mplchart 0.0.41

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 ...

📖 Documentation: tutorials and a chart gallery at furechan.github.io/mplchart

[!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

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()

Styles

Charts are styled via the style= option — a builtin style, any matplotlib stylesheet name, or a custom style dict. Styles are total: ambient matplotlib settings never affect a chart.

from mplchart.styles import available_styles

available_styles()
# ['chartist', 'modern', 'mplchart', 'nightclouds']

# builtin style
Chart(prices, title=ticker, style="nightclouds").plot(Candlesticks()).show()

# any matplotlib stylesheet
Chart(prices, title=ticker, style="ggplot").plot(Candlesticks()).show()

# custom style dict
MY_STYLE = {
    "stylesheet": "dark_background",
    "settings": {
        "candle.up.color": "#26a69a",
        "candle.down.color": "#ef5350",
    },
}
Chart(prices, title=ticker, style=MY_STYLE).plot(Candlesticks()).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 explicitely to normalize the dataframe.

# Normalize prices to lower case column names

import yfinance as yf
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
• 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
• Stripes to shade background areas where a condition is active
• Markers to mark signal crossings with symbols
• ZigZag lines between pivot points
• Swings to mark local peaks and valleys (swing highs/lows)
• TrendLines to fit support and resistance trend lines (experimental)
• HLine to draw a horizontal reference line on the current pane
• VLine to draw a vertical line across all panes at a given date

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. Instantiated they are callables, you can apply them like calling a function SMA(50)(prices).

Some of the indicators included are:

• SMA Simple Moving Average
• EMA Exponential Moving Average
• WMA Weighted Moving Average
• HMA Hull Moving Average
• RMA Rolling Moving Average (Wilder's)
• DEMA Double Exponential Moving Average
• TEMA Triple Exponential Moving Average
• MOM Momentum
• ROC Rate of Change
• RSI Relative Strength Index
• 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
• TRANGE True Range
• ATR Average True Range
• NATR Normalized Average True Range
• BBANDS Bollinger Bands
• BBP Bollinger Bands Percent
• BBW Bollinger Bands Width
• KELTNER Keltner Channel
• DONCHIAN Donchian Channel
• MEDPRICE Median Price
• TYPPRICE Typical Price
• WCLPRICE Weighted Close Price
• AVGPRICE Average Price

Pass an indicator to a rendering primitive to customize display — the @ binding operator is an equivalent alternative:

# Customizing indicator style with LinePlot

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

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

Chart(prices).plot(indicators)

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)),
    LinePlot(RSI(), 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.

Pass an expression to a rendering primitive to customize display — the @ binding operator is an equivalent alternative:

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

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

Talib Functions

If you have ta-lib installed you can use its abstract functions as indicators. They are created by calling the Function factory 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.

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

• mplfinance - Matplotlib utilities for the visualization, and visual analysis, of financial data
• cufflinks - Productivity Tools for Plotly + Pandas
• 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