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 integrates with pandas DataFrames and Series.

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

Interfaces

Mintalib offers two interfaces for different workflows:

  • Functions (mintalib.functions) — concrete functions for arrays and pandas objects.
  • Indicators (mintalib.indicators) — pandas-only composable indicators that bind an indicator with its calculation parameters.

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 Series, or pandas DataFrames as appropriate.

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 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)
)

Function Reference
abs(series) Absolute Value
adx(prices, period=14) Average Directional Index
alma(series, period=9, offset=0.85, sigma=6.0) Arnaud Legoux Moving Average
atr(prices, period=14) Average True Range
avgprice(prices) Average Price
bbands(series, period=20, nbdev=2.0) Bollinger Bands
bbp(series, period=20, nbdev=2.0) Bollinger Bands Percent (%B)
bbw(series, period=20, nbdev=2.0) Bollinger Bands Width
bop(prices) Balance of Power
cci(prices, period=20) Commodity Channel Index
clag(series, period=1) Confirmation Lag
cmf(prices, period=20) Chaikin Money Flow
crossover(series, level=0.0) Cross Over
crossunder(series, level=0.0) Cross Under
dema(series, period) Double Exponential Moving Average
diff(series, period=1) Difference
dmi(prices, period=14) Directional Movement Indicator
donchian(prices, period=20) Donchian Channel
ema(series, period, *, adjust=False) Exponential Moving Average
exp(series) Exponential
flag(series) Flag Value
hma(series, period) Hull Moving Average
kama(series, period=10, fastn=2, slown=30) Kaufman Adaptive Moving Average
keltner(prices, period=20, nbatr=2.0) Keltner Channel
ker(series, period=10) Kaufman Efficiency Ratio
lag(series, period) Lag Function
linreg(series, period=20, offset=0) Linear Regression (least squares moving average)
linreg_rmse(series, period=20) Linear Regression Root Mean Square Error
linreg_rvalue(series, period=20) Linear Regression R-Value
linreg_slope(series, period=20) Linear Regression Slope
log(series) Logarithm
lroc(series, period=1) Logarithmic Rate of Change
macd(series, n1=12, n2=26, n3=9) Moving Average Convergence Divergence
macdv(prices, n1=12, n2=26, n3=9) Moving Average Convergence Divergence - Volatility Normalized
mad(series, period=14) Rolling Mean Absolute Deviation
mav(series, period=20, *, matype='sma') Generic Moving Average
max(series, period) Rolling Maximum
mdi(prices, period=14) Minus Directional Index
medprice(prices) Median Price
mfi(prices, period=14) Money Flow Index
min(series, period) Rolling Minimum
natr(prices, period=14) Normalized Average True Range
pdi(prices, period=14) Plus Directional Index
ppo(series, n1=12, n2=26, n3=9) Price Percentage Oscillator
price(prices, item=None) Generic Price
quadreg(series, period=20, offset=0) Quadratic Regression (parabolic moving average)
quadreg_curve(series, period=20) Quadratic Regression Curve
quadreg_rmse(series, period=20) Quadratic Regression Root Mean Square Error
quadreg_rvalue(series, period=20) Quadratic Regression R-Value
quadreg_slope(series, period=20, offset=0) Quadratic Regression Slope
rma(series, period) Rolling Moving Average (RSI style)
roc(series, period=1) Rate of Change
rocp(series, period=1) Rate of Change Percentage
rsi(series, period=14) Relative Strength Index
sar(prices, afs=0.02, maxaf=0.2) Parabolic Stop and Reverse
sign(series) Sign
sma(series, period) Simple Moving Average
stdev(series, period=20) Standard Deviation
step(series, threshold=1.0) Step Function
stoch(prices, period=14, fastn=3, slown=3) Stochastic Oscillator
streak(series) Consecutive streak of values above zero
sum(series, period) Rolling sum
tema(series, period=20) Triple Exponential Moving Average
trange(prices, *, log_prices=False, percent=False) True Range
typprice(prices) Typical Price
updown(series, up_level=0.0, down_level=0.0) Flag for value crossing up & down levels
wclprice(prices) Weighted Close Price
wma(series, period) Weighted Moving Average
zlema(series, period) 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 to use the indicator interface or pandas objects.

Dependencies

  • python >= 3.11
  • numpy
  • pandas [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

Project links

Data verified by PyPI on Aug 28, 2026
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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 28, 2026
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License expression

MIT
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Requires

Python >=3.11

Provides Extra

pandas polars all

Tags

cython technical-analysis indicators pandas polars finance quantitative-finance

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