streamlit-lightweight-charts-pro 0.1.1

Enhanced Streamlit wrapper for TradingView's lightweight-charts with ultra-simplified API and performance optimizations

Streamlit Lightweight Charts Pro

Professional-grade financial charting library for Streamlit applications

Streamlit Lightweight Charts Pro is a comprehensive Python library that brings TradingView's powerful lightweight-charts library to Streamlit with an intuitive, fluent API. Create interactive financial charts with ease, from simple line charts to complex multi-pane trading dashboards.

📚 Documentation

• Examples - Comprehensive code examples and tutorials
• GitHub Repository - Source code and issue tracking
• PyPI Package - Installation and package info

✨ Features

🎯 Core Capabilities

• Interactive Financial Charts: Candlestick, line, area, bar, histogram, and baseline charts
• Fluent API: Method chaining for intuitive chart creation
• Multi-Pane Charts: Synchronized charts with multiple series and timeframes
• Trade Visualization: Built-in support for displaying trades with markers and annotations
• Advanced Annotations: Text, arrows, and shape annotations with layering
• Responsive Design: Auto-sizing charts that adapt to container dimensions
• Pandas Integration: Seamless DataFrame to chart data conversion

🚀 Advanced Features

• Price-Volume Charts: Pre-built candlestick + volume combinations
• Range Switchers: Professional time range selection (1D, 1W, 1M, 3M, 6M, 1Y, ALL)
• Auto-Sizing: Responsive charts with min/max constraints
• Custom Styling: Full control over colors, fonts, and visual elements
• Performance Optimized: Handles large datasets efficiently
• Type Safety: Comprehensive type hints and validation

🔧 Developer Experience

• Production Ready: Comprehensive logging, error handling, and security
• Well Documented: Complete API documentation with examples
• Tested: 450+ unit tests with 95%+ coverage
• Code Quality: Black formatting, type hints, and linting compliance

📦 Installation

pip install streamlit_lightweight_charts_pro

🚀 Quick Start

Basic Line Chart

import streamlit as st
from streamlit_lightweight_charts_pro import Chart, LineSeries
from streamlit_lightweight_charts_pro.data import SingleValueData

# Create sample data
data = [
    SingleValueData("2024-01-01", 100),
    SingleValueData("2024-01-02", 105),
    SingleValueData("2024-01-03", 103),
    SingleValueData("2024-01-04", 108),
]

# Create and render chart
chart = Chart(series=LineSeries(data, color="#2196F3"))
chart.render(key="basic_line_chart")

Candlestick Chart with Volume

import streamlit as st
import pandas as pd
from streamlit_lightweight_charts_pro import PriceVolumeChart

# Load your OHLCV data
df = pd.read_csv('stock_data.csv', index_col='date', parse_dates=True)

# Create price-volume chart
chart = PriceVolumeChart(
    df=df,
    price_type='candlestick',
    price_height=400,
    volume_height=100
)
chart.render(key="price_volume_chart")

Fluent API with Method Chaining

from streamlit_lightweight_charts_pro import create_chart, create_text_annotation

# Create chart with fluent API
chart = (create_chart()
         .add_line_series(data, color="#ff0000")
         .set_height(400)
         .set_width(800)
         .add_annotation(create_text_annotation("2024-01-01", 100, "Start Point"))
         .build())
chart.render(key="fluent_chart")

📊 Chart Types

Single Series Charts

# Line Chart
LineSeries(data, color="#2196F3")

# Area Chart  
AreaSeries(data, color="#4CAF50", fill_color="rgba(76, 175, 80, 0.3)")

# Bar Chart
BarSeries(data, color="#FF9800")

# Histogram
HistogramSeries(data, color="#9C27B0")

# Baseline Chart
BaselineSeries(data, base_value=100, top_color="#4CAF50", bottom_color="#F44336")

Candlestick Charts

from streamlit_lightweight_charts_pro.data import OhlcData

# Create OHLC data
ohlc_data = [
    OhlcData("2024-01-01", 100, 105, 98, 102),
    OhlcData("2024-01-02", 102, 108, 101, 106),
    OhlcData("2024-01-03", 106, 110, 104, 108),
]

# Create candlestick series
CandlestickSeries(ohlc_data, up_color="#4CAF50", down_color="#F44336")

Multi-Pane Charts

# MultiPaneChart removed - using Chart instead

# MultiPaneChart removed - using individual charts instead
chart = Chart(series=[CandlestickSeries(ohlc_data), HistogramSeries(volume_data)])
chart.render(key="multi_pane")

🎨 Advanced Features

Trade Visualization

from streamlit_lightweight_charts_pro.data import Trade, TradeType, TradeVisualization

# Create trades
trades = [
    Trade(
        entry_time="2024-01-01",
        entry_price=100,
        exit_time="2024-01-05", 
        exit_price=105,
        quantity=100,
        trade_type=TradeType.LONG,
        id="T001"
    )
]

# Add trades to chart
chart = Chart(
    series=[CandlestickSeries(ohlc_data)],
    trades=trades,
    trade_visualization=TradeVisualization.BOTH  # Shows markers and rectangles
)

Annotations

from streamlit_lightweight_charts_pro.data.annotation import (
    create_text_annotation, create_arrow_annotation, create_shape_annotation
)

# Text annotation
text_ann = create_text_annotation("2024-01-01", 100, "Important Event")

# Arrow annotation
arrow_ann = create_arrow_annotation("2024-01-02", 105, "Trend Change")

# Shape annotation
shape_ann = create_shape_annotation("2024-01-03", 103, "rectangle", "#FF9800")

# Add to chart
chart.add_annotation(text_ann)

Range Switcher

# Add professional time range switching
chart_options = {
    "rangeSwitcher": {
        "ranges": [
            {"label": "1D", "seconds": 86400},
            {"label": "1W", "seconds": 604800},
            {"label": "1M", "seconds": 2592000},
            {"label": "3M", "seconds": 7776000},
            {"label": "6M", "seconds": 15552000},
            {"label": "1Y", "seconds": 31536000},
            {"label": "ALL", "seconds": None}
        ],
        "position": "top-right",
        "visible": True,
        "defaultRange": "1M"
    }
}

📈 Data Sources

From Pandas DataFrames

import pandas as pd

# Load data
df = pd.read_csv('stock_data.csv', index_col='date', parse_dates=True)

# Create chart directly from DataFrame
chart = Chart(series=CandlestickSeries.from_dataframe(
    df=df,
    column_mapping={
        'time': 'date',
        'open': 'open',
        'high': 'high',
        'low': 'low',
        'close': 'close'
    }
))

From CSV Files

# Direct CSV loading with custom column mapping
chart = PriceVolumeChart(
    df=pd.read_csv('data.csv'),
    column_mapping={
        'time': 'Date',
        'open': 'Open',
        'high': 'High', 
        'low': 'Low',
        'close': 'Close',
        'volume': 'Volume'
    }
)

From APIs

import yfinance as yf

# Fetch data from Yahoo Finance
ticker = yf.Ticker("AAPL")
df = ticker.history(period="1y")

# Create chart
chart = PriceVolumeChart(df=df)
chart.render(key="aapl_chart")

🎛️ Customization

Chart Options

from streamlit_lightweight_charts_pro.charts.options import ChartOptions

options = ChartOptions(
    height=500,
    width=800,
    layout={
        "background": {"type": "solid", "color": "white"},
        "textColor": "black"
    },
    grid={
        "vertLines": {"color": "rgba(197, 203, 206, 0.5)"},
        "horzLines": {"color": "rgba(197, 203, 206, 0.5)"}
    },
    crosshair={"mode": 1},
    rightPriceScale={
        "borderColor": "rgba(197, 203, 206, 0.8)",
        "scaleMargins": {"top": 0.1, "bottom": 0.2}
    }
)

chart = Chart(series=series, options=options)

Series Styling

# Custom line series
LineSeries(
    data=data,
    color="#2196F3",
    line_width=2,
    line_type="solid",
    crosshair_marker_visible=True,
    last_value_visible=True,
    price_line_color="#2196F3",
    price_line_width=1
)

# Custom candlestick series
CandlestickSeries(
    data=ohlc_data,
    up_color="#4CAF50",
    down_color="#F44336",
    border_visible=False,
    wick_up_color="#4CAF50",
    wick_down_color="#F44336"
)

🔧 Advanced Usage

Auto-Sizing Charts

# Responsive chart that adapts to container
chart_options = {
    "autoSize": True,
    "minWidth": 300,
    "maxWidth": 1200,
    "minHeight": 200,
    "maxHeight": 800
}

Multi-Pane Synchronization

# MultiPaneChart removed - using individual charts instead
chart = Chart(series=[CandlestickSeries(ohlc_data), HistogramSeries(volume_data), LineSeries(rsi_data)])

Custom Annotations with Layers

# Create annotation layer
layer = AnnotationLayer("analysis", visible=True)

# Add annotations to layer
layer.add_annotation(create_text_annotation("2024-01-01", 100, "Support"))
layer.add_annotation(create_text_annotation("2024-01-05", 110, "Resistance"))

# Add layer to chart
chart.add_annotation_layer(layer)

📚 Examples

Check out the comprehensive examples in the examples/ directory:

• candlestick_chart.py - Basic candlestick chart
• price_volume_chart.py - Price and volume combination
• multi_pane_chart.py - Multi-pane synchronized charts
• trade_drawing_example.py - Trade visualization
• range_switcher_example.py - Time range switching
• method_chaining_demo.py - Fluent API examples
• AutoSizingExample.py - Responsive charts

🛠️ Development

Installation for Development

# Clone the repository
git clone https://github.com/your-username/streamlit_lightweight_charts_pro.git
cd streamlit_lightweight_charts_pro

# Install in development mode
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=streamlit_lightweight_charts_pro

# Run specific test categories
pytest tests/unit/
pytest tests/integration/
pytest tests/e2e/

Code Quality

# Format code
black streamlit_lightweight_charts_pro/

# Sort imports
isort streamlit_lightweight_charts_pro/

# Lint code
pylint streamlit_lightweight_charts_pro/

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass
6. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• TradingView for the lightweight-charts library
• Streamlit for the amazing web app framework
• All contributors and users of this library

📞 Support

• Documentation: Check the examples and docstrings
• Issues: Report bugs and request features on GitHub
• Discussions: Join community discussions
• Email: For security issues, contact [e-mail]

🔗 Links

• PyPI Package
• GitHub Repository
• TradingView Lightweight Charts
• Streamlit Documentation

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