tradingview-mcp-chefy

tradingview-mcp-chefy

Token-efficient TradingView MCP for traders who backtest Pine strategies; aggregates strategy results and trade data server-side to reduce token usage.

Category
Visit Server

README

tradingview-mcp-chefy

Token-efficient TradingView MCP for traders who actually backtest.

This is a fork built specifically for strategy testing. The existing TradingView MCPs work well for chart reading and morning workflows, but they burn tokens hard when you're iterating on Pine strategies — every backtest read dumps the full trade list, equity curve, and console output into your context. This fork rewrites those reads to aggregate inside TradingView's runtime before the data crosses the wire, returning summaries by default and detail on demand.

[!WARNING] Not affiliated with TradingView Inc. or Anthropic. This tool connects to your locally running TradingView Desktop app via Chrome DevTools Protocol. Review the Disclaimer before use.

[!IMPORTANT] Requires a valid TradingView subscription. This tool does not bypass any TradingView paywall. It reads from and controls the TradingView Desktop app already running on your machine.

[!NOTE] All processing is local. Nothing is sent anywhere. No TradingView data leaves your machine.


Credits

This project stands on two pieces of prior work:

If those repos help you, go star them.


What's Different in This Fork

Area Upstream behavior This fork Status
data_get_strategy_results Returns full strategy tester payload (~67K tokens) In-browser aggregation: returns ~40 curated metrics + computed expectancy + first/last trade timestamps. verbose: true for the full raw payload shipped (v0.1)
data_get_trades max_trades cap only Cursor-paginated. Default limit: 20. all: true for full list planned
data_get_equity Full curve point-by-point Downsampled to N buckets (default 50). verbose: true for raw planned
pine_get_console All console output New pine_console_errors filters server-side planned
Strategy detector Stops at first is_price_study === false source — latches onto Volume / EMA Score-based: scans all sources and picks the one with strongest strategy signals (ordersData, _strategyOrdersPaneView, _reportData, is_strategy meta) shipped (v0.1)

Measured token cost — data_get_strategy_results

Real numbers from a 121-week (255-trade) backtest of a Pine strategy on COMEX:GC1! 1H:

Mode Output size Approx tokens
Default (summary) ~1.7 KB ~425
verbose: true 268,792 chars ~67,000

~99% reduction per call in default mode. On a heavy iteration session (20+ runs), this is the difference between burning ~$20 of tokens on result reads alone vs ~$0.10.

The aggregation runs inside TradingView's Electron runtime — same CDP round-trip, ~150× less data crossing the wire.


Status

v0.1 — first aggregation tool (data_get_strategy_results) shipped, with hardened strategy detector and trade-aggregate computation in-browser. Other tools still match upstream behavior.

Roadmap:

  • [x] Fork repo, set up structure
  • [x] data_get_strategy_results summary mode + verbose escape hatch
  • [x] Strategy detector hardening (score-based, no is_price_study gate)
  • [x] In-browser trade-derived aggregates (win rate, PF, max DD, expectancy)
  • [ ] data_get_equity downsampling
  • [ ] data_get_trades cursor pagination
  • [ ] pine_console_errors filtered tool
  • [ ] Token-cost benchmark video
  • [ ] PR uncontroversial fixes back to LewisWJackson upstream
  • [ ] v0.2 release

Quick Start

Same setup as upstream for now. When the new tools land in v0.2, the install path stays identical — only the mcpServers config name might change.

Prerequisites

  • TradingView Desktop app (paid subscription required for real-time / strategy data)
  • Node.js 18+
  • Claude Code (or any MCP client)
  • macOS, Windows, or Linux

Install

git clone https://github.com/Chefy3x/tradingview-mcp-chefy.git ~/tradingview-mcp-chefy
cd ~/tradingview-mcp-chefy
npm install

Launch TradingView with debug port

Mac:

./scripts/launch_tv_debug_mac.sh

Windows:

scripts\launch_tv_debug.bat

Linux:

./scripts/launch_tv_debug_linux.sh

Add to Claude Code

Add to ~/.claude/.mcp.json (merge with existing servers):

{
  "mcpServers": {
    "tradingview": {
      "command": "node",
      "args": ["/Users/YOUR_USERNAME/tradingview-mcp-chefy/src/server.js"]
    }
  }
}

Replace YOUR_USERNAME with your actual username (echo $USER on Mac/Linux).

Verify

Restart Claude Code and ask: "Use tv_health_check to verify TradingView is connected."


Architecture

Claude Code  ←→  MCP stdio  ←→  src/server.js  ←→  CDP :9222  ←→  TradingView Desktop (Electron)
  • Connection: Chrome DevTools Protocol on localhost:9222
  • Aggregation: for backtest reads, the JS expression sent over CDP performs the reduction inside TradingView's runtime before returning. Same network hop, ~200x less data crossing the boundary.
  • No external network calls — everything runs locally
  • Zero added dependencies beyond what upstream uses

Contributing

Two-way street with upstream:

  • Token efficiency improvements to existing tools → I'll PR these back to LewisWJackson upstream so everyone benefits
  • New backtest_* family of tools → stays in this fork (changes the mental model of how the MCP is used)

If you spot a bug in shared code paths, open an issue here and I'll route it.


Disclaimer

This project is provided for personal, educational, and research purposes only.

This tool uses the Chrome DevTools Protocol (CDP), a standard debugging interface built into all Chromium-based applications. It does not reverse engineer any proprietary TradingView protocol, connect to TradingView's servers, or bypass any access controls. The debug port must be explicitly enabled by the user via a standard Chromium command-line flag.

By using this software you agree that:

  1. You are solely responsible for ensuring your use complies with TradingView's Terms of Use and all applicable laws.
  2. This tool accesses undocumented internal TradingView APIs that may change at any time.
  3. This tool must not be used to redistribute, resell, or commercially exploit TradingView's market data.
  4. The authors are not responsible for any account bans, suspensions, or other consequences.

Use at your own risk.

License

MIT — see LICENSE. Inherited from upstream. Applies to source code only, not to TradingView's software, data, or trademarks.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured