +186,18 @@ for symbol in ["SPY", "QQQ", "IWM"]: - Weight truncation via `threshold` limits computation window - Typical: 383 lines of implementation for the full module -> **Book**: *ML for Trading, 3rd ed.* — Ch9 `03_fractional_differencing.py` applies FFD to equity data with ADF test validation and memory-stationarity analysis. The ETFs and US Equities Panel case studies use FFD in their `04_temporal.py` notebooks as a standard feature preparation step. +## See It In The Book + +- Ch9 `03_fractional_differencing.py` for the memory-stationarity tradeoff +- ETFs and US Equities Panel `04_temporal.py` workflows for production usage +- [Book Guide](../book-guide/index.md) for the full chapter and case-study map + +## Next Steps + +- Read [Features](features.md) to combine FFD with the broader feature pipeline. +- Read [Dataset Builder](dataset-builder.md) if the transformed series feed model + training workflows next. +- Use the [API Reference](../api/index.md) for exact function locations. ## References diff --git a/docs/user-guide/labeling.md b/docs/user-guide/labeling.md index 208f24f..b189109 100644 --- a/docs/user-guide/labeling.md +++ b/docs/user-guide/labeling.md @@ -18,6 +18,9 @@ All methods return a Polars DataFrame with standardized output columns. Performa > **Book**: *ML for Trading, 3rd ed.* — Ch7 `03_label_methods.py` walks through all 7 methods on real ETF data with visualizations. All case study `02_labels.py` notebooks apply these methods in production pipelines. +Use the [Book Guide](../book-guide/index.md) for the broader mapping from Chapter 7 +and case-study `02_labels.py` files to the production labeling APIs. + ## Choosing a Method ``` @@ -515,6 +518,22 @@ Time-based horizons require a `timestamp_col` in the input DataFrame. 5. **Time-based horizons for irregular data**: If your bars are not equally spaced (e.g., volume bars), use duration strings (`"4h"`) instead of bar counts to ensure consistent holding periods. +## See It In The Book + +- Ch7 `03_label_methods.py` for the full comparison of labeling methods +- Ch7 `04_minimum_favorable_adverse_excursion.py` for barrier behavior analysis +- Case-study `02_labels.py` workflows, especially CME Futures for ATR barriers +- [Book Guide](../book-guide/index.md) for the full chapter and case-study map + +## Next Steps + +- Read [Dataset Builder](dataset-builder.md) when labeled data moves into training + and CV workflows. +- Read [Preprocessing](preprocessing.md) if labels are part of a broader feature + preparation pipeline. +- Use the [API Reference](../api/index.md) for the full labeling surface and config + objects. + ## References - Lopez de Prado, M. (2018). *Advances in Financial Machine Learning*. Wiley. Chapters 3-4. diff --git a/docs/user-guide/ml-readiness.md b/docs/user-guide/ml-readiness.md index b715b32..398177b 100644 --- a/docs/user-guide/ml-readiness.md +++ b/docs/user-guide/ml-readiness.md @@ -4,6 +4,9 @@ This guide explains the `normalized` field in feature metadata and how to prepar > **Book**: *ML for Trading, 3rd ed.* — Ch8 `01_price_volume_features.py` compares normalized vs non-normalized features on real ETF data, including preprocessing strategies for each type. +Use this page when you need to decide which features can go straight into a model and +which ones should pass through a preprocessing step first. + ## Overview Features in ml4t-engineer have a `normalized` metadata field that indicates whether a feature produces bounded outputs suitable for direct ML consumption: @@ -176,3 +179,13 @@ result = result.with_columns( - See [API Reference](../api/index.md) for full metadata API - See [Preprocessing](preprocessing.md) for scaler documentation +- See the [Book Guide](../book-guide/index.md) for the broader chapter and case-study + map + +## Next Steps + +- Read [Preprocessing](preprocessing.md) to scale the non-normalized features. +- Read [Feature Discovery](discovery.md) to filter features by metadata before model + building. +- Read [Dataset Builder](dataset-builder.md) for train/test splitting and fold-aware + scaling. diff --git a/docs/user-guide/preprocessing.md b/docs/user-guide/preprocessing.md index 3e59efa..ec6c8aa 100644 --- a/docs/user-guide/preprocessing.md +++ b/docs/user-guide/preprocessing.md @@ -2,6 +2,9 @@ ML4T Engineer provides sklearn-compatible scalers built on Polars for leakage-safe feature preprocessing. +Use this page when your engineered features are not yet on model-friendly scales or +when you need train-only transforms without leakage. + ## Scalers All scalers follow the sklearn pattern: `fit()` on training data, `transform()` on any data. This prevents information leakage from test data into training. @@ -168,4 +171,15 @@ builder = create_dataset_builder( X_train, X_test, y_train, y_test = builder.train_test_split(train_size=0.8) ``` -> **Book**: *ML for Trading, 3rd ed.* — Ch7 `02_preprocessing_pipeline.py` demonstrates split-aware preprocessing with StandardScaler in a feature preparation pipeline. See also the [ML-Readiness guide](ml-readiness.md) for which features need preprocessing vs. which are ML-ready out of the box. +## See It In The Book + +- Ch7 `02_preprocessing_pipeline.py` for split-aware preprocessing +- [ML Readiness](ml-readiness.md) for deciding which features need scaling first +- [Book Guide](../book-guide/index.md) for the full Chapter 7 workflow map + +## Next Steps + +- Read [Dataset Builder](dataset-builder.md) for the end-to-end training-data workflow. +- Read [ML Readiness](ml-readiness.md) to separate normalized and non-normalized + features. +- Use the [API Reference](../api/index.md) for exact scaler and pipeline objects. diff --git a/mkdocs.yml b/mkdocs.yml index 264accc..478acdd 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -7,35 +7,23 @@ site_url: https://ml4trading.io/docs/engineer/ repo_url: https://github.com/ml4t/engineer repo_name: ml4t/engineer -# Theme configuration - Picasso Blue Period palette +# Theme configuration — shared ml4t-docs-theme for website integration theme: - name: material - custom_dir: docs/overrides + name: ml4t-docs-theme palette: - - media: "(prefers-color-scheme: light)" - scheme: ml4t - toggle: - icon: material/brightness-7 - name: Switch to dark mode - - media: "(prefers-color-scheme: dark)" - scheme: slate - primary: custom - accent: amber - toggle: - icon: material/brightness-4 - name: Switch to light mode + scheme: default + primary: custom + accent: custom - font: - text: DM Sans - code: JetBrains Mono + font: false features: - navigation.tabs - navigation.tabs.sticky + - navigation.indexes - navigation.sections - navigation.expand - - navigation.path - navigation.top - navigation.footer - toc.follow @@ -48,10 +36,6 @@ theme: icon: repo: fontawesome/brands/github -# Custom CSS for Picasso palette -extra_css: - - stylesheets/ml4t-theme.css - # Extensions markdown_extensions: - abbr @@ -128,6 +112,8 @@ nav: - ML Readiness: user-guide/ml-readiness.md - API Reference: - api/index.md + - Book Guide: + - book-guide/index.md # Extra configuration extra: diff --git a/pyproject.toml b/pyproject.toml index 5d6ce60..7ff26aa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,8 +14,8 @@ version-file = "src/ml4t/engineer/_version.py" [tool.hatch.build.targets.wheel] packages = ["src/ml4t"] namespaces = true -# Include AGENT.md files for agent navigation -artifacts = ["src/ml4t/**/AGENT.md"] +# Include AGENTS.md files for agent navigation +artifacts = ["src/ml4t/**/AGENTS.md"] [tool.hatch.build.targets.sdist] include = [ @@ -71,7 +71,7 @@ requires-python = ">=3.12" # Core dependencies dependencies = [ - "ml4t-data>=0.1.0b7", + "ml4t-specs>=0.1.0b0", # Data processing (Polars-first) "polars>=0.20.0", @@ -291,4 +291,4 @@ exclude_lines = [ ] [tool.uv.sources] -ml4t-data = { path = "../ml4t-data", editable = true } +ml4t-specs = { path = "../ml4t-specs", editable = true } diff --git a/src/ml4t/engineer/AGENT.md b/src/ml4t/engineer/AGENT.md deleted file mode 100644 index ad638f1..0000000 --- a/src/ml4t/engineer/AGENT.md +++ /dev/null @@ -1,101 +0,0 @@ -# ml4t.engineer Package - -Package-level navigation for feature engineering library. - -## Top-Level Modules - -| Module | Lines | Purpose | Key Functions | -|--------|-------|---------|---------------| -| `api.py` | 445 | Main API | `compute_features()`, `list_features()`, `describe_feature()` | -| `preprocessing.py` | 800 | Scalers, transforms | `StandardScaler`, `RobustScaler`, `Preprocessor` | -| `dataset.py` | 630 | Dataset creation | `FeatureDataset`, `create_dataset()` | -| `__init__.py` | - | Package exports | Re-exports from api.py | - -## Subdirectories - -| Directory | Purpose | AGENT.md | -|-----------|---------|----------| -| `features/` | 120 technical indicators (10 categories) | [features/AGENT.md](features/AGENT.md) | -| `labeling/` | ML label generation methods | [labeling/AGENT.md](labeling/AGENT.md) | -| `bars/` | Alternative bar sampling | [bars/AGENT.md](bars/AGENT.md) | -| `core/` | Registry, types, validation, decorators | See below | -| `config/` | Pydantic v2 configurations | See below | -| `selection/` | Feature selection methods | See below | -| `store/` | DuckDB feature storage | See below | -| `validation/` | Cross-validation utilities | See below | -| `visualization/` | Plot export utilities | See below | - -## core/ - Foundation - -| File | Purpose | Key Exports | -|------|---------|-------------| -| `registry.py` | Feature metadata registry | `FeatureRegistry`, `FeatureMetadata`, `get_registry()` | -| `decorators.py` | @feature decorator | `feature()` | -| `types.py` | Type definitions | `PriceDataFrame`, `FeatureSpec` | -| `schemas.py` | Polars schemas | `OHLCV_SCHEMA` | -| `validation.py` | Input validation | `validate_ohlcv()`, `validate_window()` | -| `exceptions.py` | Custom exceptions | `DataValidationError`, `InvalidParameterError` | - -## config/ - Configurations - -| File | Purpose | Key Classes | -|------|---------|-------------| -| `feature_config.py` | Feature specifications | `FeatureConfig`, `FeatureSpec` | -| `labeling.py` | Labeling config | `LabelingConfig` | -| `preprocessing_config.py` | Scaler config | `PreprocessingConfig`, `ScalerConfig` | -| `base.py` | Base config classes | `BaseConfig` | - -## selection/ - Deprecated (Moved to ml4t-diagnostic) - -FeatureSelector has moved to `ml4t.diagnostic.selection`. - -## store/ - Storage - -| File | Purpose | -|------|---------| -| `offline.py` | DuckDB-based feature storage | - -## Common Patterns - -### Adding a New Feature - -```python -from ml4t.engineer.core.decorators import feature - -@feature(name="my_indicator", category="momentum") -def my_indicator(data: pl.DataFrame, period: int = 14) -> pl.DataFrame: - """One-line description.""" - # Implementation using Polars expressions - return data.with_columns(result.alias(f"my_indicator_{period}")) -``` - -### Using Registry - -```python -from ml4t.engineer.core.registry import get_registry - -registry = get_registry() -all_features = registry.list_features() -momentum = registry.list_features(category="momentum") -metadata = registry.get("rsi") -``` - -## File Size Tiers (for refactoring reference) - -**Very Large (>1000 lines)** - Consider splitting: -- None currently in active labeling public surface (legacy `labeling/core.py` removed) - -**Large (500-1000 lines)**: -- `config/feature_config.py`, `preprocessing.py`, `features/risk.py` -- `selection/systematic.py`, `features/ml/rolling_entropy.py` -- `bars/run.py`, `features/cross_asset.py`, `dataset.py` - -## Note: Feature Outcome Analysis - -IC analysis, importance scoring, and drift detection have moved to ``ml4t-diagnostic``. -Install with: ``pip install ml4t-diagnostic`` - -```python -from ml4t.diagnostic.evaluation import FeatureOutcome, analyze_drift -from ml4t.diagnostic.visualization import plot_importance_summary -``` diff --git a/src/ml4t/engineer/AGENTS.md b/src/ml4t/engineer/AGENTS.md new file mode 100644 index 0000000..061071e --- /dev/null +++ b/src/ml4t/engineer/AGENTS.md @@ -0,0 +1,83 @@ +# ml4t.engineer Package + +Package-level navigation for the public `ml4t-engineer` surface. + +## Main Modules + +| Module | Purpose | Key Exports | +|--------|---------|-------------| +| `api.py` | Feature-computation entry point | `compute_features()` | +| `dataset.py` | Leakage-safe dataset preparation | `MLDatasetBuilder`, `create_dataset_builder()` | +| `preprocessing.py` | Train-only scalers and transform pipelines | `StandardScaler`, `MinMaxScaler`, `RobustScaler`, `PreprocessingPipeline` | +| `discovery/catalog.py` | Metadata-driven feature exploration | `FeatureCatalog`, `feature_catalog` | +| `config/` | Reusable config models | `LabelingConfig`, `PreprocessingConfig`, `DataContractConfig` | +| `__init__.py` | Public re-exports and AGENTS discovery helper | `get_agent_docs()` | + +## Subdirectories + +| Directory | Purpose | AGENTS | +|-----------|---------|--------| +| `features/` | Registry-backed indicator implementations and standalone feature helpers | [features/AGENTS.md](features/AGENTS.md) | +| `labeling/` | Barrier labels, percentile labels, meta-labeling, uniqueness | [labeling/AGENTS.md](labeling/AGENTS.md) | +| `bars/` | Tick, volume, dollar, imbalance, and run bars | [bars/AGENTS.md](bars/AGENTS.md) | +| `core/` | Registry, metadata, schemas, decorators, validation | [core/AGENTS.md](core/AGENTS.md) | +| `config/` | Pydantic configuration models and schema bridges | [config/AGENTS.md](config/AGENTS.md) | +| `discovery/` | Metadata-driven feature search and filtering | [discovery/AGENTS.md](discovery/AGENTS.md) | +| `relationships/` | Correlation helpers and plotting utilities | [relationships/AGENTS.md](relationships/AGENTS.md) | +| `store/` | Offline DuckDB storage helpers | [store/AGENTS.md](store/AGENTS.md) | +| `artifacts/` | Lightweight artifact records for features, labels, predictions | [artifacts/AGENTS.md](artifacts/AGENTS.md) | +| `logging/` | Structured logging configuration | [logging/AGENTS.md](logging/AGENTS.md) | +| `utils/` | optional dependency helpers and low-level utilities | [utils/AGENTS.md](utils/AGENTS.md) | + +## Current Public API Shape + +```python +from ml4t.engineer import ( + compute_features, + create_dataset_builder, + feature_catalog, + MLDatasetBuilder, + StandardScaler, + RobustScaler, +) +from ml4t.engineer.config import LabelingConfig, PreprocessingConfig +from ml4t.engineer.labeling import triple_barrier_labels, atr_triple_barrier_labels +from ml4t.engineer.bars import TickBarSampler, VolumeBarSampler, DollarBarSampler +``` + +## Core Patterns + +### Compute features through the registry + +```python +from ml4t.engineer import compute_features + +result = compute_features(df, ["rsi", "macd", "atr"]) +``` + +### Discover features through metadata + +```python +from ml4t.engineer import feature_catalog + +feature_catalog.list(category="momentum") +feature_catalog.search("volatility estimator") +feature_catalog.describe("rsi") +``` + +### Build train/test data without leakage + +```python +from ml4t.engineer import create_dataset_builder + +builder = create_dataset_builder(features, labels, scaler="robust") +X_train, X_test, y_train, y_test = builder.train_test_split(train_size=0.8) +``` + +## Notes + +- `FeatureSelector` has moved out of this library and belongs in `ml4t-diagnostic` +- `store/` exists but is lower-priority than the core feature, labeling, bar, and + dataset workflows +- AGENTS files are the current navigation surface; older singular filename references + should be treated as stale diff --git a/src/ml4t/engineer/__init__.py b/src/ml4t/engineer/__init__.py index 2839815..060e0ec 100644 --- a/src/ml4t/engineer/__init__.py +++ b/src/ml4t/engineer/__init__.py @@ -5,9 +5,9 @@ for financial machine learning models. Agent Navigation: - This package includes AGENT.md files for AI agent navigation. + This package includes AGENTS.md files for AI agent navigation. Call `get_agent_docs()` to get paths to all documentation files. - Start with the root AGENT.md for package overview and navigation. + Start with the root AGENTS.md for package overview and navigation. """ from importlib.metadata import PackageNotFoundError as _PackageNotFoundError @@ -54,7 +54,7 @@ def get_agent_docs() -> dict[str, _Path]: - """Get paths to AGENT.md documentation files for AI agent navigation. + """Get paths to AGENTS.md documentation files for AI agent navigation. Returns a dict mapping logical names to file paths. Start with 'root' for package overview, then drill into specific areas as needed. @@ -68,6 +68,11 @@ def get_agent_docs() -> dict[str, _Path]: - 'features/{category}': Category-specific signatures - 'labeling': ML label generation methods - 'bars': Alternative bar sampling + - 'core', 'core/calendars': Registry and validation internals + - 'config': Reusable configuration models + - 'discovery': Metadata-driven feature search + - 'artifacts', 'relationships', 'store', 'logging', 'utils': Secondary + package areas with their own navigation guides Example ------- @@ -76,24 +81,37 @@ def get_agent_docs() -> dict[str, _Path]: >>> print(docs['root'].read_text()[:200]) # Read overview """ pkg_dir = _Path(__file__).parent - docs = {} + docs: dict[str, _Path] = {} # Root and package-level - if (p := pkg_dir / "AGENT.md").exists(): + if (p := pkg_dir / "AGENTS.md").exists(): docs["root"] = p # Features index and categories features_dir = pkg_dir / "features" - if (p := features_dir / "AGENT.md").exists(): + if (p := features_dir / "AGENTS.md").exists(): docs["features"] = p for category_dir in features_dir.iterdir(): - if category_dir.is_dir() and (p := category_dir / "AGENT.md").exists(): + if category_dir.is_dir() and (p := category_dir / "AGENTS.md").exists(): docs[f"features/{category_dir.name}"] = p # Other modules - for module in ["labeling", "bars"]: - if (p := pkg_dir / module / "AGENT.md").exists(): + for module in [ + "labeling", + "bars", + "core", + "config", + "discovery", + "artifacts", + "relationships", + "store", + "logging", + "utils", + ]: + if (p := pkg_dir / module / "AGENTS.md").exists(): docs[module] = p + if (p := pkg_dir / "core" / "calendars" / "AGENTS.md").exists(): + docs["core/calendars"] = p return docs diff --git a/src/ml4t/engineer/artifacts/AGENTS.md b/src/ml4t/engineer/artifacts/AGENTS.md new file mode 100644 index 0000000..da054db --- /dev/null +++ b/src/ml4t/engineer/artifacts/AGENTS.md @@ -0,0 +1,17 @@ +# artifacts/ - Lightweight Artifact Records + +Helpers for representing feature, label, and prediction artifacts in reusable +pipeline workflows. + +## Files + +| File | Purpose | +|------|---------| +| `features.py` | feature artifact record helpers | +| `labels.py` | label artifact record helpers | +| `predictions.py` | prediction artifact record helpers | + +## Notes + +- this directory is a support surface, not the primary public API +- use it when orchestration code needs typed artifact records rather than raw frames diff --git a/src/ml4t/engineer/bars/AGENT.md b/src/ml4t/engineer/bars/AGENT.md deleted file mode 100644 index 80cc035..0000000 --- a/src/ml4t/engineer/bars/AGENT.md +++ /dev/null @@ -1,87 +0,0 @@ -# bars/ - Alternative Bar Sampling - -Information-driven bar types for tick data. Reference: López de Prado (2018), Chapter 2. - -## Time vs Information Bars - -Standard time bars (1-min, daily) sample at fixed intervals regardless of market activity. -Alternative bars sample based on market information flow. - -## Volume Bars - -### volume_bars(ticks, volume_threshold=1000) -> DataFrame -Equal volume per bar. New bar when cumulative volume reaches threshold. -More bars during high activity, fewer during quiet periods. - -### dollar_bars(ticks, dollar_threshold=1_000_000) -> DataFrame -Equal dollar volume per bar. Normalizes for price level changes. -Preferred over volume bars for most applications. - -## Tick Bars - -### tick_bars(ticks, tick_threshold=100) -> DataFrame -Equal number of trades per bar. - -## Information Bars (Imbalance) - -### tick_imbalance_bars(ticks, expected_imbalance=100) -> DataFrame -New bar when cumulative tick imbalance (buy vs sell) exceeds threshold. -Uses tick rule for trade classification. - -### volume_imbalance_bars(ticks, expected_imbalance=10000) -> DataFrame -New bar when cumulative volume imbalance exceeds threshold. - -### dollar_imbalance_bars(ticks, expected_imbalance=1_000_000) -> DataFrame -New bar when cumulative dollar imbalance exceeds threshold. - -## Run Bars - -### tick_run_bars(ticks, expected_run=50) -> DataFrame -New bar when run length (consecutive same-direction trades) exceeds threshold. - -### volume_run_bars(ticks, expected_run=5000) -> DataFrame -Volume-weighted run bars. - -### dollar_run_bars(ticks, expected_run=500_000) -> DataFrame -Dollar-weighted run bars. - -## Input Requirements - -All functions expect tick data with columns: -- `datetime`: Timestamp -- `price`: Trade price -- `volume`: Trade size - -## Output Format - -Returns OHLCV DataFrame: -- `datetime`: Bar timestamp (first tick) -- `open`, `high`, `low`, `close`: Price OHLC -- `volume`: Total volume in bar -- `tick_count`: Number of ticks in bar - -## Files Reference - -| File | Lines | Purpose | -|------|-------|---------| -| `base.py` | - | Base sampler interface | -| `vectorized.py` | 557 | Vectorized implementations | -| `volume.py` | - | Volume/dollar bars | -| `tick.py` | - | Tick bars | -| `imbalance.py` | - | Imbalance bars | -| `run.py` | 673 | Run bars | - -## Example - -```python -from ml4t.engineer.bars import dollar_bars, tick_imbalance_bars - -# Load tick data -ticks = pl.read_parquet("ticks.parquet") - -# Create dollar bars -bars = dollar_bars(ticks, dollar_threshold=1_000_000) - -# Create information bars -info_bars = tick_imbalance_bars(ticks, expected_imbalance=100) -``` diff --git a/src/ml4t/engineer/bars/AGENTS.md b/src/ml4t/engineer/bars/AGENTS.md new file mode 100644 index 0000000..8774f9a --- /dev/null +++ b/src/ml4t/engineer/bars/AGENTS.md @@ -0,0 +1,72 @@ +# bars/ - Alternative Bar Sampling + +Information-driven bar samplers for converting trade-level data into OHLCV bars that +track market activity instead of clock time. + +## Public Samplers + +### Standard event-driven bars + +- `TickBarSampler` +- `VolumeBarSampler` +- `DollarBarSampler` + +### Adaptive or information-driven bars + +- `ImbalanceBarSampler` for volume-imbalance bars +- `TickImbalanceBarSampler` +- `FixedTickImbalanceBarSampler` +- `FixedVolumeImbalanceBarSampler` +- `WindowTickImbalanceBarSampler` +- `WindowVolumeImbalanceBarSampler` + +### Run bars + +- `TickRunBarSampler` +- `VolumeRunBarSampler` +- `DollarRunBarSampler` + +## Recommended Usage + +- production: fixed-threshold imbalance bars for stable behavior +- research: window-based or adaptive imbalance bars when threshold adaptation matters +- baseline comparisons: tick, volume, and dollar bars + +## Input Contract + +All samplers expect trade-level data with at least: + +- `timestamp` +- `price` +- `volume` + +Imbalance and run bars also require side information or a trade-classification path +that the sampler can derive. + +## Files Reference + +| File | Purpose | +|------|---------| +| `base.py` | common sampler interface | +| `vectorized.py` | vectorized default implementations | +| `tick.py` | tick-bar implementations | +| `volume.py` | volume and dollar bars | +| `imbalance.py` | imbalance and fixed-threshold samplers | +| `run.py` | run-bar implementations | + +## Example + +```python +from ml4t.engineer.bars import ( + VolumeBarSampler, + FixedTickImbalanceBarSampler, +) + +volume_bars = VolumeBarSampler(volume_per_bar=50_000).sample(trades_df) +info_bars = FixedTickImbalanceBarSampler(threshold=100).sample(trades_df) +``` + +## Reference + +See `docs/user-guide/bars.md` for the current production guidance and the tradeoffs +between adaptive, fixed-threshold, and window-based approaches. diff --git a/src/ml4t/engineer/config/AGENTS.md b/src/ml4t/engineer/config/AGENTS.md new file mode 100644 index 0000000..eadb908 --- /dev/null +++ b/src/ml4t/engineer/config/AGENTS.md @@ -0,0 +1,26 @@ +# config/ - Reusable Configuration Models + +Pydantic-based configuration objects for labeling, preprocessing, experiments, and +data contracts. + +## Main Files + +| File | Purpose | Key Objects | +|------|---------|-------------| +| `labeling.py` | barrier and horizon labeling configs | `LabelingConfig` | +| `preprocessing_config.py` | scaler and preprocessing configs | `PreprocessingConfig` | +| `data_contract.py` | schema and column-contract mapping | `DataContractConfig` | +| `experiment.py` | experiment persistence and loading | `ExperimentConfig` | +| `spec_bridge.py` | bridge from market-data specs into contracts | `data_contract_from_market_data_spec()` | +| `base.py` | shared config primitives | `BaseConfig`, `ComputationalConfig` | + +## Typical Usage + +```python +from ml4t.engineer.config import LabelingConfig, PreprocessingConfig + +label_cfg = LabelingConfig.triple_barrier( + upper_barrier=0.02, lower_barrier=0.01, max_holding_period=20, +) +prep_cfg = PreprocessingConfig.robust() +``` diff --git a/src/ml4t/engineer/core/AGENTS.md b/src/ml4t/engineer/core/AGENTS.md new file mode 100644 index 0000000..006630f --- /dev/null +++ b/src/ml4t/engineer/core/AGENTS.md @@ -0,0 +1,22 @@ +# core/ - Registry, Metadata, and Validation + +The internal foundation of `ml4t-engineer`. + +## Files + +| File | Purpose | +|------|---------| +| `registry.py` | feature metadata registry and lookup helpers | +| `decorators.py` | `@feature` registration decorator | +| `schemas.py` | canonical schemas and column expectations | +| `types.py` | shared type aliases and protocol-like definitions | +| `validation.py` | runtime validation helpers | +| `exceptions.py` | custom exception types | + +## Notes + +- this directory underpins `compute_features()` and `feature_catalog` +- it is mostly an implementation surface, but useful when debugging registration, + lookback metadata, or input validation + +See [calendars/AGENTS.md](calendars/AGENTS.md) for the nested trading-calendar helpers. diff --git a/src/ml4t/engineer/core/calendars/AGENTS.md b/src/ml4t/engineer/core/calendars/AGENTS.md new file mode 100644 index 0000000..8962e69 --- /dev/null +++ b/src/ml4t/engineer/core/calendars/AGENTS.md @@ -0,0 +1,16 @@ +# calendars/ - Trading Calendar Helpers + +Calendar utilities for session-aware workflows. + +## Files + +| File | Purpose | +|------|---------| +| `base.py` | common calendar interfaces | +| `equity.py` | equity-market calendar behavior | +| `crypto.py` | always-open crypto calendar behavior | + +## Notes + +- these helpers matter when labeling or horizon logic depends on trading sessions +- public calendar-facing APIs are surfaced mainly through the labeling module diff --git a/src/ml4t/engineer/discovery/AGENTS.md b/src/ml4t/engineer/discovery/AGENTS.md new file mode 100644 index 0000000..c228317 --- /dev/null +++ b/src/ml4t/engineer/discovery/AGENTS.md @@ -0,0 +1,19 @@ +# discovery/ - Feature Discovery Surface + +Metadata-driven search and filtering for the feature registry. + +## Files + +| File | Purpose | Key Objects | +|------|---------|-------------| +| `catalog.py` | catalog wrapper around registry metadata | `FeatureCatalog`, `features` | + +## Typical Usage + +```python +from ml4t.engineer import feature_catalog + +feature_catalog.list(category="momentum") +feature_catalog.search("volatility estimator") +feature_catalog.describe("rsi") +``` diff --git a/src/ml4t/engineer/features/AGENT.md b/src/ml4t/engineer/features/AGENT.md deleted file mode 100644 index 73c47b4..0000000 --- a/src/ml4t/engineer/features/AGENT.md +++ /dev/null @@ -1,64 +0,0 @@ -# features/ - Technical Indicators - -120 features in 10 categories. All use `@feature` decorator for registry. - -## Categories - -| Category | Count | Directory | Key Functions | -|----------|-------|-----------|---------------| -| momentum | 31 | `momentum/` | rsi, macd, adx, stoch, cci, mfi, willr, aroon, sar | -| trend | 10 | `trend/` | sma, ema, wma, dema, tema, kama, t3 | -| volatility | 15 | `volatility/` | atr, yang_zhang, bollinger_bands, parkinson, garch | -| microstructure | 12 | `microstructure/` | kyle_lambda, amihud, vpin, roll_spread | -| ml | 11 | `ml/` | lag, cyclical_encode, rolling_entropy, fourier | -| statistics | 8 | `statistics/` | stddev, var, linear_regression, tsf | -| volume | 3 | `volume/` | obv, ad, adosc | -| price_transform | 5 | `price_transform/` | avgprice, typprice, medprice, wclprice | -| math | 3 | `math/` | max_, min_, sum_ (O(n) optimized) | -| regime | 3 | `regime.py` | hurst_exponent, choppiness_index, variance_ratio | - -## Standalone Files - -| File | Lines | Purpose | -|------|-------|---------| -| `risk.py` | 748 | Risk metrics: CVaR, Sortino, max_drawdown, information_ratio | -| `regime.py` | 537 | Regime detection: Hurst, choppiness, fractal efficiency | -| `cross_asset.py` | 654 | Multi-asset: beta_to_market, rolling_correlation | -| `fdiff.py` | 383 | Fractional differentiation: fractional_diff, find_min_d | -| `composite.py` | 312 | Feature combinations | - -## Navigation - -| Category | Detail | -|----------|--------| -| momentum | [momentum/AGENT.md](momentum/AGENT.md) | -| trend | [trend/AGENT.md](trend/AGENT.md) | -| volatility | [volatility/AGENT.md](volatility/AGENT.md) | -| microstructure | [microstructure/AGENT.md](microstructure/AGENT.md) | -| ml | [ml/AGENT.md](ml/AGENT.md) | -| statistics | [statistics/AGENT.md](statistics/AGENT.md) | -| volume | [volume/AGENT.md](volume/AGENT.md) | -| price_transform | [price_transform/AGENT.md](price_transform/AGENT.md) | -| math | [math/AGENT.md](math/AGENT.md) | - -## Common Signature - -All features follow this pattern: - -```python -def feature_name( - data: pl.DataFrame, - period: int = 14, - price_col: str = "close", -) -> pl.DataFrame: - """Returns DataFrame with original data + new column(s).""" -``` - -## TA-Lib Compatibility - -60 features are validated against TA-Lib at 1e-6 tolerance: -- All momentum indicators (RSI, MACD, ADX, STOCH, etc.) -- All trend indicators (SMA, EMA, WMA, etc.) -- Basic volatility (ATR, NATR, Bollinger) -- Volume indicators (OBV, A/D) -- Statistics (STDDEV, VAR, regression family) diff --git a/src/ml4t/engineer/features/AGENTS.md b/src/ml4t/engineer/features/AGENTS.md new file mode 100644 index 0000000..2b0571a --- /dev/null +++ b/src/ml4t/engineer/features/AGENTS.md @@ -0,0 +1,62 @@ +# features/ - Technical Indicators and Feature Helpers + +This directory contains the registry-backed indicator implementations used by +`compute_features()` plus a small set of standalone feature helpers. + +## Categories Registered in `compute_features()` + +| Category | Count | Location | Examples | +|----------|-------|----------|----------| +| momentum | 31 | `momentum/` | `rsi`, `macd`, `stoch`, `adx`, `mfi` | +| trend | 10 | `trend/` | `sma`, `ema`, `wma`, `kama`, `donchian_channels` | +| volatility | 15 | `volatility/` | `atr`, `bollinger_bands`, `yang_zhang_volatility`, `garch_forecast` | +| microstructure | 15 | `microstructure/` | `kyle_lambda`, `amihud_illiquidity`, `realized_spread` | +| ml | 14 | `ml/` plus `fdiff.py` | lag features, entropy, Fourier, percentile-rank features | +| statistics | 14 | `statistics/` | `stddev`, `var`, `linearreg`, `structural_break` | +| volume | 3 | `volume/` | `obv`, `ad`, `adosc` | +| price_transform | 5 | `price_transform/` | `avgprice`, `typprice`, `medprice`, `wclprice`, `midprice` | +| math | 3 | `math/` | rolling `max_`, `min_`, `sum_` | +| regime | 4 | `regime.py` | `hurst_exponent`, `choppiness_index`, `fractal_efficiency` | +| risk | 6 | `risk.py` | drawdown, downside deviation, tail ratio | + +## Standalone Helpers + +These are not registry entries but remain important: + +| File | Purpose | +|------|---------| +| `cross_asset.py` | multi-asset and panel features such as rolling correlation and beta | +| `fdiff.py` | fractional differencing utilities such as `ffdiff()` and `find_optimal_d()` | +| `composite.py` | composite indicators and z-score combinations | + +## Navigation + +| Category | AGENTS | +|----------|--------| +| momentum | [momentum/AGENTS.md](momentum/AGENTS.md) | +| trend | [trend/AGENTS.md](trend/AGENTS.md) | +| volatility | [volatility/AGENTS.md](volatility/AGENTS.md) | +| microstructure | [microstructure/AGENTS.md](microstructure/AGENTS.md) | +| ml | [ml/AGENTS.md](ml/AGENTS.md) | +| statistics | [statistics/AGENTS.md](statistics/AGENTS.md) | +| volume | [volume/AGENTS.md](volume/AGENTS.md) | +| price_transform | [price_transform/AGENTS.md](price_transform/AGENTS.md) | +| math | [math/AGENTS.md](math/AGENTS.md) | + +## Current Usage Pattern + +```python +from ml4t.engineer import compute_features + +result = compute_features(df, [ + "rsi", + {"name": "sma", "params": {"period": 50}}, + {"name": "bollinger_bands", "params": {"period": 20, "std_dev": 2.5}}, +]) +``` + +## Trust Signals + +- 120 total features across 11 categories +- 60 TA-Lib validated indicators at `1e-6` tolerance +- registry metadata supports discovery by category, normalization, and TA-Lib parity diff --git a/src/ml4t/engineer/features/math/AGENT.md b/src/ml4t/engineer/features/math/AGENTS.md similarity index 100% rename from src/ml4t/engineer/features/math/AGENT.md rename to src/ml4t/engineer/features/math/AGENTS.md diff --git a/src/ml4t/engineer/features/microstructure/AGENT.md b/src/ml4t/engineer/features/microstructure/AGENT.md deleted file mode 100644 index 49158c4..0000000 --- a/src/ml4t/engineer/features/microstructure/AGENT.md +++ /dev/null @@ -1,58 +0,0 @@ -# microstructure/ - 12 Market Microstructure Metrics - -Academic market microstructure features. Validated against research papers. - -## Price Impact - -### kyle_lambda(data, period=20) -> DataFrame -Kyle's Lambda. Price impact coefficient. Higher = less liquid. -Reference: Kyle (1985) - -### amihud_illiquidity(data, period=20) -> DataFrame -Amihud illiquidity ratio. |return| / dollar_volume. -Reference: Amihud (2002) - -### hasbrouck_lambda(data, period=20) -> DataFrame -Hasbrouck's Lambda. Alternative price impact measure. - -## Spread Estimation - -### roll_spread(data, period=20) -> DataFrame -Roll implied spread. From serial covariance of returns. -Reference: Roll (1984) - -### corwin_schultz(data) -> DataFrame -Corwin-Schultz spread. From high-low prices. -Reference: Corwin & Schultz (2012) - -### effective_spread(data) -> DataFrame -Effective spread estimate. - -## Information Asymmetry - -### vpin(data, volume_bucket_size=50) -> DataFrame -Volume-Synchronized PIN. Probability of informed trading. -Reference: Easley, López de Prado, O'Hara (2012) - -### order_flow_imbalance(data, period=20) -> DataFrame -Order flow imbalance. Buy vs sell pressure. - -### trade_classification(data) -> DataFrame -Lee-Ready trade classification. Tick rule + quote rule. - -## Volume Analysis - -### volume_weighted_price_momentum(data, period=20) -> DataFrame -VWAP-based momentum. - -### realized_spread(data, period=20) -> DataFrame -Realized spread from price reversals. - -### price_impact_ratio(data, period=20) -> DataFrame -Price impact per unit volume. - -## Common Parameters - -- `data`: pl.DataFrame with OHLCV columns -- `period`: Rolling window for estimation -- All require volume data for meaningful results diff --git a/src/ml4t/engineer/features/microstructure/AGENTS.md b/src/ml4t/engineer/features/microstructure/AGENTS.md new file mode 100644 index 0000000..b7de140 --- /dev/null +++ b/src/ml4t/engineer/features/microstructure/AGENTS.md @@ -0,0 +1,37 @@ +# microstructure/ - 15 Market Microstructure Features + +Microstructure features for liquidity, spread estimation, order flow, and trade +intensity. These features are most useful when the research question depends on +market quality rather than just price direction. + +## Current Modules + +| Module | Purpose | +|--------|---------| +| `kyle_lambda.py` | Kyle price-impact coefficient | +| `amihud_illiquidity.py` | illiquidity from return per dollar volume | +| `roll_spread_estimator.py` | spread estimation from return covariance | +| `realized_spread.py` | realized spread and reversal-based cost proxy | +| `effective_tick_rule.py` | trade-sign inference from price moves | +| `order_flow_imbalance.py` | buy/sell pressure imbalance | +| `price_impact_ratio.py` | impact relative to traded size | +| `quote_stuffing_indicator.py` | quote stuffing proxy | +| `trade_intensity.py` | trade arrival intensity | +| `volume_at_price_ratio.py` | volume concentration by price level | +| `volume_synchronicity.py` | volume comovement | +| `volume_weighted_price_momentum.py` | VWAP-style momentum measure | +| `order_book.py` | bid/ask imbalance, depth ratio, weighted mid-price | + +## Practical Anchors + +- liquidity proxies: `kyle_lambda`, `amihud_illiquidity` +- spread proxies: `roll_spread_estimator`, `realized_spread` +- order-flow signals: `order_flow_imbalance`, `effective_tick_rule` +- quote and book-state signals: `order_book.py`, `quote_stuffing_indicator` + +## Notes + +- some signals assume richer quote or trade data than plain OHLCV +- the NASDAQ-100 microstructure case study overlaps with these implementations, but + often builds them manually for pedagogy +- see `docs/user-guide/features.md` for the production-facing feature overview diff --git a/src/ml4t/engineer/features/ml/AGENT.md b/src/ml4t/engineer/features/ml/AGENT.md deleted file mode 100644 index cd3bb52..0000000 --- a/src/ml4t/engineer/features/ml/AGENT.md +++ /dev/null @@ -1,60 +0,0 @@ -# ml/ - 11 ML-Specific Features - -Features designed for machine learning pipelines. - -## Lag Features - -### lag(data, columns, periods=[1, 2, 3, 5, 10]) -> DataFrame -Create lagged versions of columns. -```python -df = lag(df, columns=["close", "volume"], periods=[1, 5, 10]) -# Creates: close_lag_1, close_lag_5, ..., volume_lag_1, ... -``` - -### create_lag_features(data, price_col="close", lags=[1,2,3]) -> DataFrame -Convenience wrapper for price lags. - -## Temporal Encoding - -### cyclical_encode(data, column, period) -> DataFrame -Cyclical encoding for periodic features. -```python -df = cyclical_encode(df, "hour", period=24) -# Creates: hour_sin, hour_cos columns -``` - -### time_features(data, datetime_col="datetime") -> DataFrame -Extract time components: hour, dayofweek, month, quarter. - -## Frequency Domain - -### fourier_features(data, column, periods=[5, 10, 20]) -> DataFrame -Fourier transform features for seasonality detection. - -### rolling_fft(data, column, period=20) -> DataFrame -Rolling FFT for frequency analysis. - -## Information Theory - -### rolling_entropy(data, column, period=20) -> DataFrame -Shannon entropy over rolling window. Measures randomness. -High entropy = unpredictable, low = more structured. - -### mutual_information(data, columns, period=20) -> DataFrame -Rolling mutual information between columns. - -## Statistical - -### rolling_stats(data, column, period=20) -> DataFrame -Rolling mean, std, skew, kurtosis. - -### percentile_rank(data, column, period=252) -> DataFrame -Current value as percentile of history. - -### z_score(data, column, period=20) -> DataFrame -Rolling z-score normalization. - -## Target Creation - -### directional_targets(data, horizon=5) -> DataFrame -Binary direction labels. +1 up, -1 down, 0 flat. diff --git a/src/ml4t/engineer/features/ml/AGENTS.md b/src/ml4t/engineer/features/ml/AGENTS.md new file mode 100644 index 0000000..6353fae --- /dev/null +++ b/src/ml4t/engineer/features/ml/AGENTS.md @@ -0,0 +1,35 @@ +# ml/ - 14 ML-Oriented Feature Utilities + +Utilities intended for model-ready transforms rather than classical technical +analysis indicators. + +## Current Modules + +| Module | Purpose | +|--------|---------| +| `create_lag_features.py` | lagged versions of one or more series | +| `cyclical_encode.py` | cyclic encodings such as hour/day sin-cos transforms | +| `directional_targets.py` | directional targets and simple label helpers | +| `fourier_features.py` | Fourier-based seasonality features | +| `interaction_features.py` | pairwise or structured feature interactions | +| `multi_horizon_returns.py` | returns over multiple future or backward horizons | +| `percentile_rank_features.py` | rank-based normalization and percentile context | +| `regime_conditional_features.py` | transforms conditioned on regime inputs | +| `rolling_entropy.py` | entropy-style complexity measures | +| `time_decay_weights.py` | recency weighting for observations | +| `volatility_adjusted_returns.py` | return normalization by volatility | + +## Related Standalone Helpers + +- `features/fdiff.py` for fractional differencing +- `dataset.py` for leakage-safe assembly into model-ready datasets +- `preprocessing.py` for train-only scaling and transform pipelines + +## Usage Pattern + +These utilities are useful when raw indicators are not enough and you need: + +- lags and horizon structure +- rank or entropy-based transforms +- cyclical calendar encodings +- model-oriented interaction terms diff --git a/src/ml4t/engineer/features/momentum/AGENT.md b/src/ml4t/engineer/features/momentum/AGENTS.md similarity index 100% rename from src/ml4t/engineer/features/momentum/AGENT.md rename to src/ml4t/engineer/features/momentum/AGENTS.md diff --git a/src/ml4t/engineer/features/price_transform/AGENT.md b/src/ml4t/engineer/features/price_transform/AGENTS.md similarity index 100% rename from src/ml4t/engineer/features/price_transform/AGENT.md rename to src/ml4t/engineer/features/price_transform/AGENTS.md diff --git a/src/ml4t/engineer/features/statistics/AGENT.md b/src/ml4t/engineer/features/statistics/AGENT.md deleted file mode 100644 index d6b93aa..0000000 --- a/src/ml4t/engineer/features/statistics/AGENT.md +++ /dev/null @@ -1,37 +0,0 @@ -# statistics/ - 8 Statistical Features - -Rolling statistical calculations. All TA-Lib compatible. - -## Basic Statistics - -### stddev(data, period=20, nbdev=1, price_col="close") -> DataFrame -Rolling standard deviation. - -### var(data, period=20, nbdev=1, price_col="close") -> DataFrame -Rolling variance. - -### avgdev(data, period=20, price_col="close") -> DataFrame -Rolling average deviation from mean. - -## Linear Regression - -### linearreg(data, period=14, price_col="close") -> DataFrame -Linear regression value at end of period. - -### linearreg_slope(data, period=14, price_col="close") -> DataFrame -Slope of linear regression line. - -### linearreg_angle(data, period=14, price_col="close") -> DataFrame -Angle of linear regression in degrees. - -### linearreg_intercept(data, period=14, price_col="close") -> DataFrame -Y-intercept of regression line. - -### tsf(data, period=14, price_col="close") -> DataFrame -Time Series Forecast. Regression projected forward. - -## Structural Break - -### structural_break(data, period=50, min_window=10) -> DataFrame -Detects regime changes using CUSUM test. -Note: File is 646 lines - complex implementation. diff --git a/src/ml4t/engineer/features/statistics/AGENTS.md b/src/ml4t/engineer/features/statistics/AGENTS.md new file mode 100644 index 0000000..83c2fc7 --- /dev/null +++ b/src/ml4t/engineer/features/statistics/AGENTS.md @@ -0,0 +1,25 @@ +# statistics/ - Statistical and Regression Features + +Rolling statistics, regression features, and structural-break helpers. + +## Core Modules + +| Module | Purpose | +|--------|---------| +| `stddev.py` | rolling standard deviation | +| `var.py` | rolling variance | +| `avgdev.py` | average deviation | +| `linearreg.py` | regression value | +| `linearreg_slope.py` | regression slope | +| `linearreg_angle.py` | regression angle | +| `linearreg_intercept.py` | regression intercept | +| `tsf.py` | time-series forecast | +| `structural_break.py` | structural-break and instability diagnostics | + +## Notes + +- the TA-Lib-compatible regression family lives here +- `structural_break.py` is heavier than the classic rolling-stat modules and is best + treated as a specialized workflow +- additional normalized statistical features are surfaced through the registry and the + user-guide metadata views diff --git a/src/ml4t/engineer/features/trend/AGENT.md b/src/ml4t/engineer/features/trend/AGENT.md deleted file mode 100644 index 6873aab..0000000 --- a/src/ml4t/engineer/features/trend/AGENT.md +++ /dev/null @@ -1,50 +0,0 @@ -# trend/ - 10 Trend Indicators - -Moving averages and trend-following indicators. All TA-Lib compatible. - -## Simple Averages - -### sma(data, period=20, price_col="close") -> DataFrame -Simple Moving Average. - -### ema(data, period=20, price_col="close") -> DataFrame -Exponential Moving Average. - -### wma(data, period=20, price_col="close") -> DataFrame -Weighted Moving Average. Linear weights. - -## Double/Triple Smoothing - -### dema(data, period=20, price_col="close") -> DataFrame -Double EMA. Reduces lag vs single EMA. - -### tema(data, period=20, price_col="close") -> DataFrame -Triple EMA. Further reduces lag. - -### t3(data, period=5, vfactor=0.7, price_col="close") -> DataFrame -Tillson T3. Smooth with minimal lag. - -## Adaptive - -### kama(data, period=10, fast=2, slow=30, price_col="close") -> DataFrame -Kaufman Adaptive MA. Adapts to volatility. - -### trima(data, period=20, price_col="close") -> DataFrame -Triangular Moving Average. Double-smoothed SMA. - -## Other - -### midpoint(data, period=14) -> DataFrame -Midpoint over period. (highest + lowest) / 2. - -### midprice(data, period=14) -> DataFrame -Mid Price. (highest_high + lowest_low) / 2. - -## Common Usage - -```python -# Crossover signals -df = sma(df, period=20).rename({"sma_20": "sma_fast"}) -df = sma(df, period=50).rename({"sma_50": "sma_slow"}) -signal = df["sma_fast"] > df["sma_slow"] -``` diff --git a/src/ml4t/engineer/features/trend/AGENTS.md b/src/ml4t/engineer/features/trend/AGENTS.md new file mode 100644 index 0000000..61f13d4 --- /dev/null +++ b/src/ml4t/engineer/features/trend/AGENTS.md @@ -0,0 +1,26 @@ +# trend/ - 10 Trend Indicators + +Moving-average and price-level trend features. + +## Current Modules + +| Module | Purpose | +|--------|---------| +| `sma.py` | simple moving average | +| `ema.py` | exponential moving average | +| `wma.py` | weighted moving average | +| `dema.py` | double EMA | +| `tema.py` | triple EMA | +| `t3.py` | Tillson T3 smoothing | +| `kama.py` | Kaufman adaptive moving average | +| `trima.py` | triangular moving average | +| `midpoint.py` | midpoint over a rolling window | +| `donchian.py` | Donchian channel boundaries | + +## Usage Notes + +- these features are price-scale outputs, so most models will need preprocessing +- for trend strength rather than level-following outputs, combine this directory with + momentum indicators such as `adx` or `aroon` +- see `docs/user-guide/features.md` and `docs/user-guide/ml-readiness.md` for the + production-facing guidance diff --git a/src/ml4t/engineer/features/utils/AGENTS.md b/src/ml4t/engineer/features/utils/AGENTS.md new file mode 100644 index 0000000..72fab3c --- /dev/null +++ b/src/ml4t/engineer/features/utils/AGENTS.md @@ -0,0 +1,16 @@ +# features/utils/ - Feature Utility Helpers + +Low-level helpers used by feature implementations. + +## Files + +| File | Purpose | +|------|---------| +| `arithmetic.py` | arithmetic helpers shared across feature code | +| `helpers.py` | misc feature-level helper functions | +| `ma_types.py` | moving-average type constants and helpers | + +## Notes + +- internal support code for feature implementations +- useful when debugging indicator behavior or shared helper logic diff --git a/src/ml4t/engineer/features/volatility/AGENT.md b/src/ml4t/engineer/features/volatility/AGENT.md deleted file mode 100644 index 9835a11..0000000 --- a/src/ml4t/engineer/features/volatility/AGENT.md +++ /dev/null @@ -1,66 +0,0 @@ -# volatility/ - 15 Volatility Measures - -Historical and realized volatility estimators. - -## TA-Lib Compatible - -### atr(data, period=14) -> DataFrame -Average True Range. Absolute volatility measure. - -### natr(data, period=14) -> DataFrame -Normalized ATR. ATR as percentage of close. - -### trange(data) -> DataFrame -True Range. Single-bar volatility. - -### bollinger_bands(data, period=20, std_dev=2.0) -> DataFrame -Bollinger Bands. Returns: bb_upper, bb_middle, bb_lower, bb_bandwidth. - -## Academic Estimators - -### yang_zhang(data, period=20) -> DataFrame -Yang-Zhang volatility. Most efficient OHLC estimator. Drift-independent. - -### parkinson(data, period=20) -> DataFrame -Parkinson high-low volatility. Uses log(H/L). - -### garman_klass(data, period=20) -> DataFrame -Garman-Klass OHLC volatility. More efficient than close-to-close. - -### rogers_satchell(data, period=20) -> DataFrame -Rogers-Satchell. Drift-adjusted, handles trending markets. - -### close_to_close(data, period=20) -> DataFrame -Close-to-close volatility. Traditional standard deviation. - -## Realized Measures - -### realized_volatility(data, period=20) -> DataFrame -Realized volatility. Sum of squared returns. - -### ewma_volatility(data, span=20, min_periods=10) -> DataFrame -EWMA volatility. Exponentially weighted. - -### range_volatility(data, period=20) -> DataFrame -High-low range based volatility. - -## Conditional - -### garch(data, p=1, q=1) -> DataFrame -GARCH(p,q) volatility forecast. - -### conditional_volatility_ratio(data, short=5, long=20) -> DataFrame -Ratio of short-term to long-term volatility. - -### volatility_percentile_rank(data, period=252) -> DataFrame -Current volatility percentile vs history. - -## Efficiency Comparison - -| Estimator | Efficiency | Drift Robust | Notes | -|-----------|------------|--------------|-------| -| Yang-Zhang | Best | Yes | Preferred for most uses | -| Garman-Klass | Good | No | Assumes zero drift | -| Rogers-Satchell | Good | Yes | Handles trending | -| Parkinson | Medium | No | High-low only | -| Close-to-Close | Baseline | No | Simple but inefficient | diff --git a/src/ml4t/engineer/features/volatility/AGENTS.md b/src/ml4t/engineer/features/volatility/AGENTS.md new file mode 100644 index 0000000..f9e0ad5 --- /dev/null +++ b/src/ml4t/engineer/features/volatility/AGENTS.md @@ -0,0 +1,31 @@ +# volatility/ - 15 Volatility Measures + +Range-based, realized, and conditional volatility estimators. + +## Core Modules + +| Module | Purpose | +|--------|---------| +| `atr.py` | average true range | +| `natr.py` | normalized ATR | +| `trange.py` | single-bar true range | +| `bollinger_bands.py` | Bollinger bands and bandwidth | +| `yang_zhang_volatility.py` | Yang-Zhang estimator | +| `parkinson_volatility.py` | Parkinson high-low estimator | +| `garman_klass_volatility.py` | Garman-Klass OHLC estimator | +| `rogers_satchell_volatility.py` | drift-robust Rogers-Satchell estimator | +| `realized_volatility.py` | realized volatility | +| `ewma_volatility.py` | EWMA volatility | +| `garch_forecast.py` | GARCH forecast | +| `conditional_volatility_ratio.py` | short/long volatility ratio | +| `volatility_percentile_rank.py` | current volatility percentile | +| `volatility_of_volatility.py` | second-order volatility | +| `volatility_regime_probability.py` | high/low-volatility regime probability | + +## Practical Ranking + +- baseline: `atr`, `natr`, `bollinger_bands` +- research-grade estimators: `yang_zhang_volatility`, `garman_klass_volatility`, + `rogers_satchell_volatility` +- regime/context features: `conditional_volatility_ratio`, + `volatility_percentile_rank`, `volatility_regime_probability` diff --git a/src/ml4t/engineer/features/volume/AGENT.md b/src/ml4t/engineer/features/volume/AGENTS.md similarity index 100% rename from src/ml4t/engineer/features/volume/AGENT.md rename to src/ml4t/engineer/features/volume/AGENTS.md diff --git a/src/ml4t/engineer/labeling/AGENT.md b/src/ml4t/engineer/labeling/AGENTS.md similarity index 100% rename from src/ml4t/engineer/labeling/AGENT.md rename to src/ml4t/engineer/labeling/AGENTS.md diff --git a/src/ml4t/engineer/logging/AGENTS.md b/src/ml4t/engineer/logging/AGENTS.md new file mode 100644 index 0000000..b6896c4 --- /dev/null +++ b/src/ml4t/engineer/logging/AGENTS.md @@ -0,0 +1,14 @@ +# logging/ - Structured Logging Helpers + +Logging configuration utilities used by the library internals. + +## Files + +| File | Purpose | +|------|---------| +| `config.py` | logging configuration setup | +| `core.py` | shared logging helpers | + +## Notes + +- this is supporting infrastructure, not a core end-user workflow diff --git a/src/ml4t/engineer/relationships/AGENTS.md b/src/ml4t/engineer/relationships/AGENTS.md new file mode 100644 index 0000000..a89f8db --- /dev/null +++ b/src/ml4t/engineer/relationships/AGENTS.md @@ -0,0 +1,15 @@ +# relationships/ - Cross-Feature Relationship Analysis + +Helpers for analyzing relationships between engineered features. + +## Files + +| File | Purpose | Key Exports | +|------|---------|-------------| +| `correlation.py` | compute correlation matrices | `compute_correlation_matrix` | +| `plot_correlation.py` | heatmap plotting | `plot_correlation_heatmap` | + +## Notes + +- useful for multicollinearity checks and exploratory analysis +- complementary to `ml4t-diagnostic`, but smaller in scope diff --git a/src/ml4t/engineer/store/AGENTS.md b/src/ml4t/engineer/store/AGENTS.md new file mode 100644 index 0000000..7c8e6ec --- /dev/null +++ b/src/ml4t/engineer/store/AGENTS.md @@ -0,0 +1,14 @@ +# store/ - Offline Feature Store + +DuckDB-based offline storage helpers for engineered features and related artifacts. + +## Files + +| File | Purpose | Key Export | +|------|---------|------------| +| `offline.py` | local feature-store implementation | `OfflineFeatureStore` | + +## Notes + +- lower-priority than the main compute, labeling, and dataset workflows +- useful when feature generation needs persistence between runs diff --git a/src/ml4t/engineer/utils/AGENTS.md b/src/ml4t/engineer/utils/AGENTS.md new file mode 100644 index 0000000..02caf65 --- /dev/null +++ b/src/ml4t/engineer/utils/AGENTS.md @@ -0,0 +1,13 @@ +# utils/ - Utility Helpers + +Small support utilities shared by the package. + +## Files + +| File | Purpose | +|------|---------| +| `dependencies.py` | optional dependency checks and import helpers | + +## Notes + +- this is internal support code rather than a public workflow surface From ae0c805cdb71893c779468c8850fd7c26252fd6e Mon Sep 17 00:00:00 2001 From: Stefan Jansen Date: Wed, 1 Apr 2026 19:49:53 -0400 Subject: [PATCH 8/8] feat: adopt ml4t-specs shared schemas --- CHANGELOG.md | 33 +- README.md | 1 + src/ml4t/engineer/artifacts/features.py | 6 +- src/ml4t/engineer/artifacts/labels.py | 10 +- src/ml4t/engineer/artifacts/predictions.py | 10 +- src/ml4t/engineer/config/data_contract.py | 37 --- src/ml4t/engineer/config/spec_bridge.py | 46 +-- tests/test_data_contract_config.py | 60 +--- uv.lock | 358 ++------------------- 9 files changed, 84 insertions(+), 477 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 0437e83..f5ac4d3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,34 @@ Versioning follows [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [Unreleased] +## [0.1.0b3] - 2026-04-01 + +### Added +- Public documentation overhaul with a workflow-first landing page, Book Guide, + and tighter API/user-guide routing +- Expanded `AGENTS.md` coverage across the package, including secondary modules + and wheel artifacts + +### Changed +- Shared feed and artifact schema definitions now come from `ml4t-specs` +- `data_contract_from_market_data_spec()` now normalizes inputs through + `ml4t.specs.FeedSpec` +- Artifact schema defaults align with shared spec naming: + `asset`, `label_value`, and `prediction_value` + +### Removed +- `DataContractConfig.from_ml4t_data()` in favor of shared spec-driven contracts + +## [0.1.0b2] - 2026-03-23 + +### Added +- Engineered artifact spec dataclasses for feature, label, and prediction outputs + +### Changed +- Shared market data specs can now be bridged directly into engineer config + +## [0.1.0b1] - 2026-03-03 + ### Removed - Dead modules: `selection/`, `validation/`, `visualization/`, `pipeline/` - Diagnostic config classes (`feature_config.py`) — moved to ml4t-diagnostic @@ -118,7 +146,10 @@ Initial public alpha release. - `MLDatasetBuilder` for dataset construction - `PreprocessingPipeline` for feature transformation -[Unreleased]: https://github.com/stefan-jansen/ml4t-engineer/compare/v0.1.0a11...HEAD +[Unreleased]: https://github.com/stefan-jansen/ml4t-engineer/compare/v0.1.0b3...HEAD +[0.1.0b3]: https://github.com/stefan-jansen/ml4t-engineer/compare/v0.1.0b2...v0.1.0b3 +[0.1.0b2]: https://github.com/stefan-jansen/ml4t-engineer/compare/v0.1.0b1...v0.1.0b2 +[0.1.0b1]: https://github.com/stefan-jansen/ml4t-engineer/compare/v0.1.0a11...v0.1.0b1 [0.1.0a11]: https://github.com/stefan-jansen/ml4t-engineer/compare/v0.1.0a10...v0.1.0a11 [0.1.0a10]: https://github.com/stefan-jansen/ml4t-engineer/compare/v0.1.0a9...v0.1.0a10 [0.1.0a9]: https://github.com/stefan-jansen/ml4t-engineer/compare/v0.1.0a8...v0.1.0a9 diff --git a/README.md b/README.md index 2528010..395b05a 100644 --- a/README.md +++ b/README.md @@ -164,6 +164,7 @@ ibars = TickImbalanceBarSampler(expected_imbalance=100).sample(tick_data) ## Related Libraries +- **ml4t-specs**: Shared feed and artifact schema definitions across the ML4T stack - **ml4t-data**: Market data acquisition and storage - **ml4t-diagnostic**: Signal evaluation and statistical validation - **ml4t-backtest**: Event-driven backtesting diff --git a/src/ml4t/engineer/artifacts/features.py b/src/ml4t/engineer/artifacts/features.py index 5962941..fbe1b99 100644 --- a/src/ml4t/engineer/artifacts/features.py +++ b/src/ml4t/engineer/artifacts/features.py @@ -5,7 +5,7 @@ from dataclasses import dataclass, field from typing import Any -from ml4t.data.artifacts.base import ArtifactKind, ArtifactProvenance, ArtifactSpec, ArtifactStorage +from ml4t.specs.base import ArtifactKind, ArtifactProvenance, ArtifactSpec, ArtifactStorage @dataclass(frozen=True, slots=True) @@ -13,7 +13,7 @@ class FeatureSchema: """Column layout for a feature artifact.""" timestamp_col: str = "timestamp" - entity_col: str = "symbol" + entity_col: str = "asset" feature_columns: tuple[str, ...] = () @classmethod @@ -23,7 +23,7 @@ def from_mapping(cls, mapping: dict[str, Any] | None) -> FeatureSchema: feature_columns = mapping.get("feature_columns", ()) return cls( timestamp_col=str(mapping.get("timestamp_col", "timestamp")), - entity_col=str(mapping.get("entity_col", "symbol")), + entity_col=str(mapping.get("entity_col", "asset")), feature_columns=tuple(str(item) for item in feature_columns), ) diff --git a/src/ml4t/engineer/artifacts/labels.py b/src/ml4t/engineer/artifacts/labels.py index 77bb147..b841c8d 100644 --- a/src/ml4t/engineer/artifacts/labels.py +++ b/src/ml4t/engineer/artifacts/labels.py @@ -5,7 +5,7 @@ from dataclasses import dataclass, field from typing import Any -from ml4t.data.artifacts.base import ( +from ml4t.specs.base import ( ArtifactKind, ArtifactProvenance, ArtifactSpec, @@ -19,8 +19,8 @@ class LabelSchema: """Column layout for a label artifact.""" timestamp_col: str = "timestamp" - entity_col: str = "symbol" - label_col: str = "label" + entity_col: str = "asset" + label_col: str = "label_value" @classmethod def from_mapping(cls, mapping: dict[str, Any] | None) -> LabelSchema: @@ -28,8 +28,8 @@ def from_mapping(cls, mapping: dict[str, Any] | None) -> LabelSchema: return cls() return cls( timestamp_col=str(mapping.get("timestamp_col", "timestamp")), - entity_col=str(mapping.get("entity_col", "symbol")), - label_col=str(mapping.get("label_col", "label")), + entity_col=str(mapping.get("entity_col", "asset")), + label_col=str(mapping.get("label_col", "label_value")), ) diff --git a/src/ml4t/engineer/artifacts/predictions.py b/src/ml4t/engineer/artifacts/predictions.py index 3d19968..86cf910 100644 --- a/src/ml4t/engineer/artifacts/predictions.py +++ b/src/ml4t/engineer/artifacts/predictions.py @@ -5,7 +5,7 @@ from dataclasses import dataclass, field from typing import Any -from ml4t.data.artifacts.base import ( +from ml4t.specs.base import ( ArtifactKind, ArtifactProvenance, ArtifactSpec, @@ -19,8 +19,8 @@ class PredictionSchema: """Column layout for a prediction artifact.""" timestamp_col: str = "timestamp" - entity_col: str = "symbol" - prediction_col: str = "prediction" + entity_col: str = "asset" + prediction_col: str = "prediction_value" @classmethod def from_mapping(cls, mapping: dict[str, Any] | None) -> PredictionSchema: @@ -28,8 +28,8 @@ def from_mapping(cls, mapping: dict[str, Any] | None) -> PredictionSchema: return cls() return cls( timestamp_col=str(mapping.get("timestamp_col", "timestamp")), - entity_col=str(mapping.get("entity_col", "symbol")), - prediction_col=str(mapping.get("prediction_col", "prediction")), + entity_col=str(mapping.get("entity_col", "asset")), + prediction_col=str(mapping.get("prediction_col", "prediction_value")), ) diff --git a/src/ml4t/engineer/config/data_contract.py b/src/ml4t/engineer/config/data_contract.py index 66b39a2..cf1d906 100644 --- a/src/ml4t/engineer/config/data_contract.py +++ b/src/ml4t/engineer/config/data_contract.py @@ -31,41 +31,4 @@ def from_mapping(cls, mapping: Mapping[str, Any]) -> DataContractConfig: """Create contract from a generic mapping source.""" return cls(**dict(mapping)) - @classmethod - def from_ml4t_data(cls) -> DataContractConfig: - """Create a contract from ml4t-data's canonical multi-asset schema.""" - try: - from ml4t.data.core import MultiAssetSchema - except ImportError as exc: # pragma: no cover - environment-dependent - msg = ( - "ml4t-data is required to build DataContractConfig.from_ml4t_data(). " - "Install/enable ml4t-data or provide contract fields explicitly." - ) - raise ImportError(msg) from exc - - schema = getattr(MultiAssetSchema, "SCHEMA", {}) - schema_cols = {str(col) for col in schema} - - def pick(candidates: tuple[str, ...], default: str | None) -> str | None: - for candidate in candidates: - if candidate in schema_cols: - return candidate - return default - - close_col = pick(("close", "close_price", "last", "last_price"), "close") - return cls( - timestamp_col=pick(("timestamp", "ts", "ts_event", "datetime", "date"), "timestamp"), - symbol_col=pick(("symbol", "ticker", "asset", "asset_id"), None), - price_col=pick( - ("close", "close_price", "price", "mid_price", "last", "last_price"), - close_col, - ), - open_col=pick(("open", "open_price"), "open"), - high_col=pick(("high", "high_price"), "high"), - low_col=pick(("low", "low_price"), "low"), - close_col=close_col, - volume_col=pick(("volume", "volume_base", "size"), "volume"), - ) - - __all__ = ["DataContractConfig"] diff --git a/src/ml4t/engineer/config/spec_bridge.py b/src/ml4t/engineer/config/spec_bridge.py index 09b7ced..e302159 100644 --- a/src/ml4t/engineer/config/spec_bridge.py +++ b/src/ml4t/engineer/config/spec_bridge.py @@ -5,24 +5,9 @@ from collections.abc import Mapping from typing import Any -from ml4t.engineer.config.data_contract import DataContractConfig - - -def _get_field(source: Mapping[str, Any] | object | None, *names: str) -> Any: - """Return the first available field value from a mapping or object.""" - if source is None: - return None +from ml4t.specs import FeedSpec - if isinstance(source, Mapping): - for name in names: - if name in source: - return source[name] - return None - - for name in names: - if hasattr(source, name): - return getattr(source, name) - return None +from ml4t.engineer.config.data_contract import DataContractConfig def data_contract_from_market_data_spec( @@ -33,26 +18,17 @@ def data_contract_from_market_data_spec( The bridge accepts either a nested mapping produced by ``MarketDataSpec.to_dict()`` or an object exposing a ``schema`` attribute with the same field names. """ - - schema = _get_field(spec, "schema") - - timestamp_col = _get_field(schema, "timestamp_col") or _get_field(spec, "timestamp_col") - symbol_col = _get_field(schema, "entity_col", "symbol_col") or _get_field( - spec, "entity_col", "symbol_col" - ) - close_col = _get_field(schema, "close_col", "price_col") or _get_field( - spec, "close_col", "price_col" - ) + feed_spec = FeedSpec.from_any(spec) return DataContractConfig( - timestamp_col=timestamp_col or "timestamp", - symbol_col=symbol_col or "symbol", - price_col=close_col or "close", - open_col=_get_field(schema, "open_col") or _get_field(spec, "open_col") or "open", - high_col=_get_field(schema, "high_col") or _get_field(spec, "high_col") or "high", - low_col=_get_field(schema, "low_col") or _get_field(spec, "low_col") or "low", - close_col=close_col or "close", - volume_col=_get_field(schema, "volume_col") or _get_field(spec, "volume_col") or "volume", + timestamp_col=feed_spec.timestamp_col, + symbol_col=feed_spec.entity_col or "asset", + price_col=feed_spec.price_col, + open_col=feed_spec.open_col, + high_col=feed_spec.high_col, + low_col=feed_spec.low_col, + close_col=feed_spec.close_col, + volume_col=feed_spec.volume_col, ) diff --git a/tests/test_data_contract_config.py b/tests/test_data_contract_config.py index 137100b..0393726 100644 --- a/tests/test_data_contract_config.py +++ b/tests/test_data_contract_config.py @@ -1,9 +1,6 @@ """Tests for DataContractConfig.""" -import sys -from types import ModuleType, SimpleNamespace - -import pytest +from types import SimpleNamespace from ml4t.engineer.config import DataContractConfig, data_contract_from_market_data_spec @@ -49,49 +46,6 @@ def test_from_mapping(self): assert contract.symbol_col == "asset_id" assert contract.price_col == "mid_price" - def test_from_ml4t_data_or_actionable_error(self): - """Bridge should either work or fail with an explicit import message.""" - try: - contract = DataContractConfig.from_ml4t_data() - assert contract.timestamp_col == "timestamp" - assert contract.symbol_col == "symbol" - assert contract.price_col == "close" - except ImportError as exc: - assert "ml4t-data is required" in str(exc) - - def test_from_ml4t_data_uses_available_schema_aliases(self, monkeypatch: pytest.MonkeyPatch): - """Bridge should map to available schema aliases when canonical names differ.""" - - class _SchemaWithAliases: - SCHEMA = { - "ts_event": object(), - "ticker": object(), - "open_price": object(), - "high_price": object(), - "low_price": object(), - "last_price": object(), - "volume_base": object(), - } - - data_module = ModuleType("ml4t.data") - data_module.__path__ = [] # type: ignore[attr-defined] - core_module = ModuleType("ml4t.data.core") - core_module.MultiAssetSchema = _SchemaWithAliases - data_module.core = core_module - - monkeypatch.setitem(sys.modules, "ml4t.data", data_module) - monkeypatch.setitem(sys.modules, "ml4t.data.core", core_module) - - contract = DataContractConfig.from_ml4t_data() - assert contract.timestamp_col == "ts_event" - assert contract.symbol_col == "ticker" - assert contract.open_col == "open_price" - assert contract.high_col == "high_price" - assert contract.low_col == "low_price" - assert contract.close_col == "last_price" - assert contract.price_col == "last_price" - assert contract.volume_col == "volume_base" - def test_from_market_data_spec_mapping(self): """Shared market-data mappings should convert into engineer contracts.""" contract = data_contract_from_market_data_spec( @@ -100,6 +54,7 @@ def test_from_market_data_spec_mapping(self): "schema": { "timestamp_col": "ts_event", "entity_col": "asset_id", + "price_col": "mid_price", "open_col": "open_price", "high_col": "high_price", "low_col": "low_price", @@ -111,7 +66,7 @@ def test_from_market_data_spec_mapping(self): assert contract.timestamp_col == "ts_event" assert contract.symbol_col == "asset_id" - assert contract.price_col == "mid_close" + assert contract.price_col == "mid_price" assert contract.open_col == "open_price" assert contract.high_col == "high_price" assert contract.low_col == "low_price" @@ -123,15 +78,16 @@ def test_from_market_data_spec_object(self): spec = SimpleNamespace( schema=SimpleNamespace( timestamp_col="bar_end", - entity_col="symbol", + entity_col="asset", + price_col="mid_close", close_col="close_bid_price", ) ) contract = data_contract_from_market_data_spec(spec) assert contract.timestamp_col == "bar_end" - assert contract.symbol_col == "symbol" - assert contract.price_col == "close_bid_price" + assert contract.symbol_col == "asset" + assert contract.price_col == "mid_close" assert contract.close_col == "close_bid_price" assert contract.open_col == "open" assert contract.high_col == "high" @@ -143,7 +99,7 @@ def test_from_market_data_spec_defaults_when_schema_is_missing(self): contract = data_contract_from_market_data_spec({}) assert contract.timestamp_col == "timestamp" - 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