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.

The first parameter is either prices (a pandas/polars DataFrame with columns open, high, low, close, volume — all lower case, see mintalib.utils.normalize_prices to fix capitalization) or series (a pandas/polars series or numpy array).

In [1]:

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[1]:

	open	high	low	close	volume
date
1980-12-12	0.098298	0.098725	0.098298	0.098298	469033600
1980-12-15	0.093597	0.093597	0.093169	0.093169	175884800
1980-12-16	0.086758	0.086758	0.086331	0.086331	105728000
1980-12-17	0.088468	0.088895	0.088468	0.088468	86441600
1980-12-18	0.091032	0.091460	0.091032	0.091032	73449600
...	...	...	...	...	...
2026-04-20	270.329987	274.279999	270.290009	273.049988	36590200
2026-04-21	271.500000	272.799988	265.399994	266.170013	50209800
2026-04-22	267.820007	273.739990	266.869995	273.170013	43249200
2026-04-23	275.049988	275.769989	271.649994	273.429993	33399600
2026-04-24	272.760010	273.059998	269.649994	271.059998	38124500

11433 rows × 5 columns

Series Functions¶

Series functions take a single series as first argument — select the column explicitly. Passing a DataFrame to a series function raises a TypeError: there is no implicit close selection.

In [2]:

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

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

Out[2]:

date
1980-12-12           NaN
1980-12-15           NaN
1980-12-16           NaN
1980-12-17           NaN
1980-12-18           NaN
                 ...    
2026-04-20    257.638998
2026-04-21    258.372999
2026-04-22    259.449500
2026-04-23    260.489999
2026-04-24    261.398499
Length: 11433, dtype: float64

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

In [3]:

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

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

Out[3]:

	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
...	...	...	...
2026-04-20	2.872127	0.764249	2.107878
2026-04-21	2.926444	1.196688	1.729756
2026-04-22	3.494055	1.656161	1.837893
2026-04-23	3.919685	2.108866	1.810819
2026-04-24	4.019427	2.490978	1.528449

11433 rows × 3 columns

Prices Functions¶

Prices functions take the full prices DataFrame:

In [4]:

ta.atr(prices, 14)

ta.atr(prices, 14)

Out[4]:

date
1980-12-12         NaN
1980-12-15         NaN
1980-12-16         NaN
1980-12-17         NaN
1980-12-18         NaN
                ...   
2026-04-20    5.964207
2026-04-21    6.084621
2026-04-22    6.190717
2026-04-23    6.042809
2026-04-24    5.881179
Length: 11433, dtype: float64

Backends¶

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 [5]:

polars_prices = sample_prices(backend="polars")

ta.macd(polars_prices["close"])

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

Out[5]:

shape: (11_433, 3)

macd	macdsignal	macdhist
f64	f64	f64
null	null	null
null	null	null
null	null	null
null	null	null
null	null	null
…	…	…
2.872127	0.764249	2.107878
2.926444	1.196688	1.729756
3.494055	1.656161	1.837893
3.919685	2.108866	1.810819
4.019427	2.490978	1.528449