Minimal Technical Analysis Library for Python

This package offers a curated list of technical analysis indicators implemented in Cython for optimal performance. The library is built around numpy arrays and provides interfaces for eager NumPy/pandas/polars calculations, composable pandas indicators, and native polars expressions.

[!NOTE] This project is experimental and the interface can change.

Interfaces

Mintalib offers three equivalent calculation interfaces for different workflows:

  • Functions (mintalib.functions) — eager functions for NumPy arrays and pandas or polars objects.
  • Indicators (mintalib.indicators) — composable pandas indicators that bind a calculation with its parameters.
  • Expressions (mintalib.expressions) — composable expression factories for polars-native workflows.

Conventions

Prices DataFrames are expected to have lower case column names open, high, low, close, volume. If your DataFrame has different column name capitalization you can use the normalize_prices utility function to normalize the column names.

from mintalib.utils import normalize_prices

prices = normalize_prices(rawprices)

Functions

Concrete functions are available from the mintalib.functions module with names in lower case like sma, atr, macd, etc.

Functions accept NumPy arrays, pandas objects, or polars objects as appropriate and return the same eager container type when possible.

The first parameter of a function is either prices or series depending on whether the function expects a dataframe of prices or a single series.

import mintalib.functions as ta

prices = ... # pandas or polars DataFrame

sma = ta.sma(prices['close'], 50)
atr = ta.atr(prices, 14)

Composable Indicators

For workflows that benefit from reusable or chained calculations, mintalib.indicators binds a function and its parameters into a callable object.

Indicators work with pandas DataFrames and Series. They are callable, and chain with | or the equivalent .then() method.

from mintalib.indicators import SMA, EMA, ROC, RSI, MACD

prices = ... # pandas DataFrame

result = prices.assign(
    ema20 = EMA(20),
    rsi = RSI(14),
    trend = EMA(20) | ROC(1)
)

Expressions

Polars expression factories are available from mintalib.expressions with upper-case names such as SMA, EMA, ATR, and MACD.

Series calculations default to the close column and prices calculations read the full DataFrame when src is omitted. A column name or polars expression can be supplied through src, and a leading expression is accepted for composition with Expr.pipe. Multi-output calculations such as MACD return a polars struct expression.

from mintalib.expressions import EMA, ATR, ROC, MACD

prices = ... # polars DataFrame

result = prices.with_columns(
    ema20 = EMA(20),
    atr = ATR(14),
    trend = EMA(20).pipe(ROC, 1),
    macd = MACD()
)

List of Indicators

Name Input Description
ABS Series Absolute Value
ADX Prices Average Directional Index
ALMA Series Arnaud Legoux Moving Average
ATR Prices Average True Range
AVGPRICE Prices Average Price
BBANDS Series Bollinger Bands
BBP Series Bollinger Bands Percent (%B)
BBW Series Bollinger Bands Width
BOP Prices Balance of Power
CCI Prices Commodity Channel Index
CLAG Series Confirmation Lag
CMF Prices Chaikin Money Flow
CROSSOVER Series Cross Over
CROSSUNDER Series Cross Under
DEMA Series Double Exponential Moving Average
DIFF Series Difference
DMI Prices Directional Movement Indicator
DONCHIAN Prices Donchian Channel
EMA Series Exponential Moving Average
EVAL Prices Evaluate a pandas expression against a DataFrame's columns.
EXP Series Exponential
FLAG Series Flag Value
HMA Series Hull Moving Average
KAMA Series Kaufman Adaptive Moving Average
KELTNER Prices Keltner Channel
KER Series Kaufman Efficiency Ratio
LAG Series Lag Function
LINREG Series Linear Regression (least squares moving average)
LINREG_RMSE Series Linear Regression Root Mean Square Error
LINREG_RVALUE Series Linear Regression R-Value
LINREG_SLOPE Series Linear Regression Slope
LOG Series Logarithm
LROC Series Logarithmic Rate of Change
MACD Series Moving Average Convergence Divergence
MACDV Prices Moving Average Convergence Divergence - Volatility Normalized
MAD Series Rolling Mean Absolute Deviation
MAV Series Generic Moving Average
MAX Series Rolling Maximum
MDI Prices Minus Directional Index
MEDPRICE Prices Median Price
MFI Prices Money Flow Index
MIN Series Rolling Minimum
NATR Prices Normalized Average True Range
PDI Prices Plus Directional Index
PPO Series Price Percentage Oscillator
PRICE Prices Generic Price
QUADREG Series Quadratic Regression (parabolic moving average)
QUADREG_CURVE Series Quadratic Regression Curve
QUADREG_RMSE Series Quadratic Regression Root Mean Square Error
QUADREG_RVALUE Series Quadratic Regression R-Value
QUADREG_SLOPE Series Quadratic Regression Slope
RMA Series Rolling Moving Average (RSI style)
ROC Series Rate of Change
ROCP Series Rate of Change Percentage
RSI Series Relative Strength Index
SAR Prices Parabolic Stop and Reverse
SIGN Series Sign
SMA Series Simple Moving Average
STDEV Series Standard Deviation
STEP Series Step Function
STOCH Prices Stochastic Oscillator
STREAK Series Consecutive streak of values above zero
SUM Series Rolling sum
TEMA Series Triple Exponential Moving Average
TRANGE Prices True Range
TYPPRICE Prices Typical Price
UPDOWN Series Flag for value crossing up & down levels
WCLPRICE Prices Weighted Close Price
WMA Series Weighted Moving Average
ZLEMA Series Zero-Lag Exponential Moving Average

Example Notebooks

Example notebooks are available in the examples folder.

Installation

pip install mintalib

Mintalib requires Python 3.11 or newer. The base install includes only NumPy; add pandas and/or polars for their corresponding objects and interfaces.

Prebuilt cp311-abi3 wheels are available for regular CPython 3.11 and newer on Linux (x86_64 and ARM64), macOS (Intel and Apple silicon), and Windows (x64). Supported installations therefore do not need a local C compiler.

Dependencies

  • python >= 3.11
  • numpy
  • pandas [optional]
  • polars [optional]

Related Projects

  • ta-lib Python wrapper for TA-Lib
  • pandas-ta Technical Analysis Indicators for pandas
  • ta Technical Analysis Library for pandas
  • finta Financial Technical Analysis for pandas
  • qtalib Quantitative Technical Analysis Library
  • polars-ta Technical Analysis Indicators for polars
  • polars-talib Polars extension for TA-Lib

Project links

Data verified by PyPI on Aug 29, 2026
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Key dates

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  • Released:
Newer release available (0.1.12)

1 maintainer

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Credits

Author: Furechan

GitHub Statistics

Data verified by PyPI on Aug 29, 2026
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License expression

MIT
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Requires

Python >=3.11

Provides Extra

pandas polars all

Tags

technical-analysis indicators pandas polars finance

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