mplchart

Create classic technical analysis stock charts in Python with minimal code. The library is built around matplotlib and supports both pandas and polars DataFrames. Charts are defined with a declarative interface, based on a set of drawing primitives like Candlesticks, Volume and technical indicators like SMA, EMA, RSI, ROC, MACD, etc.

Warning

This project is experimental and the interface is likely to change. For a related project with a mature api you may want to look into mplfinance.

Installation

pip install mplchart

Typical Usage

# Candlesticks chart with SMA, RSI and MACD indicators

import yfinance as yf

from mplchart.chart import Chart
from mplchart.primitives import Candlesticks, Volume, Pane, LinePlot
from mplchart.indicators import SMA, RSI, MACD

ticker = 'AAPL'
prices = yf.Ticker(ticker).history('5y')

Chart(prices, title=ticker, max_bars=250, normalize=True).plot(
    Candlesticks(), Volume(), SMA(50), SMA(200),
    Pane("above", yticks=(30, 50, 70)),
    LinePlot(RSI(14), overbought=70, oversold=30),
    Pane("below"),
    MACD(),
).show()

Conventions

Prices data is expected to be a dataframe with columns open, high, low, close, volume in lower case and a datetime column named date or datetime (or a datetime index for pandas). If your data has column names in different capitalization (like data from yfinance) use the normalize option Chart(..., normalize=True) or call normalize_prices explicitly to normalize the dataframe.

Where to go next

Head over to the tutorials in the sidebar — each one is a runnable Jupyter notebook, starting with Typical Usage.