mintalib 0.0.30

Minimal Technical Analysis Library for Python

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 and offers a variety of interfaces for pandas and polars dataframes and series.

[!WARNING] This project is experimental and the interface is likely to change.

Installation

You can install this package with pip

pip install mintalib

Dependencies

• python >= 3.10
• numpy
• pandas
• polars [optional]

Interfaces

Mintalib provides three interfaces for different workflows:

• Functions (mintalib.functions) — plain functions, useful for scripting or building custom pipelines
• Polars Expressions (mintalib.expressions) — composable polars expressions, best for polars-native workflows
• Indicators (mintalib.indicators) — callable objects that bind a calculation with its parameters, work with both pandas and polars

Functions

Calculation functions are available from the mintalib.functions module with names in lower case like sma, atr, macd, etc.

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

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/polars series or a numpy array.

import mintalib.functions as ta

prices = ... # pandas/polars DataFrame

sma = ta.sma(prices['close'], 50)
atr = ta.atr(prices, 14)

Polars Expressions

Mintalib offers expression factory methods via the mintalib.expressions module with names in upper case like EMA, SMA, ATR, MACD, ... The methods accept a source expression as an optional keyword-only src parameter. The source expression can also be passed as the first parameter to facilitate the use with pipe. Multi column output calculations like MACD return a tuple of expressions.

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

prices = ... # polars DataFrame

prices.with_columns(
    MACD(),                      # uses 'close' by default
    sma=SMA(50),
    atr=ATR(14),
    trend=EMA(50).pipe(ROC, 1)   # ROC(1) applied to EMA(50)
)

Using Indicators

Indicators offer a composable interface where a calculation function is bound with its parameters into a callable object. Indicators are accessible from the mintalib.indicators module with names in upper case like EMA, SMA, ATR, MACD, etc ...

An indicator instance can be invoked as a function or via the @ operator as syntactic sugar.

So for example SMA(50) @ prices can be used to compute the 50 period simple moving average on prices, in place of SMA(50)(prices).

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

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

prices = ... # pandas DataFrame

result = prices.assign(
    sma50 = SMA(50),
    sma200 = SMA(200),
    rsi = RSI(14),
    trend = ROC(1) @ EMA(20)
)

List of Indicators

Name	Description
ABS	calc_abs(series)
ADX	calc_adx(prices, long period=14)
ALMA	calc_alma(series, long period=9, double offset=0.85, double sigma=6.0)
ATR	calc_atr(prices, long period=14)
AVGPRICE	calc_avgprice(prices)
BBANDS	calc_bbands(prices, long period=20, double nbdev=2.0)
BBP	calc_bbp(prices, long period=20, double nbdev=2.0)
BBW	calc_bbw(prices, long period=20, double nbdev=2.0)
BOP	calc_bop(prices, long period=20)
CCI	calc_cci(prices, long period=20)
CLAG	calc_clag(series, long period=1)
CMF	calc_cmf(prices, long period=20)
CROSSOVER	calc_crossover(series, double level=0.0)
CROSSUNDER	calc_crossunder(series, double level=0.0)
CURVE	calc_curve(series, long period=20)
DEMA	calc_dema(series, long period)
DIFF	calc_diff(series, long period=1)
DMI	calc_dmi(prices, long period=14)
EMA	calc_ema(series, long period, *, bool adjust=False)
EVAL	calc_eval(prices, str expr, *, bool as_flag=False)
EXP	calc_exp(series)
FLAG	calc_flag(series)
HMA	calc_hma(series, long period)
KAMA	calc_kama(series, int period=10, int fastn=2, int slown=30)
KELTNER	calc_keltner(prices, long period=20, double nbatr=2.0)
KER	calc_ker(series, int period=10)
LAG	calc_lag(series, long period)
LOG	calc_log(series)
LROC	calc_lroc(series, long period=1)
MACD	calc_macd(series, long n1=12, long n2=26, long n3=9)
MACDV	calc_macdv(prices, long n1=12, long n2=26, long n3=9)
MAD	calc_mad(series, int period: int = 14)
MAV	calc_mav(series, long period=20, *, str ma_type='SMA')
MAX	calc_max(series, long period)
MDI	calc_mdi(prices, long period=14)
MFI	calc_mfi(prices, long period=14)
MIDPRICE	calc_midprice(prices)
MIN	calc_min(series, long period)
NATR	calc_natr(prices, long period=14)
PDI	calc_pdi(prices, long period=14)
PPO	calc_ppo(series, long n1=12, long n2=26, long n3=9)
PRICE	calc_price(prices, str item: str = None)
QSF	calc_qsf(series, long period=20, long offset=0)
RMA	calc_rma(series, long period)
ROC	calc_roc(series, long period=1)
RSI	calc_rsi(series, long period=14)
RVALUE	calc_rvalue(series, long period=20)
SAR	calc_sar(prices, double afs=0.02, double maxaf=0.2)
SIGN	calc_sign(series)
SLOPE	calc_slope(series, long period=20)
SMA	calc_sma(series, long period)
STDEV	calc_stdev(series, long period=20)
STEP	calc_step(series, double threshold: float = 1.0)
STOCH	calc_stoch(prices, long period=14, long fastn=3, long slown=3)
STREAK	calc_streak(series)
SUM	calc_sum(series, long period)
TEMA	calc_tema(series, long period=20)
TRANGE	calc_trange(prices, *, bool log_prices=False, bool percent=False)
TSF	calc_tsf(series, long period=20, long offset=0)
TYPPRICE	calc_typprice(prices)
UPDOWN	calc_updown(series, double up_level=0.0, double down_level=0.0)
WCLPRICE	calc_wclprice(prices)
WMA	calc_wma(series, long period)

Example Notebooks

Example notebooks are available in the examples folder.

Related Projects

• ta-lib Python wrapper for TA-Lib
• pandas-ta Technical Analysis Indicators for pandas
• ta Technical Analysis Library for pandas
• finta Financial Technical Analysis for pandas
• 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