mplchart 0.0.13

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 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 experimental and the interface can change. For a similar project 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(),
    Volume(),
    SMA(50),
    SMA(200),
    RSI(),
    MACD(),
]

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

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 or datetime. 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 like an indicator in the plot api. Primitives are classes and must be instantiated before being used as parameters to 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 candlestick plots
• OHLC for open, high, low, close bar plots
• Price for price line plots
• Volume for volume bar plots
• Peaks to mark peaks and valleys

Builtin Indicators

The libary contains some basic technical analysis indicators implemented in pandas/numpy. Indicators are classes and must be instantiated before being used as parameters to 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
• SLOPE Slope (time linear regression)
• BBANDS Bollinger Bands

Talib Abstract Functions

If you have ta-lib installed you can use the library abstract functions as indicators. The indicators are created by calling 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'),
]

Select target axes with NewAxes and SameAxes modifiers

Indicators usually plot in a new axes below, except for a few indicators that plot by default in the main axes. You can change the target axes to use for any indicator by using an axes modifier. A modifier is applied to an indicator with the | operator as in the example below.

from mplchart.modifiers import NewAxes, SameAxes

indicators = [
    Candlesticks(),
    ROC(20) | NewAxes(),
    ROC(50) | SameAxes(),
]

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.

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

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

    same_scale = True
    # same_scale is an optional class attribute
    # to specify that the indicator can be drawn
    # on the same axes as the previous indicator

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

    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

Dependencies

• python >= 3.9
• 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, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
• yfinance Download market data from Yahoo! Finance's API
• ta-lib Python wrapper for TA-Lib