ml-data-access
Read side of the malatium data store. One load_* per table that ml-data-pipelines writes, over a bear-lake database, in the shape of at-research's data helpers.
pip install ml-data-access
import datetime as dt
import ml_data_access
db = ml_data_access.connect() # ML_DATA_STORE, or connect(path)
start, end = dt.date(2018, 7, 2), dt.date(2025, 6, 30)
reference_df = ml_data_access.load_reference_returns(db, start, end)
scores_df = ml_data_access.load_signals(db, "vrp", start, end)
loadings_df = ml_data_access.load_factor_loadings(db, start, end)
chain_df = ml_data_access.load_option_greeks(db, "AAPL", dt.date(2025, 1, 1), dt.date(2025, 3, 31))
Every loader is load_x(db, start=None, end=None) and returns a collected DataFrame for the inclusive window; the three per-symbol chain loaders take the symbol first. Nothing is screened, joined or derived: the store holds canonical tables (symbol is the option root, right is C/P, iv is null where the vendor's inversion failed, vega is per vol point).
The frames slot straight into malatium:
from malatium.providers import PanelProvider, TradingCalendar
calendar = TradingCalendar(ml_data_access.load_sessions(db, start, end))
reference = PanelProvider(reference_df)
scores = PanelProvider(scores_df)
Loaders
| loader | table |
|---|---|
load_calendar, load_sessions |
exchange sessions, as a frame or a list of dates |
load_universe |
point-in-time S&P 500 membership, ticker and symbol |
load_sectors |
GICS sector snapshot |
load_indices, load_yields, load_rates |
index levels (2024 on), the CBOE yield curve, SOFR |
load_corporate_actions, load_earnings |
splits and dividends; announcement dates with a session |
load_underlying |
EOD stock OHLCV, 2023-06 on |
load_option_greeks(db, symbol, ...), load_index_greeks, load_open_interest |
one name's chain, index chain, open interest |
load_symbology_check, usable_symbol_years |
which symbol-years are the right company |
load_reference_returns |
the reference straddle's per-vega P&L, SPX included |
load_factor_returns, load_factor_loadings, load_factor_covariances, load_idio_vol |
the vol risk model |
load_surface, load_realized_vol, load_forecast, load_stock_features |
the derived panels |
load_signals(db, name, ...) |
one signal's (date, symbol, score) |
in_universe |
semi-join a (date, symbol) panel to membership |
ml_data_access.describe() lists what the store holds. ml_data_access.scan(name, years, symbols) is the lazy scan under every loader; it opens only the partitions asked for, using bear-lake's <table>/<year>/<symbol>.parquet layout.
Things to know before trusting a number
- Eighteen symbol-years are another company's chain. Semi-join against
usable_symbol_years(db)for any multi-year study. - Open interest is one day stale by construction and joins on the same
date. ivis null, not wrong, on the ~3% of contract-days that failed to invert.- Repaired index sessions (mostly 2020-21) carry null gamma and a 15:59 underlying.
Development
uv sync
uv run pytest # builds a synthetic store in a temp dir
uv run ruff check . && uv run ruff format .
1 maintainer
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