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, but also offers an interface for pandas and polars dataframes and series.

Warning This project is experimental and the interface can change.

Structure

The mintalib package contains four main modules:

• mintalib.core: core calculation routines at numpy level, implemented in cython, with names like calc_sma, calc_ema, etc ...
• mintalib.functions: wrapper functions to apply calculations to series and dataframes, with names like sma, ema, etc ...
• mintalib.indicators: composable interface to pandas indicators with names like SMA, EMA, etc ...
• mintalib.expressions: polars expressions library with names like SMA, EMA, etc ...

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

Using Functions

Functions are available via the mintalib.functins module, with lower case names like sma, ema, etc ... The best way to use this module is to import it with a short alias name like ta.

import mintalib.functions as ta

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

Functions that expect series data can also be applied to a prices dataframe, in which case they use the column specified with the item parameter or by default the 'close' column.

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 or polars series.

Functions automatically wrap the result to match the type and the index of the input data when applicable.

import yfinance as yf
import mintalib.functions as ta

# fetch prices (eg with yfinance)
prices = yf.Ticker('AAPL').history('5y')

# convert column and index names to lower case
prices = prices.rename(columns=str.lower).rename_axis(index=str.lower)

# compute indicators
sma50 = ta.sma(prices, 50)  # SMA of 'close' with period 50
sma200 = ta.sma(prices, 200)  # SMA of 'close' with period 200
high200 = ta.max(prices, 200, item='high')  # MAX of 'high' with period 200

Using Indicators

Indicators are available via the mintalib.indicators module, with similar names as functions but in upper case. Indicators are best imported directly in the name space like:

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

Indicators offer a composable interface where a function is bound with its calculation parameters. When instantiated with parameters an indicator yields a callable that can be applied to prices or series data.

An indicator is a callable that accepts a series or a prices dataframe as a single parameter. You can also use the @ operator as syntactic sugar to apply an indicator to its parameter.

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).

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

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

trend = ROC(1) @ EMA(20) @ prices

Using Indicators with pandas

With pandas dataframes you can compose and apply multiple indicators in one call using the assign dataframe method.

import yfinance as yf

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

# fetch prices (eg with yfinance)
prices = yf.Ticker('AAPL').history('5y')

# convert column and index names to lower case
prices = prices.rename(columns=str.lower).rename_axis(index=str.lower)

# compute and append indicators to prices
# note that calculations can use results from prior indicators
result = prices.assign(
    sma50 = SMA(50),
    sma200 = SMA(200),
    rsi = RSI(14),
    trend = ROC(1) @ EMA(20),
    flag = EVAL("sma50 > sma200")
)

Using Expressions with polars (experimental)

Expressions are available via the mintalib.expressions module, with similar names as functions but in upper case. Expressions are best imported directly in the name space like:

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

Series based expressions have a first argument src that is required. The src parameter can be a column name or a polars expression. So for example SMA('close', period=50), is equivalent to SMA(pl.col('close'), period=50).

prices.select(
    SMA('close', period=50)
)

Series expressions can also be applied with the polars pipe method as in:

prices.select(
    pl.col('close').pipe(SMA, period=50)
)

In prices based expressions like ATR, the src argument is optional and keyword only.

prices.select(
    ATR(period=14)
)

Multi-column expressions like MACD return a struct series instead of a dataframe. You can access the fields with the polars struct accessor. So for example to convert he MACD expression to individual columns you can use the following:

prices.select(
    MACD('close').struct.field('*')
)

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