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
ADX(period=14) Average Directional Index
ALMA(period=9, offset=0.85, sigma=6.0) Arnaud Legoux moving average
APO(fast=12, slow=26, matype="ema") Absolute Price Oscillator
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
CLAG(period=1) Confirmation lag for discrete states
CMF(period=20) Chaikin Money Flow
CMO(period=14) Rolling-window Chande Momentum Oscillator
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
LROC(period=1) Logarithmic Rate of Change
MA(period=30, matype="sma") Generic moving-average dispatcher
MACD(fast=12, slow=26, signal=9) MACD, signal and histogram expressions
MAD(period=20) Rolling mean absolute deviation
MDI(period=14) Negative Directional Indicator
MEDPRICE() Median price, (high + low) / 2
MFI(period=14) Money Flow Index
MIDPRICE(period=14) Midpoint of the rolling highest high and lowest low
MOM(period=1) Momentum
NATR(period=14) Normalized Average True Range (%)
OBV() On-Balance Volume
PDI(period=14) Positive Directional Indicator
PPO(fast=12, slow=26, matype="ema") 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) Rate of Change (%)
ROCP(period=1) Rate of Change as an unscaled fraction
RSI(period) Wilder's relative strength index
SAR(afs=0.02, maxaf=0.2) Parabolic Stop and Reverse
SMA(period) Simple moving average
STEP(threshold=1.0) Threshold-limited step function
STOCH(period=14, fastn=3, slown=3) Slow stochastic oscillator, %K and %D
STOCHRSI(period=14, fastn=3, slown=3) Stochastic RSI, fast K and fast D
STREAK(src) Consecutive true count
SUPERTREND(period=10, multiplier=3.0) Supertrend line and bullish/bearish direction
TEMA(period=20) Triple exponential moving average
TRANGE() True range
TRIX(period=30) Triple-smoothed EMA rate of change (%)
TYPPRICE() Typical price, (high + low + close) / 3
ULTOSC(fast=7, medium=14, slow=28) Ultimate Oscillator
VWMA(period=20) Volume-weighted moving average
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.

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.

Project links

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  • Released:
Newer release available (0.1.7)

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Credits

Author: Furechan

GitHub Statistics

Data verified by PyPI on Sep 2, 2026
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Requires

Python >=3.11

Tags

polars technical-analysis indicators finance quantitative-finance rust

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