mintalib 0.0.9

Minimal Technical Analysis Library for Python

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 pandas or 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 into 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

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 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

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).

sma1 = SMA(50) @ prices
sma2 = 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

Please note that 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 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 = ROC(1) @ EMA(20),
    uptrend = EVAL("sma50 > sma200")
)

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

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
DEMA	Double Exponential Moving Average
DIFF	Difference
EMA	Exponential Moving Average
EVAL	Expression Eval (pandas only)
EXP	Exponential
FLAG_ABOVE	Flag for value above level
FLAG_BELOW	Flag for value below level
FLAG_INVERT	Inverse flag
FLAG_UPDOWN	Flag for value crossing up & down levels
FORECAST	Forecast (time linear regression)
HMA	Hull Moving Average
KAMA	Kaufman Adaptive Moving Average
KELTNER	Keltner Channel
KER	Kaufman Efficiency Ratio
LATR	Average True Range (logarithmic)
LOG	Logarithm
MA	Generic Moving Average
MACD	Moving Average Convergenge Divergence
MAD	Mean Absolute Deviation
MAX	Rolling Maximum
MFI	Money Flow Index
MIDPRICE	Mid Price
MIN	Rolling Minimum
MINUSDI	Minus Directional Index
NATR	Average True Range (normalized)
PLUSDI	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	RValue (time linear regression)
SAR	Parabolic Stop and Reverse
SLOPE	Slope (time 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
TYPPRICE	Typical Price
WCLPRICE	Weighted Close Price
WMA	Weighted Moving Average