lwcharts - Lightweight Charts for Jupyter

Ultra-simple TradingView Lightweight Charts integration for Jupyter notebooks. No widget infrastructure, no build steps, just works.

Why lwcharts?

lwcharts takes a radically simple approach to displaying charts in Jupyter:

• ✅ No JavaScript build required - Uses inline HTML with CDN-loaded libraries
• ✅ No widget infrastructure - Just pure IPython HTML display
• ✅ Works everywhere - PyCharm, JupyterLab, VS Code, Colab, anywhere Jupyter runs
• ✅ Zero configuration - Install and use immediately
• ✅ Tiny package - Less than 2KB, only depends on IPython

Installation

pip install lwcharts

That's it! No additional setup, no extensions to install, no build steps.

Quick Start

Line Chart

from lwcharts import Chart

chart = Chart(chart_type='line', width=800, height=400)
chart.data = [
    {'time': '2025-01-01', 'value': 100},
    {'time': '2025-01-02', 'value': 110},
    {'time': '2025-01-03', 'value': 105},
    {'time': '2025-01-04', 'value': 120},
]
chart  # Display the chart

Candlestick Chart

from lwcharts import Chart

chart = Chart(chart_type='candlestick', width=800, height=400)
chart.data = [
    {'time': '2025-01-01', 'open': 100, 'high': 110, 'low': 95, 'close': 105},
    {'time': '2025-01-02', 'open': 105, 'high': 115, 'low': 100, 'close': 112},
    {'time': '2025-01-03', 'open': 112, 'high': 120, 'low': 108, 'close': 115},
]
chart

Supported Chart Types

1. Line Chart

chart = Chart(chart_type='line')
chart.data = [{'time': '2025-01-01', 'value': 100}, ...]

2. Area Chart

chart = Chart(chart_type='area')
chart.data = [{'time': '2025-01-01', 'value': 100}, ...]

3. Candlestick Chart

chart = Chart(chart_type='candlestick')
chart.data = [
    {'time': '2025-01-01', 'open': 100, 'high': 110, 'low': 95, 'close': 105},
    ...
]

4. Bar Chart

chart = Chart(chart_type='bar')
chart.data = [
    {'time': '2025-01-01', 'open': 100, 'high': 110, 'low': 95, 'close': 105},
    ...
]

5. Histogram Chart

chart = Chart(chart_type='histogram')
chart.data = [
    {'time': '2025-01-01', 'value': 50000, 'color': '#26a69a'},
    ...
]

Customization

Chart Dimensions

chart = Chart(chart_type='line', width=1000, height=600)

Chart Options

chart = Chart(chart_type='candlestick')

# Customize appearance
chart.chart_options = {
    'layout': {
        'background': {'color': '#1e1e1e'},
        'textColor': '#d1d4dc',
    },
    'grid': {
        'vertLines': {'color': '#2a2a2a'},
        'horzLines': {'color': '#2a2a2a'},
    },
}

Series Options

# Customize candlestick colors
chart.series_options = {
    'upColor': '#26a69a',
    'downColor': '#ef5350',
    'wickUpColor': '#26a69a',
    'wickDownColor': '#ef5350',
}

Real-World Example: Stock Data

import pandas as pd
import yfinance as yf
from lwcharts import Chart

# Download stock data
ticker = yf.Ticker('AAPL')
df = ticker.history(period='1mo')

# Convert to lwc format
data = [
    {
        'time': index.strftime('%Y-%m-%d'),
        'open': row['Open'],
        'high': row['High'],
        'low': row['Low'],
        'close': row['Close'],
    }
    for index, row in df.iterrows()
]

# Display chart
chart = Chart(chart_type='candlestick', width=1000, height=600)
chart.data = data
chart

Supported Environments

Works in any Jupyter environment:

Environment	Status
PyCharm Professional	✅ Works
JupyterLab	✅ Works
Jupyter Notebook	✅ Works
VS Code	✅ Works
Google Colab	✅ Works
Kaggle Notebooks	✅ Works
Databricks	✅ Works

API Reference

Chart Class

Chart(
    chart_type='line',    # 'line', 'area', 'candlestick', 'bar', 'histogram'
    width=800,            # Chart width in pixels
    height=400            # Chart height in pixels
)

Properties

• data (list): Chart data points
• chart_type (str): Type of chart
• width (int): Chart width in pixels
• height (int): Chart height in pixels
• chart_options (dict): Chart configuration
• series_options (dict): Series configuration

Methods

• display(): Explicitly display the chart
• set_data(data): Set chart data
• add_data_point(point): Add a single data point

How It Works

Unlike traditional Jupyter widgets that require complex JavaScript builds and widget infrastructure, lwcharts uses a simple approach:

1. Creates HTML with embedded JavaScript
2. Loads lightweight-charts library from CDN (unpkg.com)
3. Generates inline JavaScript to create and populate the chart
4. Returns HTML via _repr_html_() for Jupyter display

This means:

• ✅ No RequireJS/AMD module loading issues
• ✅ No widget registration needed
• ✅ No nbextension installation
• ✅ Works immediately after pip install

Examples

See the examples.ipynb notebook for more usage examples including all chart types and customization options.

Requirements

• Python 3.7+
• IPython (automatically installed with Jupyter)
• Internet access (to load lightweight-charts from CDN)

Documentation

For chart options and customization, see the TradingView Lightweight Charts documentation.

License

Apache License 2.0 - See LICENSE file for details.

Acknowledgments

Built on TradingView Lightweight Charts

Support

• Open an issue on GitHub
• Check the examples.ipynb notebook
• Read the TradingView docs