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 This project is work in progress and the api is bound to change. For a library with a mature api you may want to look into mplfinance.

Typical Usage

import yfinance as yf

from mplchart.chart import Chart
from mplchart.primitives import Candlesticks, Volume
from mplchart.indicators import ROC, SMA, EMA, RSI, MACD

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

max_bars = 250

indicators = [
    Candlesticks(), SMA(50), SMA(200), Volume(),
    RSI(),
    MACD(),
]

chart = Chart(title=ticker, max_bars=max_bars)
chart.plot(prices, indicators)

Conventions

Price data is expected to be presented as a pandas DataFrame with columns open, high, low, close volume and a timestamp index named date. Please note, the library will automatically convert column and index names to lower case for its internal use.

Drawing Primitives

The library contains drawing primitives that can be used as an indicator in the plot api. All primitives are classes that must be instantiated before being used in the plot api.

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

indicators = [Candlesticks()]
chart = Chart(title=title, max_bars=max_bars)
chart.plot(prices, indicators)

The main drawing primitives are :

• Candlesticks for candlesticks plots
• OHLC for open, high, low, close bar plots
• Price for price line plots
• Volume for volume bar plots
• Peaks to plot peaks and valleys
• SameAxes to force plot on the same axes
• NewAxes to force plot on a new axes

Builtin Indicators

The libary contains some basic technical analysis indicators implemented in pandas/numpy. Indicators are classes that must be instantiated before being used in the plot api.

Some of the indicators included are:

• SMA Simple Moving Average
• EMA Exponential Moving Average
• ROC Rate of Change
• RSI Relative Strength Index
• MACD Moving Average Convergence Divergence
• PPO Price Percentage Oscillator
• SLOPE Slope (linear regression with time)
• BBANDS Bolling Bands

Ta-lib Abstract Functions

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

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

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

Custom Indicators

Any callable that takes a prices data frame and returns a series as result can be used as indicator. A function can be used as an indicator but you can also implement an indicator as a callable dataclass.

from dataclasses import dataclass

from mplchart.library import get_series, calc_ema

@dataclass
class DEMA:
    """ Double Exponential Moving Average """
    period: int = 20

    same_scale = True
    # same_scale is an optional class attribute that indicates
    # the indicator should be plot on the same axes by default

    def __call__(self, prices):
        series = 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 the current version of this package with pip

python -mpip install git+https://github.com/furechan/mplchart.git

Requirements:

• python >= 3.8
• matplotlib
• pandas
• numpy
• yfinance

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
• yfinance Download market data from Yahoo! Finance's API
• ta-lib Python wrapper for TA-Lib
• pandas Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
• numpy The fundamental package for scientific computing with Python