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Minimal Technical Analysis Library for Python

This package offers a curated list of technical analysis indicators implemented in cython to offer improved performance. It is built around numpy arrays and aims to be compatible with pandas and also polars where applicable.

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.

Structure

The mintalib package contains three main modules:

  • mintalib.core core calculation rountines implemented in cython, with names like calc_sma, calc_ema, etc ...
  • mintalib.functions wrapper functions to compute calculations on series and dataframes, with names like sma, ema, etc ...
  • mintalib.indicators composable interface to indicators with names like SMA, EMA, etc ...

List of Indicators

Name Description
ADX Average Directional Index
ATR Average True Range
AVGPRICE Average Price
BBANDS Bollinger Bands
BOP Balance of Power
CCI Commodity Channel Index
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 for value above zero
HMA Hull Moving Average
KAMA Kaufman Adaptive Moving Average
KELTNER Keltner Channel
KER Kaufman Efficiency Ratio
LAG Lag Function
LOG Logarithm
MACD Moving Average Convergenge Divergence
MAD 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
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
STOCH Stochastic Oscillator
STREAK Consecutive streak of ups or downs
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

Mintalib Core

The calculation routines in mintalib.core, implemented in cython, operate on numpy arrays and return numpy arrays by default. Functions in mintalib.functions and indicators in mintalib.indicatoers are just wrappers around the core calculation routines.

Using Functions

The function names are all lower case and may conflict with standard functions, so the best way to use this module is to alias it to a short name like ta and access all functions as attributes.

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 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, volume all in lower case.

A series can be a pandas series, a polars series or any iterable compatible with numpy arrays.

Functions automatically wrap the result to match the type and the index ofthe 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 indicators module, with similar names as functions but in upper 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).

from mintalib.indicators import ROC. SMA, EMA

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).

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

Using Indicators with Pandas

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

# Average True Range
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),
    trend = ROC(1) @ EMA(20),
    flag = EVAL("sma50 > sma200")
)

Using Indicators with Polars

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

The @ operator has been extended to work with polars expressions. Series indicators should be applied to a polars.col object, while prices indicators should be applied to the polars.all() expression. In the expression context, multi column outputs are returned as struct series.

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

import polars as pl
import yfinance as yf

from mintalib.indicators import SMA, ATR

# 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) @ pl.col('close'),
    sma50 = SMA(50) @ pl.col('close'),
    atr14 = ATR(14) @ pl.all()
)

Example Notebooks

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

Dependencies

  • python >= 3.9
  • pandas
  • numpy

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