Minimal Technical Analysis Library for Python

This library offers a curated list of technical analysis indicators implemented in cython. It is built around numpy arrays and aims to be compatible with both pandas and polars dataframes where possible.

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

Structure

The mintalib package contains three main modules:

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

Functions

Indicators are available as simple functions 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 still be applied to prices data, in which case they use the close column by default or otherwize the column specified with the item parameter.

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 attempt to wrap their result to match their input, so that for example pandas based inputs will yield pandas based results with 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 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

Composable Interface

Indicators are also available through a composable interface via the indicators module, with similar the same names all in uper case. Each indicator is implemented as a class to be instanciated with calculation parameters. The instance objects are callables that can be applied to series or prices data like regular functions. So for example SMA(50) is a callable object that will return the 50 period simple moving average of its argument.

Indicators can be composed with the @ operator, where for example EMA(20) @ ROC(5) means EMA(20) applied to ROC(5). The same @ operator can also be used to apply an indicator to some data, where for example SMA(50) @ prices can be used to compute the 50 period simple moving average on prices.

Please note that with pandas dataframes you can compose and assign multiple indicators in one go using the builtin assign method.

import yfinance as yf

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

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

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

# compute and append indicators to prices
result = prices.assign(
    sma50 = SMA(50),
    sma200 = SMA(200),
    rsi = RSI(14),
    slope = SLOPE(20),
    trend = EMA(20) @ ROC(5),
    pos = EVAL("sma50 > sma200")
)

# you will notice that the EVAL expression at the end
# can use the names sma50 and sma200 that where defined
# just above in the same function call!

Examples

You can find example notebooks in the examples folder.

Installation

You can install the current version of this package with pip

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

Related Projects

• 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

List of Indicators