mplfinance vs mplchart¶

mplfinance is the matplotlib project's finance plotting library — mature, feature-rich, and widely used. mplchart is a younger library with a different design center: declarative chart composition with built-in indicators, on pandas or polars data. Both render classic financial charts with matplotlib, so they embed anywhere matplotlib does — scripts, notebooks, GUI apps, saved images.

This page compares the two honestly, with the same chart built in each. If you want a mature, stable API today, mplfinance is the safe choice — mplchart is experimental and its interface is still evolving.

In [1]:

import mplfinance as mpf

from mplchart.chart import Chart
from mplchart.primitives import Candlesticks, Volume
from mplchart.indicators import SMA
from mplchart.samples import sample_prices

prices = sample_prices()                                 # lower-case columns, full history
data = prices.tail(250).rename(columns=str.capitalize)   # mplfinance wants Capitalized columns
prices.tail(3)

import mplfinance as mpf from mplchart.chart import Chart from mplchart.primitives import Candlesticks, Volume from mplchart.indicators import SMA from mplchart.samples import sample_prices prices = sample_prices() # lower-case columns, full history data = prices.tail(250).rename(columns=str.capitalize) # mplfinance wants Capitalized columns prices.tail(3)

Out[1]:

	open	high	low	close	volume
date
2025-11-13	274.109985	276.700012	272.089996	272.950012	49602800
2025-11-14	271.049988	275.959991	269.600006	272.410004	47399300
2025-11-17	268.815002	270.489990	265.730011	267.459991	42862157

The same chart in both¶

Candlesticks, volume, and two moving averages — first mplfinance, then mplchart.

In [2]:

mpf.plot(data, type="candle", mav=(50, 200), volume=True, figsize=(10, 6))

mpf.plot(data, type="candle", mav=(50, 200), volume=True, figsize=(10, 6))

In [3]:

Chart(prices, title="mplchart", max_bars=250).plot(
    Candlesticks(), Volume(), SMA(50), SMA(200),
).show()

Chart(prices, title="mplchart", max_bars=250).plot( Candlesticks(), Volume(), SMA(50), SMA(200), ).show()

Same ingredients, two API styles: mplfinance is option-driven — one mpf.plot(...) call configured through keyword arguments (type=, mav=, volume=). mplchart is composition-driven — a chart is a list of primitives and indicators, extended by adding items or panes.

One subtle difference visible above: mav=(50, 200) computes over the data you pass in, so on a 250-bar slice the 200-bar average only exists for the last 50 bars. mplchart computes indicators over the full history and then windows to max_bars, so both averages are complete across the visible range.

Data conventions¶

	mplfinance	mplchart
Backend	pandas	pandas or polars
Columns	Open, High, Low, Close, Volume (capitalized)	open, high, low, close, volume (lower case)
Dates	DatetimeIndex	datetime index (pandas) or date/datetime column
Mismatched names	rename yourself	Chart(..., normalize=True)

The polars support is a real differentiator if your pipeline is polars-based: mplchart consumes polars frames natively (with polars expressions instead of indicators — see the backends reference page), no conversion to pandas needed.

Non-trading gaps¶

Both libraries can draw price history without weekend and holiday gaps, via different mechanisms. mplfinance controls it with a flag — show_nontrading=False is the default, and passing True restores calendar spacing. mplchart eliminates gaps by construction: bars are positioned by row number and the date axis maps tick labels back to dates, so gapless is the default; Chart(..., raw_dates=True) opts into raw calendar spacing instead.

Chart types¶

This is mplfinance's strongest suit. Beyond type="candle" it renders ohlc, line, hollow_and_filled, and — notably — the box-based chart types: Renko and Point & Figure, which resample price movement into bricks/boxes independent of time. mplchart covers the time-series types (Candlesticks, OHLC, line/area/bar plots) but has no Renko or PnF.

In [4]:

mpf.plot(data, type="renko", renko_params=dict(brick_size=5), figsize=(10, 6))

mpf.plot(data, type="renko", renko_params=dict(brick_size=5), figsize=(10, 6))

Indicators¶

mplfinance ships moving averages (mav=, ema=) built in; anything else you compute yourself and attach with mpf.make_addplot(...), which gives full control at the cost of manual wiring. mplchart ships a library of indicators (RSI, MACD, BBANDS, ATR-family, and more) that slot directly into the chart with automatic panes, plus polars expression equivalents, condition primitives (Stripes, Markers), and support for custom callables. If your charts are indicator-heavy, this is where the declarative style pays off.

Styling¶

mplfinance has its own style system with a few dozen named presets (yahoo, charles, binance, blueskies, ...) plus make_mpf_style for custom ones. mplchart stays closer to stock matplotlib: standard matplotlib styles apply, with chart-level options (colorup/colordn, color schemes) for the finance-specific parts.

Choosing¶

Reach for mplfinance when you want a mature, stable library, the box-based chart types (Renko, PnF), or its preset style gallery. Reach for mplchart when your charts are indicator-heavy and declarative composition fits your code, or when your data lives in polars. Both render through matplotlib, so either embeds wherever matplotlib does — but that is where the overlap ends: the two APIs share nothing, so switching libraries means rewriting your chart code, not tweaking it.