This package offers a curated list of technical analysis indicators implemented in cython. It is built around numpy arrays and aims to be compatible with pandas and also polars where applicable. The library is pre-compiled with cython so as not to require the cython runtime at installation. Also it does not link with numpy and so avoids binary dependency issues.

Warning This project is experimental and the interface can change. For a similar project with a mature api you may want to look into ta-lib.

The mintalib package contains three main modules:

• mintalib.core low level calculation rountines implemented in cython
• mintalib.functions wrapper functions to compute indicators
• mintalib.indicators composable interface to indicators

Most calculations are available in three flavors.

• The raw calculation routine is called something like calc_sma and is available from the mintalib.core module. This routine implemented in cython.
• A function called something like SMA is also available from the mintalib.functions module, and includes extra facilities like selection of column (item) and wrapping of results.
• Finally an indicator with the same name SMA is available from the mintalib.indicators and offers a composable interface.

Functions are available via the functions module, with names like SMA, EMA, RSI, MACD, all in upper case. 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 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 dataframe, a polars dataframe or a dictionary of numpy arrays. The column names for prices are expected to include open, high, low, close all in lower case. A series can be a pandas series, a polars series or any iterable compatible with numpy arrays.

Functions automatically wrap their result to match their input, so that for example pandas based inputs will yield pandas based results with a matching index.

import yfinance as yf

from mintalib.functions import SMA, MAX

# 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 = SMA(prices, 50)  # SMA of 'close' with period = 50
sma200 = SMA(prices, 200)  # SMA of 'close' with period = 200
high200 = MAX(prices, 200, item='high')  # MAX of 'high' with period = 200

Indicators are available via the indicators module, with similar names as functions all in uper case.

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. Indicators support the @ operator as syntactic sugar to apply the indicator to data. So for example SMA(50) @ prices can be used to compute the 50 period simple moving average on prices, insted of SMA(50)(prices).

sma50 = SMA(50) @ prices
sma200 = SMA(200) @ prices

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

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

Prices indicators like ATR can only be applied to prices dataframes.

atr = ATR(14) @ prices

Series indicators can be applied to a prices dataframe or a series. When applied to prices you must specify a column with the item or otherwize the indicator will use the "close" column by default.

# SMA on the close column
sma50 = SMA(50) @ prices

# SMA on the volume column
vol50 = SMA(50, item="volume") @ prices

# Which is the same as
vol50 = SMA(50) @ prices.volume 

With pandas dataframes you can compose and assign multiple indicators in one call using the builtin assign 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),
    slope = ROC(1) @ EMA(20),
    uptrend = EVAL("sma50 > sma200")
)

Indicators can be applied to polars prices dataframes and series in the same way as with pandas.

The @ operator has been extended to also work with polars expressions. This is just syntactic sugar around polars map_batches.

In the following example, you can assign multiple columns using polars with_columns.

import polars as pl
from polars import col

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)

# convert to polars dataframe 
prices = pl.from_pandas(prices, include_index=True)

# compute and append indicators to prices
result = prices.with_columns(
    sma20 = SMA(20) @ col('close'),
    sma50 = SMA(50) @ col('close'),
)

You can find example notebooks in the examples folder.

You can install the current version of this package with pip

python -mpip install git+https://github.com/furechan/mintalib.git

• python >= 3.9
• pandas
• numpy

• ta-lib Python wrapper for TA-Lib
• qtalib Quantitative Technical Analysis Library
• numpy The fundamental package for scientific computing with Python
• pandas Flexible and powerful data analysis / manipulation library for Python
• polars Fast multi-threaded, hybrid-out-of-core query engine focussing on DataFrame front-ends
• yfinance Download market data from Yahoo! Finance's API