mintalib 0.0.26

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 an interface for pandas and polars dataframes and series.

Warning This project is experimental and the interface can change.

Core Functions

Core calculation functions are implemented in the core module with names like calc_sma, calc_atr, calc_macd, etc.

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.

These functions return a numpy array or a tuple of numpy arrays.

from mintalib.core import calc_sma, calc_atr

prices = ... # pandas/polars DataFrame

sma = calc_sma(prices['close'], 50)
atr = calc_atr(prices)

Pandas Extension

Mintalib can be used as a pandas extension via a ts accessor. Series calculations are accessible on pandas series, and prices calculations are accessible on dataframes.

To activate the extension you only need to import the module mintalib.pandas.

import mintlalib.pandas # noqa F401

prices = ... # pandas DataFrame

sma = prices.close.ts.sma(50)
atr = prices.ts.atr()

Polars Extension

Mintalib can be used as a polars extension via a ts accessor. Series calculations are accessible on pandas series, and prices calculations are accessible on dataframes.

To activate the extension you only need to import the module mintalib.polars.

import mintlalib.polars # noqa F401

prices = ... # polars DataFrame

sma = prices['close'].ts.sma(50)
atr = prices.ts.atr()

The extension can also be used with polars expressions via the ts namespace. Indicators that expect a prices dataframe should be based on a struct expression with all required fields (see OHLCV in example below). When using expressions please note that indicators with multi column outputs like macd return a polars struct expressions.

from mintlalib.polars import CLOSE, OHLCV

# CLOSE is short-hand for pl.col('close')
# OHLCV is short-hand for pl.struct(['open', 'high', 'low', 'close', 'value'])

prices = ... # polars DataFrame

prices.select(
    CLOSE.ts.macd().struct.unnest(),
    sma=CLOSE.ts.sma(50)
    atr=OHLCV.ts.atr()
)

Using Indicators (Legacy Interface)

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 like EMA, SMA, ATR, MACD, etc ...

An indicator instance can be invoked as a function or via the @ operator as syntactic sugar.

So for example SMA(50) @ prices can be used to compute the 50 period simple moving average on prices, instead of the more verbose SMA(50)(prices).

The @ operator can also be used to chain indicators, where for example ROC(1) @ EMA(20) means ROC(1) applied to EMA(20).

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

sma50 = SMA(50) @ prices    # SMA of 'close' with period 50
sma200 = SMA(200) @ prices  # SMA of 'close' with period 200
high200 = MAX(200, item='high') @ prices    # MAX of 'high' with period 200
trend = ROC(1) @ EMA(20) @ prices

Indicators can be used as parameters to the pandas assign and other methods.

result = prices.assign(
    sma50 = SMA(50),
    sma200 = SMA(200),
    rsi = RSI(14)
)

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
EMA	Exponential Moving Average
EVAL	Expression Eval (pandas only)
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 Convergenge Divergence
MACDV	Moving Average Convergenge 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	Average True Range (normalized)
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
SHIFT	Shift Function
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 in the examples folder.

Installation

You can install this package with pip

pip install mintalib

Dependencies

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

Related Projects

• ta-lib Python wrapper for TA-Lib
• 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