Quant Desk — TradingView MCP

A systematic multi-strategy analysis engine that follows your TradingView chart.

Reads whatever chart you have open, fetches that instrument's history from free public sources, evaluates 311 published quantitative models against it, and produces one auditable verdict with a confidence score and a concrete position size.

Runs entirely on your machine. No TradingView subscription, no market-data vendor, no paid API. Works on the free TradingView tier.

python start.py

What it does

Related MCP server: MCP Trading Quantitative Analysis Server

The design principle

A model that cannot run honestly reports unavailable rather than degrading to a price proxy and voting anyway.

119 of the 311 models need an options chain, fundamentals, a peer universe, order-book depth, on-chain or news data. Without that feed they stand down. Every screen shows "N voting of M available of 311 in library" — never "311 models agree".

22 models approximate their published method from substituted data. Each is labelled a proxy, states exactly what was substituted, and counts for 40% of a vote.

Why family weighting

Counting BUY votes treats 30 moving-average variants as 30 independent opinions. Under that scheme whichever style is most numerous wins every vote, and adding models makes the bias worse. Models sharing a family split one family's vote.

Quick start

git clone https://github.com/ankitjha67/TradingView-MCP.git
cd TradingView-MCP
python start.py

start.py checks Python, installs what's missing, verifies the engine loads, detects any IDE (Antigravity, VS Code, Cursor, Windsurf, Claude Desktop, Zed) and offers to wire up MCP, then launches the dashboard. No IDE found means the dashboard, which needs none.

Non-technical walkthrough: EASY_SETUP_GUIDE.md

Interfaces

The monitor follows your chart. Nothing else. It analyses the symbol and interval you have open and reports on that instrument — if the reading is neutral, "stand aside" is the answer, not a prompt to go looking elsewhere.

Scanning a wider universe is a separate, explicitly-invoked tool (tools/scan_universe.py). It is never run as part of monitoring.

Keeping the monitor alive

run_monitor.sh (macOS/Linux) and run_monitor.bat (Windows) set PYTHONPATH and run the monitor detached from the shell that launched it. Both accept the same flags:

./run_monitor.sh --capital 50000 --currency INR --risk 1.0

It rewrites tv_active_chart.md and tv_active_chart.json at every bar close, and follows you automatically when you switch symbol or interval.

Monitoring cadence

Re-analysis is aligned to the close of the bar on your chart's interval — every minute on a 1m chart, every 15 minutes on 15m, daily on daily. Changing symbol or interval re-analyses immediately. Stale feeds (weekends, closed markets) are detected, labelled and backed off rather than re-reported as live.

Models read the last closed bar; re-running mid-bar re-reads a forming candle, so the signal flickers then settles. One stable reading per bar is what you act on.

When the feed goes stale — weekend, closed market, halted symbol — the monitor says so and switches to a fixed 10-minute re-check so it picks up the reopen promptly, rather than sitting on the interval's own cadence and going blind for a full day.

Data

Free public sources, tried in order: Binance (crypto) → Yahoo Finance → Stooq. Eleven intervals from 1-minute to monthly. Crypto, US and international equities, indices, forex, commodities.

TradingView is used only to observe which symbol and interval you are looking at — no TradingView data API is called, which is why the free plan suffices.

Confidence engine

Hard vetoes override any score: neutral consensus, agreement below 55%, fewer than 4 independent families, target move below 2× round-trip cost, turnover too thin to fill, or an inverted historical calibration on that instrument.

Empirical calibration measures what the score has actually been worth: it buckets past bars by signal strength and reports realised forward returns. Sometimes the answer is "has NOT reliably tracked forward returns here" — and that becomes a veto.

Position sizing

quantity = (capital × risk% × confidence_multiplier) ÷ (entry − stop)

Sizing from the stop distance holds risk constant across instruments. Then constrained by lot granularity (crypto fractional, whole shares, NIFTY 75 / BANKNIFTY 15, forex micro lots), exchange minimum order value, exposure cap, and available margin.

When a trade can't be taken it refuses with the exact remedy — "Tradeable at ₹638,545 capital or 6.39% risk per trade" — rather than silently falling back to a minimum position that would exceed your stated risk limit.

Pine Script export

All 174 price-only models export to Pine v6, plus a family-weighted consensus indicator. Every translation is checked against an independent re-implementation of Pine semantics — verified across equities, ETFs, gold and crypto on multiple intervals.

Models that cannot be faithfully translated (feed-dependent, or online training loops with no Pine equivalent) are not approximated; they're listed with the reason.

Documentation

Credits

This project builds on atilaahmettaner/tradingview-mcp by Ahmet Taner Atila, which provides the MCP server, TradingView screener and scanner services, exchange symbol lists, and the news/sentiment integrations. Licensed MIT; the original copyright is preserved in LICENSE.

The core/quant/ engine — strategy library, consensus, confidence, sizing, backtester, Pine export and their verification — is added on top.

Category coverage for macro, rates, commodity carry and options income follows the family layout of alphakit.

Licence

MIT — see LICENSE. Original work © 2025 Ahmet Taner Atila.

Not investment advice. Model output is research output. The backtester routinely shows most models failing to beat buy-and-hold, and the calibration check sometimes reports that a high score has been worth nothing on a given instrument. Those results are displayed, not hidden. Never risk money you cannot afford to lose.