lightweight-charts-python

Installation

pip install lightweight-charts

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Features

1. Streamlined for live data, with methods for updating directly from tick data.
2. Multi-pane charts using Subcharts.
3. The Toolbox, allowing for trendlines, rectangles, rays and horizontal lines to be drawn directly onto charts.
4. Events allowing for timeframe selectors (1min, 5min, 30min etc.), searching, hotkeys, and more.
5. Tables for watchlists, order entry, and trade management.
6. Direct integration of market data through Polygon.io's market data API.

Supports: Jupyter Notebooks, PyQt6, PyQt5, PySide6, wxPython, Streamlit, and asyncio.

PartTimeLarry: Interactive Brokers API and TradingView Charts in Python

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1. Display data from a csv:

import pandas as pd
from lightweight_charts import Chart

if __name__ == '__main__':
    
    chart = Chart()
    
    # Columns: time | open | high | low | close | volume 
    df = pd.read_csv('ohlcv.csv')
    chart.set(df)
    
    chart.show(block=True)

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2. Updating bars in real-time:

import pandas as pd
from time import sleep
from lightweight_charts import Chart

if __name__ == '__main__':

    chart = Chart()

    df1 = pd.read_csv('ohlcv.csv')
    df2 = pd.read_csv('next_ohlcv.csv')

    chart.set(df1)

    chart.show()

    last_close = df1.iloc[-1]['close']
    
    for i, series in df2.iterrows():
        chart.update(series)

        if series['close'] > 20 and last_close < 20:
            chart.marker(text='The price crossed $20!')
            
        last_close = series['close']
        sleep(0.1)

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3. Updating bars from tick data in real-time:

import pandas as pd
from time import sleep
from lightweight_charts import Chart

if __name__ == '__main__':
    
    df1 = pd.read_csv('ohlc.csv')
    
    # Columns: time | price 
    df2 = pd.read_csv('ticks.csv')
    
    chart = Chart()
    
    chart.set(df1)
    
    chart.show()
    
    for i, tick in df2.iterrows():
        chart.update_from_tick(tick)
            
        sleep(0.03)

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4. Line Indicators:

import pandas as pd
from lightweight_charts import Chart

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

if __name__ == '__main__':
    chart = Chart()
    chart.legend(visible=True)

    df = pd.read_csv('ohlcv.csv')
    chart.set(df)

    line = chart.create_line('SMA 50')
    sma_data = calculate_sma(df, period=50)
    line.set(sma_data)

    chart.show(block=True)

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5. Styling:

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6. Callbacks:

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