Installation

pip install python-lightweight-charts

Features

1. Streamlined for live data, with methods for updating directly from tick data.
2. Multi-pane charts using Subcharts and a full pane-management API (add_pane, move_pane, resize_pane, remove_pane).
3. The Toolbox — draw trendlines, rectangles, rays, and horizontal lines directly on charts.
4. Events & Callbacks — timeframe selectors, search, hotkeys, and more.
5. Tables for watchlists, order entry, and trade management.
6. StreamChart — serve a chart over HTTP/WebSocket, viewable in any browser (no desktop window needed).
7. Plugins: Tooltip, BandsIndicator, SessionHighlighting, HeatmapSeries, VolumeProfile, PositionTool.
8. Full Lightweight Charts v5 IChartApi surface including price_scale, time_scale, watermark, grid, crosshair, and more.
9. 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

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)

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)

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)

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)

5. Styling:

import pandas as pd
from lightweight_charts import Chart


if __name__ == '__main__':

    chart = Chart()

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

    chart.layout(background_color='#090008', text_color='#FFFFFF', font_size=16,
                 font_family='Helvetica')

    chart.candle_style(up_color='#00ff55', down_color='#ed4807',
                       border_up_color='#FFFFFF', border_down_color='#FFFFFF',
                       wick_up_color='#FFFFFF', wick_down_color='#FFFFFF')

    chart.volume_config(up_color='#00ff55', down_color='#ed4807')

    chart.watermark('1D', color='rgba(180, 180, 240, 0.7)')

    chart.crosshair(mode='normal', vert_color='#FFFFFF', vert_style='dotted',
                    horz_color='#FFFFFF', horz_style='dotted')

    chart.legend(visible=True, font_size=14)

    chart.set(df)

    chart.show(block=True)

6. Callbacks:

import pandas as pd
from lightweight_charts import Chart


def get_bar_data(symbol, timeframe):
    if symbol not in ('AAPL', 'GOOGL', 'TSLA'):
        print(f'No data for "{symbol}"')
        return pd.DataFrame()
    return pd.read_csv(f'bar_data/{symbol}_{timeframe}.csv')


def on_search(chart, searched_string):  # Called when the user searches.
    new_data = get_bar_data(searched_string, chart.topbar['timeframe'].value)
    if new_data.empty:
        return
    chart.topbar['symbol'].set(searched_string)
    chart.set(new_data)


def on_timeframe_selection(chart):  # Called when the user changes the timeframe.
    new_data = get_bar_data(chart.topbar['symbol'].value, chart.topbar['timeframe'].value)
    if new_data.empty:
        return
    chart.set(new_data, True)


def on_horizontal_line_move(chart, line):
    print(f'Horizontal line moved to: {line.price}')


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

    chart.events.search += on_search

    chart.topbar.textbox('symbol', 'TSLA')
    chart.topbar.switcher('timeframe', ('1min', '5min', '30min'), default='5min',
                          func=on_timeframe_selection)

    df = get_bar_data('TSLA', '5min')
    chart.set(df)

    chart.horizontal_line(200, func=on_horizontal_line_move)

    chart.show(block=True)

7. StreamChart — browser-based chart over HTTP/WebSocket:

StreamChart spins up a local FastAPI/Uvicorn server and streams data to any browser tab. No desktop window is required — ideal for headless servers, notebooks, or remote development.

import pandas as pd
from lightweight_charts import StreamChart

if __name__ == '__main__':
    chart = StreamChart()

    df = pd.read_csv('ohlcv.csv')   # columns: time | open | high | low | close | volume
    chart.set(df)

    chart.show(port=8080, block=True)

Running the script prints a URL such as:

Chart server running at http://127.0.0.1:8080/?token=<64-hex-chars> — press Ctrl+C to stop

Open that URL in your browser to view the chart. The URL contains a one-time security token — keep it private.

Tip: pass open_browser=True to chart.show() to launch the browser automatically.

8. Plugins:

import pandas as pd
from lightweight_charts import Chart
from lightweight_charts.plugins import BandsIndicator, Tooltip

if __name__ == '__main__':
    chart = Chart()

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

    # Bollinger Bands
    upper = df['close'].rolling(20).mean() + 2 * df['close'].rolling(20).std()
    lower = df['close'].rolling(20).mean() - 2 * df['close'].rolling(20).std()
    bands = BandsIndicator(chart)
    bands.set(df['time'], upper, lower)

    # Hover tooltip
    tooltip = Tooltip(chart)

    chart.show(block=True)

Available plugins: Tooltip, BandsIndicator, SessionHighlighting, HeatmapSeries, VolumeProfile, PositionTool.

See the examples/ directory for a runnable script for each plugin.