Classic Stock Charts in Python

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

Warning The interface has changed. You must pass prices as the first parameter of Chart.

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
from mplchart.indicators import SMA, EMA, ROC, RSI, MACD

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

Chart(prices, title=ticker, max_bars=250).plot(
    Candlesticks(),
    Volume(),
    SMA(50),
    SMA(200),
).plot(
    RSI(), target="above"
).plot(
    MACD(), target="below"
).show()

Conventions

Prices data is expected to be presented as a pandas DataFrame with columns open, high, low, close volume and a datetime index named date or datetime.

Even though the chart object automatically converts price column names to lower case before calling any indicator, if you intend on using indicators independently from the chart object, you must use prices dataframes with all lower case column names!

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 = Chart(prices.title=title, max_bars=250).plot(
    Candlesticsk()
).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
• 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 libary includes some standard technical analysis indicators implemented in pandas/numpy. 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
• ATRP Average True Range Percent
• ADX Average Directional Index
• DMI Directional Movement Index
• MACD Moving Average Convergence Divergence
• PPO Price Percentage Oscillator
• CCI Commodity Channel Index
• BOP Balance of Power
• CMF Chaikin Money Flow
• MFI Money Flow Index
• SLOPE Slope (linear regression)
• STOCH Stochastic Oscillator
• BBANDS Bollinger Bands
• KELTNER Keltner Channel
• DEMA Double Exponential Moving Average
• TEMA Triple Exponential Moving Average

Talib Indicators

If you have ta-lib installed you can use its abstract functions as indicators. The indicators are created by calling Function with the name of the indicator and its parameters.

# Candlesticks chart with talib indicators

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

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

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

Override indicator rendering with the plotting primitives

Most indicators are drawn as line plots with default colors and settings. You can override the rendering of an indicator by piping it with the | operator into a primitive like LinePlot, AreaPlot or BarPlot as in the example below. If the indicator returns a dataframe instead of a series you need to specify an item (column name) in the primitive.

# 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)

Custom Indicators

Any callable that accepts a prices dataframe and returns a series or dataframe can be used as an indicator. You can also implement a custom indicator as a subclass of Indicator.

# Custom Indicator Example

from mplchart.model import Indicator
from mplchart.library import calc_ema

class DEMA(Indicator):
    """Double Exponential Moving Average"""

    same_scale: bool = True
    # boolean, whether the indicator can be drawn
    # on the same axes as the prices

    def __init__(self, period: int = 20):
        self.period = period

    def __call__(self, prices):
        series = self.get_series(prices)
        ema1 = calc_ema(series, self.period)
        ema2 = calc_ema(ema1, self.period)
        return 2 * ema1 - ema2

Examples

You can find example notebooks and scripts in the examples folder.

Installation

You can install this package with pip

pip install mplchart

Dependencies

• python >= 3.10
• matplotlib
• pandas
• numpy

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
• yfinance - Download market data from Yahoo! Finance's API