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Introduction and Overview

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levi edited this page Nov 6, 2025 · 2 revisions

Introduction and Overview

Table of Contents

  1. Introduction
  2. Core Value Proposition
  3. High-Level Architecture
  4. Key Features
  5. Practical Examples
  6. Component Relationships
  7. Common Use Cases
  8. Conclusion

Introduction

PyTradingView is a comprehensive Python client library for the TradingView Advanced Charts API, enabling developers to leverage Python's robust data science ecosystem to build custom indicators, integrate real-time data feeds, and create rich charting experiences. This library bridges the gap between Python's analytical capabilities and TradingView's powerful visualization tools, allowing users to develop sophisticated trading applications without relying on JavaScript. The library supports a wide range of features including custom indicators, real-time data integration, multi-chart layouts, and theme customization, making it suitable for algorithmic trading systems, technical analysis tools, and market data visualization platforms.

Core Value Proposition

The core value proposition of PyTradingView lies in its ability to seamlessly integrate Python's data science ecosystem with TradingView's advanced charting capabilities. By providing a Pythonic interface to the TradingView Widget API, it enables developers to utilize popular Python libraries such as pandas, NumPy, and scikit-learn for data analysis and machine learning while leveraging TradingView's professional-grade charting features. This integration allows for the creation of custom technical indicators, real-time data processing, and complex trading strategies using Python, which is particularly advantageous for data scientists and quantitative analysts who prefer Python over JavaScript. The library's modular design and event-driven architecture ensure high performance and scalability, making it ideal for both prototyping and production environments.

High-Level Architecture

The architecture of PyTradingView is built around a Python-JavaScript bridge that facilitates bidirectional communication between the Python backend and the TradingView frontend. At the core of this architecture is the TVBridge class, which manages HTTP connections and WebSocket communication, enabling asynchronous method invocation and event-driven updates. The TVWidget class serves as the main controller for interacting with TradingView charts, providing methods for chart management, theme customization, and event subscription. The indicator engine, implemented as TVEngine, follows a modular design with multiple mixins for configuration management, indicator loading, and runtime control. This architecture ensures thread safety, supports dynamic configuration updates, and provides a robust foundation for building complex trading applications.

graph TD
A[Python Application] --> B[TVEngine]
B --> C[TVBridge]
C --> D[TradingView Widget]
D --> E[JavaScript Frontend]
C --> F[Datafeed]
F --> G[Market Data Source]
B --> H[Indicator Registry]
H --> I[Custom Indicators]
D --> J[Chart Components]
J --> K[Visual Elements]
Loading

**Diagram sources **

Key Features

PyTradingView offers a comprehensive set of features designed to enhance the development of trading applications. These include full support for the TradingView Advanced Charts API, enabling the creation of custom indicators with Python. The library supports over 100 drawing shapes, including trendlines, arrows, patterns, and Fibonacci tools, allowing for detailed chart annotations. Real-time data integration is facilitated through a custom datafeed interface, supporting WebSocket connections for live market data. The library also provides high-performance asynchronous architecture, intuitive Pythonic API design, and support for multi-chart layouts. Theme customization allows for full control over the appearance of charts, while the modular design ensures clean separation of concerns and easy maintenance.

Practical Examples

Basic Engine Setup

The following example demonstrates how to initialize the PyTradingView engine and set up custom indicators:

from pytradingview import TVEngine

if __name__ == '__main__':
    # Initialize the engine
    engine = TVEngine()
    
    # Setup and run with custom indicators
    engine.get_instance().setup('./indicators').run()

Creating Custom Indicators

Custom indicators can be created by inheriting from the TVIndicator base class and implementing the required methods:

from pytradingview.indicators import (
    TVIndicator,
    TVSignal,
    TVDrawable,
    IndicatorConfig,
    InputType,
    InputDefinition,
    register_indicator
)
import pandas as pd
from typing import List, Tuple

@register_indicator(name="MyIndicator", enabled=True)
class MyCustomIndicator(TVIndicator):
    """
    Custom indicator example
    """
    
    def get_config(self) -> IndicatorConfig:
        """Define indicator configuration"""
        return IndicatorConfig(
            name="My Custom Indicator",
            version="1.0.0",
            description="A simple custom indicator",
            author="Your Name",
            enabled=True,
            inputs=[
                InputDefinition(
                    id="period",
                    display_name="Period",
                    type=InputType.INTEGER,
                    default_value=14,
                    min_value=1,
                    max_value=100
                )
            ]
        )
    
    def calculate(self, df: pd.DataFrame) -> Tuple[List[TVSignal], List[TVDrawable]]:
        """Calculate indicator signals"""
        signals = []
        drawables = []
        
        # Your indicator logic here
        # ...
        
        return signals, drawables

Working with Charts

Interacting with charts involves creating shapes and managing chart properties:

from pytradingview import TVWidget, TVChart

# Get the widget instance
widget = TVWidget.get_instance("widget_id")

# Get active chart
chart = await widget.activeChart()

# Create a shape on the chart
from pytradingview.shapes import TVTrendLine, TVShapePoint

trend_line = TVTrendLine()
await chart.createMultipointShape(
    points=[
        TVShapePoint(time=1234567890, price=50000),
        TVShapePoint(time=1234567900, price=51000)
    ],
    shape=trend_line
)

Custom Datafeed

Implementing a custom datafeed allows for real-time data integration:

from pytradingview.datafeed import (
    TVDatafeed,
    TVLibrarySymbolInfo,
    TVBar,
    TVHistoryMetadata
)

class MyDatafeed(TVDatafeed):
    """Custom datafeed implementation"""
    
    def resolveSymbol(self, symbolName, onResolve, onError, extension=None):
        """Resolve symbol information"""
        symbol_info = TVLibrarySymbolInfo(
            name=symbolName,
            ticker=symbolName,
            description=f"{symbolName} Description",
            type="crypto",
            session="24x7",
            exchange="MyExchange",
            listed_exchange="MyExchange",
            timezone="Etc/UTC",
            format="price",
            pricescale=100,
            minmov=1,
            has_intraday=True,
            supported_resolutions=["1", "5", "15", "60", "D", "W", "M"]
        )
        onResolve(symbol_info)
    
    def getBars(self, symbolInfo, resolution, periodParams, onResult, onError):
        """Get historical bars"""
        # Fetch your data here
        bars = [
            TVBar(
                time=1234567890000,  # milliseconds
                open=50000,
                high=51000,
                low=49000,
                close=50500,
                volume=1000
            ),
            # ... more bars
        ]
        
        metadata = TVHistoryMetadata(noData=False)
        onResult(bars, metadata)

Component Relationships

The components of PyTradingView work together to provide a seamless integration with TradingView widgets. The TVEngine serves as the central controller, managing the lifecycle of indicators and coordinating communication between the Python backend and the TradingView frontend. The TVBridge facilitates bidirectional communication, handling RPC calls and event propagation. The TVWidget class provides access to chart functionality, while the TVChart class manages individual chart instances. The indicator engine uses a registry pattern to manage custom indicators, allowing for dynamic loading and activation. The datafeed system integrates with market data sources, providing real-time updates to the charts. This modular architecture ensures that each component has a well-defined responsibility, promoting maintainability and extensibility.

classDiagram
class TVEngine {
+setup(indicators_dir : str)
+run()
+activate_indicator(name : str)
+deactivate_indicator(name : str)
}
class TVBridge {
+register_config_provider(config : TVWidgetConfig)
+register_chart_ready_callback(callback : Callable)
+start_http_server(on_port : int)
+connect_to_node_server()
}
class TVWidget {
+activeChart()
+chart(index : int)
+changeTheme(theme_name : str)
+applyOverrides(overrides : Dict)
}
class TVChart {
+createMultipointShape(points : List[TVShapePoint], shape : TVShape)
+onIntervalChanged(callback : Callable)
+onSymbolChanged(callback : Callable)
}
class TVDatafeed {
+resolveSymbol(symbolName : str, onResolve : Callable, onError : Callable)
+getBars(symbolInfo : TVLibrarySymbolInfo, resolution : str, periodParams : Dict, onResult : Callable, onError : Callable)
}
class IndicatorRegistry {
+register(indicator_class : Type[TVIndicator], name : str, enabled : bool)
+create_instance(name : str)
+enable(name : str)
+disable(name : str)
}
TVEngine --> TVBridge : "uses"
TVEngine --> IndicatorRegistry : "manages"
TVBridge --> TVWidget : "communicates with"
TVWidget --> TVChart : "controls"
TVChart --> TVDatafeed : "requests data from"
IndicatorRegistry --> TVIndicator : "creates instances of"
Loading

**Diagram sources **

Common Use Cases

PyTradingView is particularly well-suited for several common use cases in the financial technology domain. Algorithmic trading systems can leverage the library to implement complex trading strategies using Python's data analysis capabilities while visualizing results on TradingView charts. Technical analysis tools can be developed to create custom indicators and drawing tools, enhancing the analytical capabilities of traders. Market data visualization platforms can use the library to integrate real-time data feeds and create interactive dashboards. The library's support for multi-chart layouts and theme customization makes it ideal for building comprehensive trading workstations. Additionally, the event-driven architecture and asynchronous design enable the development of responsive and scalable applications.

Conclusion

PyTradingView provides a powerful and flexible solution for integrating Python's data science ecosystem with TradingView's advanced charting capabilities. Its modular architecture, comprehensive feature set, and intuitive API design make it an excellent choice for developing sophisticated trading applications. By enabling the use of Python for custom indicator development and real-time data processing, the library empowers data scientists and quantitative analysts to build complex trading systems without the limitations of JavaScript. The seamless integration with TradingView widgets, combined with support for multi-chart layouts and theme customization, ensures that applications built with PyTradingView can deliver professional-grade user experiences. As the library continues to evolve, it is poised to become an essential tool for developers in the financial technology space.

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