bartons

Financial and technical-analysis expressions for polars, implemented in Rust as a native plugin (PyO3 + maturin).

Each indicator is a factory returning a pl.Expr, so it composes with the rest of polars — inside select, with_columns, over, lazy frames, and so on.

Install

Requires Python 3.11+ and polars>=1.28,<1.44. Wheels are cp311-abi3, so one wheel per platform covers every Python from 3.11 up. Prebuilt wheels support Linux x86_64 and ARM64, macOS Intel and Apple silicon, and Windows x64; other platforms can build from the sdist with a Rust toolchain.

pip install bartons

Usage

import polars as pl
from bartons.indicators import ATR, CCI, DMI, EMA, MACD, RSI, SMA, TYPPRICE
from bartons.samples import sample_prices

prices = sample_prices("daily")

prices.select("date", "close", EMA(20), RSI(14), ATR(14)).tail(3)

┌────────────┬────────────┬────────────┬───────────┬──────────┐
│ date       ┆ close      ┆ ema        ┆ rsi       ┆ atr      │
╞════════════╪════════════╪════════════╪═══════════╪══════════╡
│ 2024-08-07 ┆ 209.820007 ┆ 217.642081 ┆ 40.192313 ┆ 6.920431 │
│ 2024-08-08 ┆ 213.309998 ┆ 217.229501 ┆ 45.237928 ┆ 6.809686 │
│ 2024-08-09 ┆ 216.240005 ┆ 217.135264 ┆ 49.118920 ┆ 6.666851 │
└────────────┴────────────┴────────────┴───────────┴──────────┘

Conventions

Price frames use a date or datetime column followed by lowercase open, high, low, close, and volume columns. Indicators refer to these lowercase OHLCV names by default; pass explicit column names or expressions when your schema differs.

Each indicator names its output after itself in lowercase like ema, sma ... Use explicit aliases to avoid name collisions:

prices.with_columns(EMA(20), SMA(20))                     # -> "ema", "sma"
prices.with_columns(EMA(20).alias("fast"), EMA(50).alias("slow"))

Single-source indicators typically default to pl.col("close") as source, but they also accept an explicit source, either as the first positional argument or via the src keyword, which makes them chainable with pipe:

EMA(20)                                     # default source
EMA(pl.col("close"), 20)                    # explicit source (positional)
EMA(20, src=pl.col("close"))                # explicit source (src keyword)
pl.col("close").pipe(EMA, 20)               # chaining with pipe

TRANGE, ATR and the price transforms like TYPPRICE accept multiple inputs like high, low and close, each overridable via keyword arguments:

TYPPRICE()                              # high, low and close
TYPPRICE(high="h", low="l", close="c")  # other column names

CCI is a single-source indicator, but defaults its source to TYPPRICE() rather than pl.col("close"):

CCI(20)                          # typical price by default
CCI(20, src=TYPPRICE())          # same thing

Indicators


`ADL()`	Accumulation/Distribution Line
`ADOSC(fast=3, slow=10)`	Chaikin A/D Oscillator
`ALMA(period=9, offset=0.85, sigma=6.0)`	Arnaud Legoux moving average
`AROON(period=14)`	Aroon Down and Up
`AROONOSC(period=14)`	Aroon Oscillator
`ATR(period)`	Average true range
`AVGPRICE()`	Average price, `(open + high + low + close) / 4`
`BBANDS(period=20, nbdev=2.0)`	Bollinger upper, middle and lower bands
`BBP(period=20, nbdev=2.0)`	Bollinger Percent B ratio
`BBW(period=20, nbdev=2.0)`	Bollinger BandWidth ratio
`BOP()`	Unsmoothed Balance of Power
`CCI(period=20)`	Commodity Channel Index
`CMF(period=20)`	Chaikin Money Flow
`DEMA(period)`	Double exponential moving average
`DMI(period=14)`	ADX, plus DI and minus DI expressions
`DONCHIAN(period=20)`	Donchian upper, middle and lower channels
`EMA(period)`	Exponential moving average
`HMA(period)`	Hull moving average
`KAMA(period=10, fastn=2, slown=30)`	Kaufman adaptive moving average
`KELTNER(period=20, nbatr=2.0)`	Keltner upper, middle and lower channels
`KER(period=10)`	Kaufman efficiency ratio
`LINREG(period=20, offset=0)`	Rolling linear-regression forecast
`LINREG_RMSE(period=20)`	Rolling linear-regression RMSE
`LINREG_RVALUE(period=20)`	Rolling linear-regression r-value
`LINREG_SLOPE(period=20)`	Rolling linear-regression slope
`MACD(fast=12, slow=26, signal=9)`	MACD, signal and histogram expressions
`MAD(period=20)`	Rolling mean absolute deviation
`MEDPRICE()`	Median price, `(high + low) / 2`
`MFI(period=14)`	Money Flow Index
`MOM(period=1)`	Momentum
`NATR(period=14)`	Raw fractional normalized average true range
`OBV()`	On-Balance Volume
`PPO(fast=12, slow=26)`	Raw fractional Price Percentage Oscillator
`QUADREG(period=20, offset=0)`	Rolling quadratic-regression forecast
`QUADREG_CURVE(period=20)`	Rolling quadratic coefficient
`QUADREG_RMSE(period=20)`	Rolling quadratic-regression RMSE
`QUADREG_RVALUE(period=20)`	Rolling quadratic partial r-value
`QUADREG_SLOPE(period=20, offset=0)`	Rolling quadratic-regression slope
`RMA(period)`	Wilder's running moving average
`ROC(period=1)`	Raw fractional rate of change
`RSI(period)`	Wilder's relative strength index
`SAR(afs=0.02, maxaf=0.2)`	Parabolic Stop and Reverse
`SMA(period)`	Simple moving average
`STOCH(period=14, fastn=3, slown=3)`	Slow stochastic oscillator, `%K` and `%D`
`STREAK(src)`	Consecutive true count
`TEMA(period=20)`	Triple exponential moving average
`TRANGE()`	True range
`TYPPRICE()`	Typical price, `(high + low + close) / 3`
`WCLPRICE()`	Weighted close price, `(high + low + 2 * close) / 4`
`WILLR(period=14)`	Williams %R
`WMA(period)`	Weighted moving average
`ZLEMA(period)`	Zero-lag exponential moving average

Multi-output indicators return a Polars struct expression. You can unpack its fields directly in the query:

prices.select("date", MACD().struct.unnest())

Or keep the struct column in the query result and unnest it afterward:

result = prices.select("date", MACD())  # contains "macd" struct column
result.unnest()

Bare .unnest() expands every struct column; pass a column name such as .unnest("macd") to expand only that struct. The resulting field names must not collide with existing columns.

Eager API

The compiled kernels are also callable directly on polars series, bypassing the expression layer:

from bartons import kernels

kernels.ema(prices["close"], period=20)
kernels.dmi(prices["high"], prices["low"], prices["close"]).struct.unnest()

Parameters are keyword-only here. This path needs polars>=1.28; the expression API alone works further back.

Development

Set up the development environment and run the complete source-tree validation:

uv sync
uv run inv make

Run the Rust and Python tests without regenerating the extension and stubs:

uv run inv test

License

Bartons is available under the MIT License.

Related Projects

polars-talib — a Polars extension exposing TA-Lib indicators and candlestick-pattern functions as Polars expressions.
polars-ta — an expression-oriented collection of technical-analysis, WorldQuant, and Tongdaxin operators for Polars.
Polars — a fast DataFrame library with Rust and Python APIs, an expression engine, lazy query optimization, and Arrow-compatible memory.
PyO3 — Rust bindings for creating native Python modules and calling between Rust and Python.
Maturin — a build and publishing tool for Python packages implemented in Rust.

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