mplchart 0.0.34

Classic Stock Charts in Python

Classic Stock Charts in Python

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 can be defined using 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.

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

from mplchart.utils import normalize_prices

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

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

Conventions

Prices data is expected to be a pandas or polars DataFrame with columns open, high, low, close, volume and a datetime column named date or datetime (or a datetime index for pandas).

Note: Chart and all indicators require lowercase column names. Use normalize_prices from mplchart.utils to normalize your DataFrame before use:

from mplchart.utils import normalize_prices
prices = normalize_prices(yf.Ticker(ticker).history('5y'))

Drawing Primitives

The library contains drawing primitives that can be used like an indicator in the plot api. Primitives are classes and must be instantiated as objects before being used with the plot api.

# Candlesticks chart 

from mplchart.chart import Chart
from mplchart.primitives import Candlesticks

Chart(prices, title=title, max_bars=250).plot(
    Candlesticks()
).show()

The main drawing primitives are :

• Candlesticks for candlestick plots
• OHLC for open, high, low, close bar plots
• Price for price line plots
• Volume for volume bar plots
• Pane to switch to a different pane (above or below)
• LinePlot draw an indicator as line plot
• AreaPlot draw an indicator as area plot
• BarPlot draw an indicator as bar plot
• ZigZag lines between pivot points
• Peaks to mark peaks and valleys

Builtin Indicators

The library includes some standard technical analysis indicators for pandas DataFrames. Indicators are classes and must be instantiated as objects before being used with the plot api.

Some of the indicators included are:

• SMA Simple Moving Average
• EMA Exponential Moving Average
• WMA Weighted Moving Average
• HMA Hull Moving Average
• ROC Rate of Change
• RSI Relative Strength Index
• ATR Average True Range
• NATR Normalized Average True Range
• ADX Average Directional Index
• DMI Directional Movement Index
• MACD Moving Average Convergence Divergence
• PPO Price Percentage Oscillator
• BOP Balance of Power
• CMF Chaikin Money Flow
• MFI Money Flow Index
• STOCH Stochastic Oscillator
• BBANDS Bollinger Bands
• KELTNER Keltner Channel
• DEMA Double Exponential Moving Average
• TEMA Triple Exponential Moving Average

Use | to bind an indicator to a rendering primitive, or to compose indicators:

SMA(50) | LinePlot(style="dashed", color="red")   # bind indicator to primitive
SMA(50) | ROC(1)                                   # chain indicators

# Customizing indicator style with LinePlot

from mplchart.indicators import SMA, EMA, ROC
from mplchart.primitives import Candlesticks, LinePlot

indicators = [
    Candlesticks(),
    SMA(20) | LinePlot(style="dashed", color="red", alpha=0.5, width=3)
]

Chart(prices).plot(indicators)

If the indicator returns a DataFrame instead of a Series, specify an item (column name) in the primitive.

Use prices | indicator to apply an indicator directly to data:

prices | SMA(50)               # apply indicator to data

Polars Expressions

For polars DataFrames, the expressions subpackage provides polars Expr factories as an alternative to the indicator pattern. These can be used directly with chart.plot().

# Candlesticks chart with polars expressions

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

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

Expressions are plain polars.Expr values — they can be composed with standard polars operators, passed to df.select(), or used anywhere polars expressions are accepted.

Contrary to indicators, expressions use the @ operator to bind to a primitive:

from mplchart.primitives import LinePlot, AreaPlot
from mplchart.expressions import SMA, RSI

SMA(50) @ LinePlot(color="red")    # expression → primitive
RSI(14) @ AreaPlot(color="blue")   # expression → primitive

Talib Functions

If you have ta-lib installed you can use its abstract functions as indicators. They are created by calling Function with the name of the function and its parameters. Ta-lib functions work with both pandas and polars backends.

# Candlesticks chart with talib functions

from mplchart.primitives import Candlesticks
from talib.abstract import Function

indicators = [
    Candlesticks(),
    Function('SMA', 50),
    Function('SMA', 200),
]

Chart(prices).plot(indicators).show()

Examples

Example notebooks live in the examples/ folder — see the examples README for the full list.

Installation

pip install mplchart

The indicators module requires pandas; the expressions module requires polars. If either is already in your environment, mplchart will use it automatically. The [pandas], [polars], and [all] extras are just a convenience — they install pandas or polars alongside mplchart, nothing more:

pip install mplchart[pandas]
pip install mplchart[polars]
pip install mplchart[all]

Dependencies

Required:

• python >= 3.10
• matplotlib
• numpy
• pyarrow

Optional extras:

• [pandas] — pandas
• [polars] — polars
• [all] — pandas and polars

Related Projects & Resources

• stockcharts.com - Classic stock charts and technical analysis reference
• mplfinance - Matplotlib utilities for the visualization, and visual analysis, of financial data
• matplotlib - Matplotlib: plotting with Python
• pandas - Flexible and powerful data analysis / manipulation library for Python
• polars - Fast DataFrame library for Python
• yfinance - Download market data from Yahoo! Finance's API