mplchart 0.0.2

Classic Stock Charts in Python

Classic Stock Charts in Python

This project aims at creating classic technical analysis stock charts in Python with minimum code. The library is built around the excellent matplotlib. The interface is declarative, based on a set of drawing primitives like Candleststicks, Volume, Peaks and technical indicators like SMA, EMA, RSI, ROC, MACD, etc ... If you have ta-lib installed you can also use its abstract functions as indicators but it is not a requirement.

This is work in progress! For more mature projects you may want to you look into mplfinance.

Typical Usage

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

ticker = 'AAPL'
freq = 'daily'
prices = get_prices(ticker, freq=freq)

max_bars = 250

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

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

See example notebook mplchart-usage.ipynb

Conventions

Prices are expected to be stored as a pandas DataFrame with columns open, high, low, close volume and a timestamp index named date, all in lower case!

For testing purposes you can use the helper module which can fetch sample prices in the proper format via yfinance. This is meant to be used for testing/demo purposes only! See yfinance for more information on its usage.

from mplchart.helper import get_prices

ticker = 'AAPL'
freq = 'daily'
prices = get_prices(ticker, freq=freq)

See example notebook mplchart-helper.ipynb

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 as objects before being used in the plot api. Here is a snippet for the Candlesticks primitive.

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

See example notebook mplchart-primitives.ipynb

Builtin Indicators

The libary contains some basic technical analysis indicators implemented in pandas/numpy. All 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

See example notebook mplchart-builtins.ipynb

Ta-lib Abstract Functions

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

from talib.abstract import Function

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

See example notebook mplchart-abstract.ipynb

Custom Indicators

It is easy to create custom indicators. An indicator is basically a callable that takes a prices data frame and returns a series as result. A function can be used as an indicator but we suggest you implement indicators 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

See example notebook mplchart-custom.ipynb

Example Notebooks

You can find example notebooks in the examples folder.

Developer Notes

You can install this package with pip

python3 -mpip install git+ssh://[e-mail]/furechan/mplchart-proto.git

Requirements:

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

Related Projects & Resources

• stockcharts.com Beautiful 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