sabia 0.2.0

Polars-native technical features for trading pipelines — pure, point-in-time, online-ready

sabia

Polars-native technical features for trading pipelines — pure, point-in-time, online-ready.

sabia reads price/volume into features, grounded in the trading & finance literature. It is the features brick in a layered stack:

marketgoblin (data in) → sabia (features) → quale (signals)

Runtime dependencies are just polars and numpy. (The stack's calendar brick quando will be wired in when calendar-aware seasonality or the microstructure tier needs it — v1 seasonality is pure vectorized datetime.) Risk/eval math lives in ruin; signals live in quale. sabia computes features and nothing else.

What it is

Pure factories over OHLCV bars. A factory binds its params and returns a BoundFeature — an immutable .spec plus a .expr(schema) -> pl.Expr that resolves column roles (close@tr, high@split) against a caller-supplied BarSchema. Features are strictly trailing, point-in-time correct, and deterministic. Batch-first, online-ready: nothing streams in v1, but every feature declares the history it needs and is covered by a windowed-recompute parity test, so a future online engine is a thin wrapper rather than a rewrite.

Install

uv sync --extra dev

Quickstart

import polars as pl
import sabia
from sabia import BarSchema, PriceRole, PriceField, Adjustment

frame = ...  # OHLCV LazyFrame/DataFrame; see sabia.validate for the input contract

# A BarSchema maps your physical column names to roles. sabia adjusts nothing — you declare which
# adjustment basis each column carries.
schema = BarSchema(roles={
    PriceRole(PriceField.OPEN,  Adjustment.SPLIT): "open",
    PriceRole(PriceField.HIGH,  Adjustment.SPLIT): "high",
    PriceRole(PriceField.LOW,   Adjustment.SPLIT): "low",
    PriceRole(PriceField.CLOSE, Adjustment.SPLIT): "close",
    PriceRole(PriceField.CLOSE, Adjustment.TR):    "close",
})

# Factories return BoundFeature objects; compute resolves roles against the schema and materializes:
df = sabia.compute(frame, sabia.momentum.rsi(period=14), sabia.volatility.vol_yz(window=21),
                   schema=schema)

# Query the registry by horizon or data tier:
reg = sabia.Registry.default()
reg.where(lambda s: sabia.Horizon.MEDIUM in s.native_band)
reg.available(sabia.DataTier.DAILY)

Invariants

Causality · point-in-time correctness · purity (no I/O, clocks, randomness) · Polars-native (no pandas) · determinism within a declared tolerance. All enforced by tests, not convention. See FEATURES.md for the full spec.

License

MIT