Rajandran R Creator of OpenAlgo - OpenSource Algo Trading framework for Indian Traders. Telecom Engineer turned Full-time Derivative Trader. Mostly Trading Nifty, Banknifty, High Liquid Stock Derivatives. Trading the Markets Since 2006 onwards. Using Market Profile and Orderflow for more than a decade. Designed and published 100+ open source trading systems on various trading tools. Strongly believe that market understanding and robust trading frameworks are the key to the trading success. Building Algo Platforms, Writing about Markets, Trading System Design, Market Sentiment, Trading Softwares & Trading Nuances since 2007 onwards. Author of Marketcalls.in

Integrating Tradingview Lightweight Charts with Yahoo Finance Data – Python Flask Application

2 min read

Hey traders! If you’re looking for a way to visualize your stock data dynamically and interactively, you’re in for a treat. Today, we’ll explore how to create a web application using TradingView’s Lightweight Charts and Yahoo Finance. This tool fetches real-time stock data, applies technical indicators like EMA and RSI, and offers a smooth, interactive experience. Plus, it’s open-source, so you can tweak it to your heart’s content!

Why TradingView Lightweight Charts?

TradingView’s Lightweight Charts are perfect for traders who want clean, responsive, and highly customizable charting solutions. Whether you’re analyzing market trends, tracking stock performance, or just keeping an eye on your favorite stocks, these charts offer the precision and clarity you need without any unnecessary bloat.

Why Open Source?

I believe in the power of community and collaboration. By keeping this project open-source, I’m inviting you to customize, enhance, and build upon this tool. Whether you want to add new features, integrate additional data sources, or just tweak the visuals to your liking, the code is all yours to play with. Let’s make trading tools better together!

Features of the Application

Real-Time Stock Data: Fetch and display real-time stock data from Yahoo Finance.

Technical Indicators: Apply and visualize technical indicators such as EMA (Exponential Moving Average) and RSI (Relative Strength Index).

Interactive Charts: Use TradingView’s Lightweight Charts for a smooth and interactive data visualization experience.

Theme Toggle: Switch between light and dark themes for a better user experience.

Watchlist: Maintain a watchlist to quickly switch between different stock symbols.

Crosshair Synchronization: Synchronize crosshair positions across multiple charts for better data comparison.

Working Mechanism

1. Data Fetching: The backend of the application, built with Flask, fetches stock data from Yahoo Finance using the yfinance library. It calculates EMA and RSI using the pandas_ta library.

2. Data Processing: The fetched data is processed and formatted to be used by the frontend. EMA and RSI values are included in the data and NaN values are handled appropriately.

3. Data Serving: Flask serves the processed data to the frontend through various API endpoints.

4. Interactive Visualization: The frontend uses TradingView’s Lightweight Charts to display the stock data and technical indicators interactively. Users can add stocks to a watchlist and toggle between light and dark themes.

1. Flask (Backend)

Description: Flask is a lightweight web framework for Python. It’s used to create web applications and APIs.

Usage in the Application:

• Serving HTML templates and static files.

• Defining API endpoints to fetch stock data.

• Handling HTTP requests and responses.

2. Yahoo Finance API (yfinance)

Description: yfinance is a Python library that enables easy access to historical market data from Yahoo Finance.

Usage in the Application:

• Fetching historical stock data.

• Providing real-time data for various stock symbols.

• Supporting different intervals like daily, weekly, and monthly data.

3. Pandas (Data Processing)

Description: Pandas is a powerful data manipulation and analysis library for Python.

Usage in the Application:

• Storing and processing fetched stock data in DataFrames.

• Cleaning and preparing data for visualization.

• Handling date and time operations.

4. Pandas_TA (Technical Analysis)

Description: pandas_ta is a Python library that provides technical analysis indicators using Pandas.

Usage in the Application:

• Calculating technical indicators like EMA (Exponential Moving Average) and RSI (Relative Strength Index).

• Adding these indicators to the stock data for visualization.

5. TradingView Lightweight Charts (Frontend)

Description: TradingView Lightweight Charts is a library that provides high-performance, interactive financial charts.

Usage in the Application:

• Rendering interactive candlestick charts.

• Displaying technical indicators like EMA and RSI.

• Synchronizing multiple charts for comparative analysis.

• Providing a seamless user experience with features like crosshair synchronization.

This open-source project is just a starting point. Feel free to modify, extend, and enhance it as you see fit. Together, we can create even more innovative tools for the trading community. Happy trading!

Rajandran R Creator of OpenAlgo - OpenSource Algo Trading framework for Indian Traders. Telecom Engineer turned Full-time Derivative Trader. Mostly Trading Nifty, Banknifty, High Liquid Stock Derivatives. Trading the Markets Since 2006 onwards. Using Market Profile and Orderflow for more than a decade. Designed and published 100+ open source trading systems on various trading tools. Strongly believe that market understanding and robust trading frameworks are the key to the trading success. Building Algo Platforms, Writing about Markets, Trading System Design, Market Sentiment, Trading Softwares & Trading Nuances since 2007 onwards. Author of Marketcalls.in

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