pomata 0.1.1

Verifiably-correct, Polars-native quant toolkit: technical indicators, performance & risk metrics, and PnL accounting.

pomata

A Polars-native quant toolkit — technical indicators, PnL accounting, and performance & risk metrics. Each is a composable pl.Expr, so an entire study is one lazy Polars pipeline, from price to performance.

And it doesn't ask you to trust its numbers — it proves them: every function is verified to the float64 floor against an independent reference, under 100% branch coverage.

Alpha. The API is not frozen until 1.0; expect refinement. The correctness bar holds at every commit regardless.

From price to performance, in one query

Signal, PnL, and metrics are all plain pl.Expr, so an entire study is a single Polars pipeline — no glue code, no DataFrame ping-pong, no second dependency between the steps:

import polars as pl
from pomata.indicators import rsi
from pomata.pnl import returns_simple, returns_gross, returns_net, cost_proportional, equity_curve
from pomata.metrics import sharpe_ratio, max_drawdown

report = (
    frame  # a DataFrame (or LazyFrame) with a "close" column
    .with_columns(
        weight=(rsi(pl.col("close"), 14) < 30).cast(pl.Float64).shift(1),  # go long when oversold, act next bar
        asset_returns=returns_simple(pl.col("close")),
    )
    .with_columns(
        net=returns_net(
            returns_gross(pl.col("weight"), pl.col("asset_returns")),
            cost_proportional(pl.col("weight"), rate=0.001),
        ),
    )
    .select(
        sharpe=sharpe_ratio(pl.col("net"), periods_per_year=252),
        max_drawdown=max_drawdown(equity_curve(pl.col("net"))),
    )
)

The indicator feeds the signal, the signal feeds the PnL, the PnL feeds the metrics — every arrow is a pl.Expr, so it all fuses into one Polars query (eager or lazy, single series or a multi-asset panel via .over). The .shift(1) is the whole no-look-ahead story: a signal computed at the close acts on the next bar, by construction.

Install

The only runtime dependency is Polars; Python 3.12+.

# from source (today)
git clone https://github.com/ilpomo/pomata
cd pomata && uv sync

Once published to PyPI, the install will be pip install pomata (or uv add pomata).

Every function is a free-standing pl.Expr factory — name it, compose it, run it in any Polars context. Warm-up rows are null until the window fills, never a fabricated value:

import polars as pl
from pomata.indicators import rsi

frame = pl.DataFrame({"close": [44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.42, 45.84, 46.08, 45.89, 46.03,
                                45.61, 46.28, 46.28, 46.00, 46.03, 46.41, 46.22, 45.64, 46.21, 46.25, 45.71, 46.45]})
frame.select(rsi(pl.col("close"), 14).round(2).alias("rsi"))["rsi"].to_list()
# [None, None, ..., 57.92, 62.88, 63.21, 56.01, 62.34]

What's inside

Three families, one package. They share a grammar (pure pl.Expr factories, one canonical name per concept) and a handoff: pnl emits exactly the return and equity series metrics consumes.

indicators — 75 functions

The technical-analysis layer, each indicator a pl.Expr checked against TA-Lib to the float64 floor — most bar-for-bar from the first emitted value, a documented minority only on the converged tail (the differential tier is non-gating). Multi-output indicators (bollinger_bands, macd, stochastic_slow, …) return a single pl.Struct — pick a line with .struct.field(...) or expand with .struct.unnest().

from pomata.indicators import bollinger_bands
frame.select(bollinger_bands(pl.col("close"), 20).alias("bb")).unnest("bb")

All 75 indicators, by category

• channel (5) — donchian_channels, ichimoku, keltner_channels, midpoint, midprice
• cycle (7) — dominant_cycle_period, dominant_cycle_phase, hilbert_phasor, hilbert_trendline, mama, sine_wave, trend_mode
• directional movement (8) — adx, adxr, di_minus, di_plus, dm_minus, dm_plus, dx, vortex
• momentum (17) — absolute_price_oscillator, aroon, aroon_oscillator, awesome_oscillator, balance_of_power, cci, chande_momentum_oscillator, fisher_transform, macd, mom, percentage_price_oscillator, roc, rsi, rsi_stochastic, trix, ultimate_oscillator, williams_r
• moving average (11) — dema, ema, hma, kama, rma, sma, t3, tema, trima, vwma, wma
• price transform (4) — price_average, price_median, price_typical, price_weighted_close
• statistic (9) — linear_regression, linear_regression_angle, linear_regression_intercept, linear_regression_slope, standard_deviation_ewma, standard_deviation_rolling, time_series_forecast, variance_ewma, variance_rolling
• stochastic (2) — stochastic_fast, stochastic_slow
• trend (2) — parabolic_sar, supertrend
• volatility (4) — atr, atr_normalized, bollinger_bands, true_range
• volume (6) — accumulation_distribution, accumulation_distribution_oscillator, chaikin_money_flow, money_flow_index, obv, vwap

pnl — 18 functions

Profit-and-loss accounting in two flows: returns (a signed weight of capital and asset returns) and cash (a quantity of units and a price), with composable transaction-cost models, dividends, and inverse contracts. Every degenerate input (null / NaN / 0 / ±inf / warm-up) has a defined, documented, tested behavior.

from pomata.pnl import returns_net, returns_gross, cost_proportional, equity_curve
gross = returns_gross(pl.col("weight"), pl.col("asset_returns"))
frame.select(equity_curve(returns_net(gross, cost_proportional(pl.col("weight"), rate=0.001))))

All 18 PnL functions

• cash flow — cost_borrow, cost_fixed, cost_funding, cost_notional, cost_per_share, cumulative_pnl, dividend, pnl_gross, pnl_gross_inverse, pnl_net
• returns flow — cost_proportional, cost_slippage, equity_curve, returns_gross, returns_log, returns_net, returns_simple, turnover

metrics — 60 functions

Performance & risk statistics as reducing pl.Expr: point one at a return series (e.g. pomata.pnl.returns_net) or an equity curve (e.g. pomata.pnl.equity_curve). Sharpe, Sortino, Calmar, drawdown, VaR/CVaR, capture, benchmark-relative (alpha/beta/Treynor/information ratio), and a rolling twin for every windowed form.

from pomata.metrics import sharpe_ratio, max_drawdown
frame.select(sharpe_ratio(pl.col("returns"), periods_per_year=252))

All 60 metrics

• drawdown — conditional_drawdown_at_risk, drawdown, drawdown_rolling, max_drawdown, max_drawdown_duration, pain_index, ulcer_index
• performance — cagr, cagr_rolling, stability, total_return, total_return_rolling
• ratio — adjusted_sharpe_ratio, burke_ratio, calmar_ratio, common_sense_ratio, gain_to_pain_ratio, omega_ratio, omega_ratio_rolling, pain_ratio, probabilistic_sharpe_ratio, recovery_ratio, sharpe_ratio, sharpe_ratio_rolling, sortino_ratio, sortino_ratio_rolling, sterling_ratio, ulcer_performance_ratio
• relative — alpha, alpha_rolling, beta, beta_rolling, capture_downside_ratio, capture_ratio, capture_upside_ratio, information_ratio, information_ratio_rolling, modigliani_risk_adjusted_performance, treynor_ratio, treynor_ratio_rolling
• risk — conditional_value_at_risk, downside_deviation, downside_deviation_rolling, kelly_criterion, kurtosis, kurtosis_rolling, payoff_ratio, profit_ratio, risk_of_ruin, skewness, skewness_rolling, tail_ratio, tail_ratio_rolling, value_at_risk, value_at_risk_modified, value_at_risk_parametric, value_at_risk_rolling, volatility, volatility_rolling, win_rate

Correctness

Verified, not asserted. Every function is checked against an independent reference — a second code path that shares nothing with the implementation — plus frozen golden-master values and property-based invariants, under 100% branch coverage. A function ships only when that suite is green.

For indicators there is also a public reference to meet: TA-Lib. Here is one figure to every digit a float64 holds — rsi(14), the last value of a deterministic 400-bar series:

pomata      85.20908701341023
reference   85.20908701341023   ← independent reimplementation: identical, to the last bit
TA-Lib      85.20908701341024   ← fifteen figures identical; differs only at the float64 floor

The same five indicators on the same series — most reproduce the reference exactly, the rest land at the noise floor:

indicator	pomata	vs reimplementation	vs TA-Lib
sma(20)	105.15146076264764	exact	1e-13
ema(20)	107.7299930892346	1e-13	1e-14
rsi(14)	85.20908701341023	exact	1e-14
atr(14)	1.904174462198776	9e-16	4e-15
macd(12,26,9)	2.523444380829531	1e-13	1e-14

The pomata and reference columns are pinned in the test suite; regenerate the full table — including the TA-Lib column (which needs the optional differential dependency) — from a fresh clone with uv run --group differential python scripts/precision_table.py.

pnl and metrics are proven on a different axis — every degenerate input has a defined behavior, matched against an independent reference oracle — because their math is simple and their correctness lives at the edges, not in the digits. The full method (the precision guarantee, the test-sizing derivations, exactly what is and is not claimed) is in CORRECTNESS.md.

Where pomata fits

pomata is for the quant already working in Polars. Each function is a free-standing pl.Expr with polars as the only runtime dependency, composable across eager, lazy, single-series, and grouped (.over) contexts — so the everyday primitives live in one coherent toolkit instead of a wired-together stack.

It is vectorized analytics and accounting: indicators, total mark-to-market PnL, and metrics. It is not an execution engine — no order fills, no event loop, no lot accounting.

Project

• Requirements — Python ≥ 3.12, Polars ≥ 1.40.
• Contributing — see CONTRIBUTING.md; the full gate (lint, three gating type checkers plus an advisory fourth, doctests, 100% branch coverage) runs on every commit.
• License — MIT, see LICENSE.
• Citation — CITATION.cff.