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 provides interfaces for numpy, pandas and polars.

[!NOTE] This project is experimental and the interface can change.

[!IMPORTANT] Function signatures have changed: multi-input functions no longer accept a prices DataFrame. Pass the required columns as separate arguments instead. For example, use atr(prices["high"], prices["low"], prices["close"]). See the indicator table below for the data inputs required by each function.

Interfaces

Mintalib offers three dedicated interfaces for different workflows:

  • Functions (mintalib.functions) — eager functions for NumPy arrays and pandas or polars series.
  • Indicators (mintalib.indicators) — composable indicators for pandas-based workflows.
  • Expressions (mintalib.expressions) — composable expression factories for polars-native workflows.

Conventions

Indicators and Expressions expect prices DataFrames to have lowercase column names such as open, high, low, close, and volume. If your data uses different capitalization, use the normalize_prices utility function to normalize its column names.

from mintalib.utils import normalize_prices

prices = normalize_prices(rawprices)

Functions

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

Functions accept NumPy arrays, pandas Series, or polars Series as inputs. Single-output functions preserve the input container type when possible; multi-output functions return an appropriate tabular or named result.

import mintalib.functions as ta

prices = ... # pandas or polars DataFrame

sma = ta.sma(prices['close'], period=50)
atr = ta.atr(prices['high'], prices['low'], prices['close'], 14)
macd = ta.macd(prices['close'])  # macd, macdsignal, macdhist result

Indicators (pandas only)

Indicators are available from mintalib.indicators with upper-case names such as SMA, EMA, ATR, and MACD.

Indicators bind calculation functions and their parameters together into callable objects. They are particularly useful with pandas DataFrame.assign.

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

prices = ... # pandas DataFrame

result = prices.assign(
    ema20 = EMA(20),
    rsi = RSI(14),
    trend = EMA(20) | ROC(1)
)

Expressions (polars only)

Polars expression factories are available from mintalib.expressions with upper-case names such as SMA, EMA, ATR, and MACD.

Their signature is parameters first like period followed by optional expression inputs like src for single series indicators, or open, high, low, close, volume for multi-input expressions.

Series expressions can be composed with the .pipe method, as in pl.col("close").pipe(EMA, 20). Multi-output calculations such as MACD return a polars struct expression that you can unpack with .struct.unnest().

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

prices = ... # polars DataFrame

result = prices.with_columns(
    EMA(20).alias("ema"),
    ATR(14).alias("atr"),
    EMA(20).pipe(ROC, 1).alias("trend"),
    MACD().struct.unnest()
)

List of Indicators

Name Data inputs Description
ABS series Absolute Value
ADX high, low, close Average Directional Index
ALMA series Arnaud Legoux Moving Average
ATR high, low, close Average True Range
AVGPRICE open, high, low, close Average Price
BBANDS series Bollinger Bands
BBP series Bollinger Bands Percent (%B)
BBW series Bollinger Bands Width
BOP open, high, low, close Balance of Power
CCI high, low, close Commodity Channel Index
CLAG series Confirmation Lag
CMF high, low, close, volume Chaikin Money Flow
CROSSOVER series Cross Over
CROSSUNDER series Cross Under
DEMA series Double Exponential Moving Average
DIFF series Difference
DMI high, low, close Directional Movement Indicator
DONCHIAN high, low Donchian Channel
EMA series Exponential Moving Average
EXP series Exponential
FLAG series Flag Value
HMA series Hull Moving Average
KAMA series Kaufman Adaptive Moving Average
KELTNER high, low, close Keltner Channel
KER series Kaufman Efficiency Ratio
LAG series Lag Function
LINREG series Linear Regression (least squares moving average)
LINREG_RMSE series Linear Regression Root Mean Square Error
LINREG_RVALUE series Linear Regression R-Value
LINREG_SLOPE series Linear Regression Slope
LOG series Logarithm
LROC series Logarithmic Rate of Change
MACD series Moving Average Convergence Divergence
MACDV high, low, close Moving Average Convergence Divergence - Volatility Normalized
MAD series Rolling Mean Absolute Deviation
MAV series Generic Moving Average
MAX series Rolling Maximum
MDI high, low, close Minus Directional Index
MEDPRICE high, low Median Price
MFI high, low, close, volume Money Flow Index
MIN series Rolling Minimum
NATR high, low, close Normalized Average True Range
OBV close, volume On-Balance Volume
PDI high, low, close Plus Directional Index
PPO series Price Percentage Oscillator
QUADREG series Quadratic Regression (parabolic moving average)
QUADREG_CURVE series Quadratic Regression Curve
QUADREG_RMSE series Quadratic Regression Root Mean Square Error
QUADREG_RVALUE series Quadratic Regression R-Value
QUADREG_SLOPE series Quadratic Regression Slope
RMA series Rolling Moving Average (RSI style)
ROC series Rate of Change
ROCP series Rate of Change Percentage
RSI series Relative Strength Index
SAR high, low Parabolic Stop and Reverse
SIGN series Sign
SMA series Simple Moving Average
STDEV series Standard Deviation
STEP series Step Function
STOCH high, low, close Stochastic Oscillator
STREAK series Consecutive streak of values above zero
SUM series Rolling sum
TEMA series Triple Exponential Moving Average
TRANGE high, low, close True Range
TYPPRICE high, low, close Typical Price
UPDOWN series Flag for value crossing up & down levels
WCLPRICE high, low, close Weighted Close Price
WMA series Weighted Moving Average
ZLEMA series Zero-Lag Exponential Moving Average

Example Notebooks

Example notebooks are available in the examples folder.

Installation

pip install mintalib

Mintalib requires Python 3.11 or newer. The base install includes only NumPy; add pandas and/or polars for their corresponding objects and interfaces.

Prebuilt cp311-abi3 wheels are available for regular CPython 3.11 and newer on Linux (x86_64 and ARM64), macOS (Intel and Apple silicon), and Windows (x64). Supported installations therefore do not need a local C compiler.

Dependencies

  • python >= 3.11
  • numpy
  • pandas [optional]
  • polars [optional]

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

Project links

Data verified by PyPI on Sep 3, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.1.12)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for furechan from gravatar.com furechan

Credits

Author: Furechan

GitHub Statistics

Data verified by PyPI on Sep 3, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.11

Provides Extra

pandas polars all

Tags

technical-analysis indicators pandas polars finance

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mintalib-0.1.11.tar.gz (861.1 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

mintalib-0.1.11-cp311-abi3-win_amd64.whl (1.1 MB view details)

Uploaded CPython 3.11+Windows x86-64

mintalib-0.1.11-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (2.4 MB view details)

Uploaded CPython 3.11+manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

mintalib-0.1.11-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl (2.4 MB view details)

Uploaded CPython 3.11+manylinux: glibc 2.17+ ARM64manylinux: glibc 2.28+ ARM64

mintalib-0.1.11-cp311-abi3-macosx_11_0_arm64.whl (1.1 MB view details)

Uploaded CPython 3.11+macOS 11.0+ ARM64

mintalib-0.1.11-cp311-abi3-macosx_10_9_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.11+macOS 10.9+ x86-64

File details

Details for the file mintalib-0.1.11.tar.gz.

File metadata

  • Download URL: mintalib-0.1.11.tar.gz
  • Upload date:
  • Size: 861.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.12.9 {"installer":{"name":"uv","version":"0.12.9","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for mintalib-0.1.11.tar.gz
Algorithm Hash digest
SHA256 f6316f9a738aedd95e0ff1dda14618f69d67aa1cb324ec653f06eb90baa00a9c
MD5 502c6d6445a09c8b755944cccb435c2e
BLAKE2b-256 50733b1a82a8d9daf9ab4ad212c2afb46b180a61fc9ddc6e4e3ffcd3e42ce49b

See more details on using hashes here.

File details

Details for the file mintalib-0.1.11-cp311-abi3-win_amd64.whl.

File metadata

  • Download URL: mintalib-0.1.11-cp311-abi3-win_amd64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.11+, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.12.9 {"installer":{"name":"uv","version":"0.12.9","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for mintalib-0.1.11-cp311-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 21895e702a6de708921408450d9693263b60984107c49913482963854cdd23f4
MD5 68b66ba8ec380d1bb6d4deb32d6f7220
BLAKE2b-256 b9cde19874e8615cecacbce666172b01eaf87bae4eff9c6cc990e4822a75abbe

See more details on using hashes here.

File details

Details for the file mintalib-0.1.11-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

  • Download URL: mintalib-0.1.11-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
  • Upload date:
  • Size: 2.4 MB
  • Tags: CPython 3.11+, manylinux: glibc 2.17+ x86-64, manylinux: glibc 2.28+ x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.12.9 {"installer":{"name":"uv","version":"0.12.9","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for mintalib-0.1.11-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 99aaa50ec1db0f0bf075a9d4dae235374ef76d7aa2ab67c6dc5362aa93d19b64
MD5 8bcd8d8bd06275ec19163e8edb139c92
BLAKE2b-256 20be1f77c07fba66eb06cc4c1cdea41f2de8a1ba2c1e85e7f95e8a25d654f226

See more details on using hashes here.

File details

Details for the file mintalib-0.1.11-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl.

File metadata

  • Download URL: mintalib-0.1.11-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl
  • Upload date:
  • Size: 2.4 MB
  • Tags: CPython 3.11+, manylinux: glibc 2.17+ ARM64, manylinux: glibc 2.28+ ARM64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.12.9 {"installer":{"name":"uv","version":"0.12.9","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for mintalib-0.1.11-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 9ff8dddc4030c305634571df9d20bab2d173b59d7975dd86ec1f388ef174cb64
MD5 0db98265a32b05adee0eba3a6cab3a9a
BLAKE2b-256 079b4305fad06f4ed21bd251759d6bcd9e646c01371c86e3422b46202ce33623

See more details on using hashes here.

File details

Details for the file mintalib-0.1.11-cp311-abi3-macosx_11_0_arm64.whl.

File metadata

  • Download URL: mintalib-0.1.11-cp311-abi3-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.11+, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.12.9 {"installer":{"name":"uv","version":"0.12.9","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for mintalib-0.1.11-cp311-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 b2e793850dc842c2e7c11ce75f0db0ac7efde7ecd79fe7a5f66318074bcb1ce3
MD5 cc67cf2a250572eb8b97b56c1a064345
BLAKE2b-256 056ef99bf2b1f641a86d9b36820f173e272e4d96df9f0818401b7145d061ba11

See more details on using hashes here.

File details

Details for the file mintalib-0.1.11-cp311-abi3-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: mintalib-0.1.11-cp311-abi3-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: CPython 3.11+, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: uv/0.12.9 {"installer":{"name":"uv","version":"0.12.9","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}

File hashes

Hashes for mintalib-0.1.11-cp311-abi3-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 8f0236a6fa98d86987777451419259523add0b7532b363b91444fbf040375d30
MD5 b0b616b8d4c472fb14dbd6dc00d7d055
BLAKE2b-256 cafd30472c3986005dda817ba541615834165a924e0852ad2cbfcc68da664e78

See more details on using hashes here.

Release history Release notifications | RSS feed

0.1.12

6 files

This release

0.1.11 This release

6 files

0.1.10

6 files

0.1.9

6 files

0.1.8

6 files

0.1.7

6 files

0.1.6

1 file

0.1.5

1 file

0.1.4

1 file

0.1.3

1 file

0.1.2

1 file

0.1.1

1 file

0.1.0

1 file

0.0.36

1 file

0.0.35

1 file

0.0.34

1 file

0.0.33

1 file

0.0.32

1 file

0.0.31

1 file

0.0.30

1 file

0.0.29

1 file

0.0.28

1 file

0.0.27

1 file

0.0.26

1 file

0.0.25

1 file

0.0.24

1 file

0.0.23

1 file

0.0.22

1 file

0.0.21

1 file

0.0.20

1 file

0.0.19

1 file

0.0.18

1 file

0.0.17

1 file

0.0.16

1 file

0.0.15

1 file

0.0.14

1 file

0.0.13

1 file

0.0.12

1 file

0.0.11

1 file

0.0.10

1 file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page