elvers 0.3.0

High-performance multi-factor quantitative framework built on Polars.

Polars-native factor computation engine for quantitative research. All operators execute as Rust-backed Polars expressions with no Python loops in the hot path.

Core Abstractions

• Panel -- Balanced (timestamp, symbol) container with strict alignment guarantees. Prevents look-ahead bias by construction.
• Factor -- Immutable signal vector. Every operator takes Factor and returns Factor with eager evaluation.

Numerical Conventions

Convention	Summary
Null semantics	NaN/Inf unified to null on construction. Single missing-value type throughout.
Division by zero	All divisions guarded at abs(denom) < 1e-10, returning null.
Rank range	(0, 1] -- does not pass through zero. Ties use average method.
Std/Variance/Covariance	ddof=0 (population). Correlation is ddof-invariant.
Rolling warmup	All ts_* operators: first window-1 values are null.

Full conventions and per-operator specifications: OPERATORS.md

Installation

pip install elvers

Usage

from elvers import load, ts_rank, ts_regression, zscore, signal

panel = load()                          # built-in sample data (crypto 1d OHLCV)
close, volume = panel["close"], panel["volume"]

momentum    = ts_rank(close, 20)
vol_adj     = zscore(momentum) / zscore(ts_rank(volume, 20))
beta_resid  = ts_regression(close, volume, window=60, rettype=0)
alpha       = signal(vol_adj)

Sub-daily data is supported via the interval parameter:

panel = load("hourly.parquet", interval="1h")

Operators

72 operators. All accept and return Factor.

Time-Series -- rolling window per symbol:

ts_delay ts_delta ts_mean ts_sum ts_std_dev ts_min ts_max ts_median ts_rank ts_skewness ts_kurtosis ts_zscore ts_corr ts_covariance ts_product ts_step ts_decay_linear ts_decay_exp_window days_from_last_change ts_av_diff ts_scale ts_percentile ts_quantile ts_cv ts_autocorr ts_count_nans ts_backfill kth_element last_diff_value inst_tvr ts_delta_limit ts_regression trade_when

Cross-Sectional -- across symbols at each timestamp:

rank zscore mean median scale normalize quantile signal winsorize truncate left_tail right_tail

Neutralization and Group -- sector/industry neutralization:

vector_neut regression_neut group_neutralize group_rank group_zscore group_scale group_normalize group_mean group_median group_backfill

Math:

log sqrt sign power signed_power inverse s_log_1p maximum minimum where

Arithmetic:

add subtract multiply divide reverse densify bucket and standard operators (+ - * / ** abs)

Development

pip install -e ".[dev]"
pytest tests/ -v
ruff check elvers/

See CLAUDE.md for full development standards.