Getting Started

Installation

To install the library, use pip:

pip install lightweight-charts

Pywebview’s installation can differ depending on OS. Please refer to their documentation.

When using Docker or WSL, you may need to update your language tags; see this issue.

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A simple static chart

import pandas as pd
from lightweight_charts import Chart

Download this ohlcv.csv file for this tutorial.

In this example, we are reading a csv file using pandas:

           date    open    high     low   close       volume
0    2010-06-29  1.2667  1.6667  1.1693  1.5927  277519500.0
1    2010-06-30  1.6713  2.0280  1.5533  1.5887  253039500.0
2    2010-07-01  1.6627  1.7280  1.3513  1.4640  121461000.0
3    2010-07-02  1.4700  1.5500  1.2473  1.2800   75871500.0
4..

..which can be used as data for the Chart object:

if __name__ == '__main__':
    chart = Chart()
    
    df = pd.read_csv('ohlcv.csv')
    chart.set(df)
    
    chart.show(block=True)

The block parameter is set to True in this case, as we do not want the program to exit.

Warning

Due to the library’s use of multiprocessing, instantiations of Chart should be encapsulated within an if __name__ == '__main__' block.

Adding a line

Now lets add a moving average to the chart using the following function:

def calculate_sma(df, period: int = 50):
    return pd.DataFrame({
        'time': df['date'],
        f'SMA {period}': df['close'].rolling(window=period).mean()
    }).dropna()

calculate_sma derives the data column from f'SMA {period}', which we will use as the name of our line:

if __name__ == '__main__':
    chart = Chart()
    line = chart.create_line(name='SMA 50')

    df = pd.read_csv('ohlcv.csv')
    sma_df = calculate_sma(df, period=50)

    chart.set(df)
    line.set(sma_df)
    
    chart.show(block=True)