ml4t-data

Market data acquisition, storage, and update workflows for machine learning for trading.

Use ml4t-data to fetch and validate market data, keep local datasets current, and provide repeatable inputs to research and trading workflows. It includes adapters for equities, futures, foreign exchange, crypto assets, macroeconomic series, factors, and prediction markets.

Installation

ml4t-data supports CPython 3.12, 3.13, and 3.14 on Linux, macOS, and Windows. Python 3.15 is temporarily excluded while upstream dependencies complete their compatibility work.

pip install ml4t-data

Provider-specific integrations are optional. Install ml4t-data[yahoo], ml4t-data[databento], ml4t-data[oanda], or ml4t-data[cot] when those adapters are needed. Some providers require network access, an account, credentials, or metered data. The provider guide records each boundary.

Quick start

The synthetic provider exercises the public OHLCV interface without network access or credentials.

from ml4t.data.providers import SyntheticProvider

provider = SyntheticProvider(seed=42)
data = provider.fetch_ohlcv("SYNTH", "2024-01-01", "2024-01-10", "daily")

assert not data.is_empty()
assert {"timestamp", "symbol", "open", "high", "low", "close", "volume"} <= set(data.columns)

Set ML4T_DATA_PATH to choose the root directory for local datasets. Without an explicit path, the library uses ./data in the current working directory.

Migration from QLDM names

Existing deployments may continue to use QLDM_DATA_ROOT and import QldmError. Both names emit DeprecationWarning and will not be removed before version 1.0.

• Replace QLDM_DATA_ROOT with ML4T_DATA_PATH.
• Replace QldmError with ML4TDataError from ml4t.data.core.exceptions.

Documentation and support

• Documentation
• Issue tracker
• Release notes
• License

The library supplies data to ml4t-engineer feature workflows and ml4t-backtest simulations. Neither package is required to use ml4t-data.

Development

git clone https://github.com/ml4t/data.git
cd data
uv sync --all-extras --all-groups
uv run pre-commit install
uv run ruff check src/ tests/
uv run ruff format --check src/ tests/
uv run ty check
uv run pytest tests/ -q -ra --cov=ml4t.data --cov-report=term-missing
uv run mkdocs build --strict
uv build

Pull requests must preserve offline deterministic tests. Provider contract tests that require credentials run in explicit integration lanes.