mintalib 0.0.33

Minimal Technical Analysis Library for Python

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 offers a variety of interfaces for pandas and polars dataframes and series.

[!WARNING] This project is experimental and the interface is likely to change.

Installation

Pick the backend you want to use — pandas and polars are both optional extras:

pip install mintalib[pandas]          # pandas DataFrames
pip install mintalib[polars]          # polars DataFrames
pip install mintalib[pandas,polars]   # both

A bare pip install mintalib installs only numpy (the core computation layer).

Dependencies

• python >= 3.10
• numpy
• pandas [optional]
• polars [optional]

Interfaces

Mintalib provides three interfaces for different workflows:

• Functions (mintalib.functions) — plain functions, useful for scripting or building custom pipelines (work with both pandas and polars)
• Polars Expressions (mintalib.expressions) — composable polars expressions, best for polars-native workflows
• Indicators (mintalib.indicators) — callable objects that bind a calculation with its parameters; pandas only

Functions

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

Functions are polyvalent. They can be applied equaly to pandas or polars dataframes and series.

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.

A prices dataframe can be a pandas or polars dataframe. The column names for prices are expected to include open, high, low, close, volume all in lower case.

A series can be a pandas/polars series or a numpy array.

import mintalib.functions as ta

prices = ... # pandas/polars DataFrame

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

Expressions

Mintalib offers expression factory methods via the mintalib.expressions module with names in upper case like EMA, SMA, ATR, MACD, ... The methods accept a source expression as an optional keyword-only src parameter. The source expression can also be passed as the first parameter to facilitate the use with pipe, e.g. EMA(20).pipe(ROC, 1) applies ROC(1) on top of EMA(20). Multi column output calculations like MACD return a tuple of expressions.

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

prices = ... # polars DataFrame

prices.with_columns(
    ema=EMA(20),
    atr=ATR(14),
    trend=EMA(20).pipe(ROC, 1)   # ROC(1) applied to EMA(20)
)

Indicators

Indicators offer a composable interface where a calculation function is bound with its parameters into a callable object. Indicators are accessible from the mintalib.indicators module with names in upper case like EMA, SMA, ATR, MACD, etc ...

Indicators work only on pandas DataFrames and Series.

An indicator instance is callable: SMA(50)(prices) applies it to data. The pandas idiom prices.pipe(SMA(50)) works equivalently.

Indicators chain with |, where for example EMA(20) | ROC(1) means ROC(1) applied after EMA(20). The .then() method is the fluent equivalent.

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)
)

List of Indicators

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

Example Notebooks

Example notebooks are available in the examples folder.

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: Support Ta-Lib functions in Polars expressions