Install
Start hereInstall Python 3.8+ (recommended: 3.10+).
Python 3.8Open SourceTV Scraper
Python market-data terminalPython 3.8+ + FastAPI + Uvicorn + Pydantic + Pandas + BeautifulSoup4 + TradingView WebSocket
TV Scraper.
Enterprise-grade real-time market data intelligence terminal built as a powerful wrapper around the TradingView WebSocket protocol. FastAPI backend serves 6 REST endpoints powering a zero-dependency Glassmorphic frontend. Covers symbol lookup (overview/indicators/fundamentals/OHLCV), market movers across 5 regions, dynamic screener with custom filters, and universal CSV/JSON data export.
What the README tells you
1 ⭐ | 1 fork | 263 commits | The README documents 4 feature modules with descriptions, full setup with Python virtual environment, Uvicorn server command, 6 API endpoints with query parameter examples, Swagger docs at /docs, pytest test suite location, and export/data directory structure. Languages: Python 89.9%, JS 4.1%, HTML 3.3%, CSS 2.7%.
Features
What this repo gives you.
A quick practical feature map before you open the source code.
Advanced Symbol Lookup — Fetch comprehensive data for any ticker (Stocks, Crypto, Forex) across 4 sub-modules: Overview (general info, price, performance), Indicators (RSI, MACD, Moving Averages, etc.), Fundamentals (deep financial data), Real-Time OHLCV (WebSocket streaming with adjustable timeframes 1m–1M and candle limits).
Market Movers Dashboard — Track global markets across USA, India, UK, Crypto, Forex. Categories: Gainers, Losers, Most Active, Penny Stocks, Pre-Market Gainers/Losers, After-Hours Gainers/Losers with color-coded percentage changes.
Dynamic Market Screener — Filter thousands of assets with custom parameters: Min/Max Price, Min Volume, Min Market Cap. Supports USA, India, UK, Canada, Germany, Crypto, and Global Forex.
Universal Data Export — Every module supports one-click CSV or JSON download for integration into data pipelines, backtesting engines, or spreadsheets.
6 REST API Endpoints — GET /api/overview/{exchange}/{ticker}, GET /api/indicators/{exchange}/{ticker}, GET /api/fundamentals/{exchange}/{ticker}, GET /api/ohlcv/{exchange}/{ticker}?timeframe=1d&candles=100, GET /api/movers?market=stocks-usa&category=gainers&limit=25, GET /api/screener?market=america&min_price=10&min_volume=1000000.
Swagger Documentation — Interactive API docs at http://localhost:8000/docs. Frontend served directly by FastAPI backend — zero separate frontend build needed.
Installation
Step-by-Step Setup.
Follow these steps to get TV Scraper running on your machine.
Setup Path
Run it locally, then inspect the system.
Follow the README flow first. Once the app opens locally, use the AI prompts below to trace the data flow and make focused changes.
CloneInstallRunInspect
Clone
Clone. git clone https://github.com/MrChartist/tradingview-scraper.git && cd tradingview-scraper
git clone https://github.com/MrChartist/tradingview-scraper.gitConfigure
Create virtual environment. python -m venv .venv && .venv\Scripts\activate (Windows) or source .venv/bin/activate (Mac/Linux).
python -mInstall
Install dependencies. pip install -r requirements.txt && pip install -r api/requirements.txt
pip install -r requirements.txtStep 5
Start server: uvicorn api.main:app --reload --port 8000
Step 6
Open http://localhost:8000 for the Glassmorphic terminal UI, or http://localhost:8000/docs for Swagger API docs.
Step 7
ProductionRun tests: pytest tests/
AI-Powered Setup
Set Up TV Scraper With AI.
Pick your AI tool, copy the prompt, and let it handle the entire setup and codebase walkthrough for you.
Google Antigravity
Best when you want the assistant to operate inside the editor and handle setup end-to-end.
Desktop editorGitNode or PythonModel access
Ready-to-use prompt ↓
Open https://github.com/MrChartist/tradingview-scraper. Clone the repository, inspect the README first, then set up the project locally using the exact setup steps from the README. Before editing anything, explain the stack, folder structure, data flow, required environment variables, and the safest first customization for this project: Set up the Python venv, install requirements, start Uvicorn, then explore the Swagger docs at /docs before asking an AI to map the scraper modules.
How it works: Paste the prompt into Antigravity after choosing the repo. Ask it to read the README first, run setup commands, keep changes small, and explain every file it touches before editing.
OpenAI Codex
Best when you want command-by-command setup, debugging, code edits, and verification in a local workspace.
GitTerminalNode or PythonLocal workspace
Ready-to-use prompt ↓
I want to work on https://github.com/MrChartist/tradingview-scraper. First read the README and summarize what the project does. Then give me the exact commands for my operating system to clone, install, configure environment variables, and run it locally. After it runs, inspect the codebase and propose one small change that matches this goal: Set up the Python venv, install requirements, start Uvicorn, then explore the Swagger docs at /docs before asking an AI to map the scraper modules.
How it works: Paste the prompt into Codex with the repository URL. Ask it to run the project, identify the stack from files instead of guessing, make scoped edits, and verify with build or browser checks.
Claude
Best when you want architecture explanation, codebase understanding, and a clean implementation plan before edits.
Repo accessREADME contextTerminal if using Claude Code
Ready-to-use prompt ↓
Analyze https://github.com/MrChartist/tradingview-scraper from the README and source structure. Create a clear architecture map, explain the main modules, identify setup risks, and recommend a safe implementation plan for extending it. Focus especially on: Ask the AI to map the FastAPI route structure in api/main.py: how each of the 6 endpoints maps to scraper functions in the tradingview_scraper/ package. Ask it to explain the TradingView WebSocket protocol implementation for real-time OHLCV streaming and how binary data is parsed. Ask it to trace the scraper pipeline: HTTP request → TradingView WebSocket/REST → Pandas DataFrame → JSON/CSV response. Ask it to identify rate-limit risks and caching opportunities before adding automation or scheduled jobs. Start with one focused change: add a new market region to the screener, add a custom technical indicator to the indicators endpoint, or schedule a data collection cron job.
How it works: Paste the prompt with the GitHub link. Ask Claude to read the README, map modules, list risks, and produce a practical plan before using Claude Code or another agent to implement.
AI Focus
What to Ask the AI.
After setup, use these focused questions to get the most value from your AI assistant.
AI Briefing Mode
Ask for maps, not magic.
Use these prompts after the repo runs locally. The goal is to make the AI trace the system before it changes code.
FlowLogicRiskChange
Prompt 1
Ask the AI to map the FastAPI route structure in api/main.py: how each of the 6 endpoints maps to scraper functions in the tradingview_scraper/ package.
Prompt 2
Ask it to explain the TradingView WebSocket protocol implementation for real-time OHLCV streaming and how binary data is parsed.
Prompt 3
Ask it to trace the scraper pipeline: HTTP request → TradingView WebSocket/REST → Pandas DataFrame → JSON/CSV response.
Make one focused change
Ask it to identify rate-limit risks and caching opportunities before adding automation or scheduled jobs.
Make one focused change
Start with one focused change: add a new market region to the screener, add a custom technical indicator to the indicators endpoint, or schedule a data collection cron job.
Best Next Step
Set up the Python venv, install requirements, start Uvicorn, then explore the Swagger docs at /docs before asking an AI to map the scraper modules.