Using Functions¶

The mintalib.functions module is the simplest interface, providing plain calculation functions.

Functions are named in lower case (e.g. sma, ema, macd). Some names like abs, min, max, sum shadow Python builtins, so it is best to import the module with a short alias rather than importing names directly.

Functions take either a single series or separate price columns like open, high, low, close, volume. Inputs can be pandas/polars series or numpy arrays.

In [14]:

import mintalib.functions as ta
from mintalib.samples import sample_prices

prices = sample_prices()
prices

import mintalib.functions as ta from mintalib.samples import sample_prices prices = sample_prices() prices

Out[14]:

	open	high	low	close	volume
date
1980-12-12	0.098207	0.098634	0.098207	0.098207	469033600
1980-12-15	0.093510	0.093510	0.093083	0.093083	175884800
1980-12-16	0.086678	0.086678	0.086251	0.086251	105728000
1980-12-17	0.088386	0.088813	0.088386	0.088386	86441600
1980-12-18	0.090949	0.091376	0.090949	0.090949	73449600
...	...	...	...	...	...
2026-07-31	304.809998	310.690002	300.000000	308.910004	132489100
2026-08-03	309.579987	311.799988	302.559998	303.420013	75052000
2026-08-04	302.730011	310.420013	301.320007	309.380005	68001000
2026-08-05	309.359985	311.709991	305.670013	311.000000	49178700
2026-08-06	314.339996	316.289398	313.489990	314.260010	7165536

11504 rows × 5 columns

Series Functions¶

Series functions take a single series as first argument.

In [15]:

ta.sma(prices["close"], period=20)

ta.sma(prices["close"], period=20)

Out[15]:

date
1980-12-12         NaN
1980-12-15         NaN
1980-12-16         NaN
1980-12-17         NaN
1980-12-18         NaN
                ...   
2026-07-31    324.3670
2026-08-03    323.9050
2026-08-04    323.8410
2026-08-05    323.7215
2026-08-06    323.6235
Length: 11504, dtype: float64

Multi Input Functions¶

Multi-input functions take their column data as separate positional arguments

In [16]:

ta.atr(prices['high'], prices['low'], prices['close'], period=14)

ta.atr(prices['high'], prices['low'], prices['close'], period=14)

Out[16]:

date
1980-12-12         NaN
1980-12-15         NaN
1980-12-16         NaN
1980-12-17         NaN
1980-12-18         NaN
                ...   
2026-07-31    9.872007
2026-08-03    9.826863
2026-08-04    9.774945
2026-08-05    9.508161
2026-08-06    9.206821
Length: 11504, dtype: float64

Multi Output Functions¶

Multi-output functions like macd return a DataFrame (a named tuple at the core level):

In [17]:

ta.macd(prices["close"], 12, 26, 9)

ta.macd(prices["close"], 12, 26, 9)

Out[17]:

	macd	macdsignal	macdhist
date
1980-12-12	NaN	NaN	NaN
1980-12-15	NaN	NaN	NaN
1980-12-16	NaN	NaN	NaN
1980-12-17	NaN	NaN	NaN
1980-12-18	NaN	NaN	NaN
...	...	...	...
2026-07-31	6.894136	8.260785	-1.366649
2026-08-03	4.536518	7.515932	-2.979414
2026-08-04	3.113124	6.635370	-3.522246
2026-08-05	2.091683	5.726633	-3.634950
2026-08-06	1.527629	4.886832	-3.359203

11504 rows × 3 columns

Polars Backend¶

Results come back in the same backend as the input: pandas in → pandas out (index preserved), polars in → polars out (with NaN converted to null), numpy in → numpy out.

In [18]:

polars_prices = sample_prices(backend="polars")

ta.macd(polars_prices["close"])

polars_prices = sample_prices(backend="polars") ta.macd(polars_prices["close"])

Out[18]:

shape: (11_504, 3)

macd	macdsignal	macdhist
f64	f64	f64
null	null	null
null	null	null
null	null	null
null	null	null
null	null	null
…	…	…
6.894136	8.260785	-1.366649
4.536518	7.515932	-2.979414
3.113124	6.63537	-3.522246
2.091683	5.726633	-3.63495
1.527629	4.886832	-3.359203

In [ ]: