Typical Usage¶

In [1]:

from mplchart.chart import Chart
from mplchart.samples import sample_prices
from mplchart.primitives import Candlesticks, Volume, Pane, Line
from mplchart.indicators import SMA, RSI, MACD

prices = sample_prices()

from mplchart.chart import Chart from mplchart.samples import sample_prices from mplchart.primitives import Candlesticks, Volume, Pane, Line from mplchart.indicators import SMA, RSI, MACD prices = sample_prices()

SVG charts in notebooks¶

By default notebooks display charts as PNG images. For sharper inline charts, switch the display format to SVG at the top of your notebook — as this notebook does:

In [2]:

from matplotlib_inline.backend_inline import set_matplotlib_formats

set_matplotlib_formats("svg")

from matplotlib_inline.backend_inline import set_matplotlib_formats set_matplotlib_formats("svg")

or equivalently with the IPython magic %config InlineBackend.figure_formats = {"svg"}. This is notebook display configuration (an IPython/matplotlib feature, not an mplchart one) — it has no effect on scripts or on exporting charts to files.

Plot chart with indicators¶

Indicators are evaluated from prices during plotting. SMA and MACD use default rendering. The @ operator binds RSI(14) to a Line renderer to customize its display; Line(RSI(14), ...) is the equivalent constructor form.

In [3]:

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

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

Plot existing columns¶

If your pipeline already computes indicators, store them as columns in prices and select them by name. Here pandas calculates the moving average before charting; plot("sma-20") reads that column and draws it with default rendering. The column can come from any calculation library. plot(SMA(20)) also uses default rendering, calculating from prices during plotting. Renderer primitives are optional: use Line("sma-20", color="red") or Line(SMA(20), color="red") when you want to customize the display.

In [4]:

prices["sma-20"] = prices["close"].rolling(20).mean()

Chart(prices, title="Existing columns", max_bars=250).plot(
    Candlesticks(),
    "sma-20",
).show()

prices["sma-20"] = prices["close"].rolling(20).mean() Chart(prices, title="Existing columns", max_bars=250).plot( Candlesticks(), "sma-20", ).show()

What about polars?¶

This walkthrough uses pandas data, but mplchart works the same with polars DataFrames — the charting layer is identical, with polars expressions taking the place of indicators. See the Pandas and polars backends and How expressions work tutorials.