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

Agent desk (second opinion)

The engine's rule is that an LLM explains what the models found and never originates a signal. TauricResearch/TradingAgents is the opposite: a team of LLM agents — fundamentals, news, sentiment and technical analysts, then a bull/bear debate and a three-way risk review — argues its way to a BUY/SELL/HOLD.

Both hold at once, because the desk is wired as a second reader, not a voter:

Measured on AAPL daily: consensus BUY +0.166, score 57.1, grade C in every case; size ×0.325 with no desk, with an absent desk, and with an agreeing desk — and ×0.163 with a disagreeing one. Direction and score never moved.

Why include it at all. Every run reports ~119 of 311 models standing down for "missing data feed" — options chains, fundamentals, on-chain, news, sentiment. Those are exactly the inputs TradingAgents has. It is not a better price model; it reads a different part of the problem, and the one thing it is trusted to do is make you take less when it sees something the price series cannot show.

It runs in its own virtualenv, deliberately

tradingagents resolves pandas 3.0.5; this engine is 21 modules and 311 models on pandas 2.3.3, and pyproject.toml sets no upper bound — so a plain pip install silently upgrades pandas underneath the library. The install also pulls chainlit, redis, textual and ~40 opentelemetry instrumentation packages.

So the desk lives in .venv-agents/ and is reached over a subprocess with JSON on the wire. Each side keeps the dependency tree it wants; the cost is one process spawn per debate, which already takes minutes.

python tools/setup_agent_desk.py          # create the venv and install
python tools/setup_agent_desk.py --check  # status, changes nothing

It reuses whatever LLM provider is configured in Settings — including NVIDIA NIM, which tradingagents does not name but reaches as an OpenAI-compatible endpoint.

Cadence and cost

A debate is many model calls over several minutes, and this engine re-analyses at every bar close. So a verdict is cached per (ticker, trading date) and never started automatically — the Live Signal tab has a button. News and fundamentals do not change between two 1-minute bars, which is the resolution the debate actually reads at. Once run, every refresh that day reuses it, including the monitor.

Statistical significance

Every backtest reports a t-statistic beside its Sharpe:

t = annualised Sharpe x sqrt(years observed)

A Sharpe on its own says very little. 1,500 one-minute bars is six trading days, and over six days a Sharpe of 8 carries t ≈ 1.0 — indistinguishable from luck. The engine now says so instead of ranking it first.

The convention and the 1.96 threshold come from the replication catalogue in paperswithbacktest/awesome-systematic-trading, which publishes both figures for 1,687 replicated papers and notes plainly that half of them fail the bar. The identity was verified against all 61 of its published rows before being trusted here (largest deviation 0.12, pure rounding) — and is re-verified by tools/sync_replication_catalogue.py, which stores the table locally as JSON and warns if upstream ever changes convention.

python tools/sync_replication_catalogue.py            # refresh the catalogue
python tools/sync_replication_catalogue.py --compare  # scale against our library

What it changes

compare_strategies reports significant and significantly_losing next to the ranking, the Backtest Lab shows a t-stat column and warns when nothing clears the bar, and the performance report adds a caveat when the headline Sharpe is undetermined.

Measured on real data at the time of writing:

Not one model in the library clears significance on either instrument, and on the 1-minute chart 157 of 174 are significantly negative. That is the point of measuring it: the top row of a Sharpe-sorted table reads like a discovery, and usually is not.

For scale, across the catalogue's 61 published replications the best Sharpe is 3.39 and the median 1.06 — each measured over 16+ years. A model here reporting Sharpe 8 over six days is not four times better than the best replicated paper in the set.

Research reading (not a data feed)

A panel in Live Signal and a CLI for reading public news and pages about the instrument you are looking at, routed via Panniantong/Agent-Reach.

python tools/research.py --doctor            # what this machine can read
python tools/research.py                     # news for the open chart
python tools/research.py --url https://...   # read one public page

It deliberately does not feed the models, and cannot. Every voting model implements score(f) -> pd.Series over the whole frame — that historical path is what the backtester replays, what the confidence engine calibrates, and what earns a t-statistic. A web read returns what a page says now; there is no way to ask it what was being said on each of the previous 1,499 bars. A model fed this way could fill the last element of the series and nothing else, so it could never be backtested or clear the significance bar. Marking DataNeed.NEWS satisfied on that basis would flip ten sentiment models to "voting" while leaving them unverifiable — the exact failure the DataNeed enum exists to prevent.

A test asserts nothing under core/quant/ imports it, so the separation is enforced rather than promised. core/research.py sits outside the engine for the same reason.

Zero-configuration sources only

Agent-Reach tags each channel with a tier. Tier 0 needs no configuration; tier 1 and above work only through a personal logged-in session or exported cookies — its own notes say Reddit's anonymous API is blocked and only the logged-in route remains. Driving those from an unattended monitor would mean acting on your accounts against the platforms' terms, so the ceiling is enforced in code (MAX_TIER = 0), not left to a docstring.

Note on installing it. pip install agent-reach fetches a different project — PyPI's package of that name is jgalea/agent-reach, unrelated to this one. Install from source: pip install git+https://github.com/Panniantong/Agent-Reach.

If you want social data properly

The honest route is to record these readings forward into a dated archive and wait for history to accumulate — not to backfill from a live endpoint, whose results are ranked by today's engagement and silently omit whatever has since been deleted. That is survivorship bias baked into the input, and a backtest built on it would be worse than no backtest.

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.

The agent desk wraps TauricResearch/TradingAgents by Tauric Research, run unmodified in its own environment and consulted as a second opinion.

The Sharpe significance standard, and the replication figures used to calibrate against it, come from paperswithbacktest/awesome-systematic-trading.

Research reading is routed through Panniantong/Agent-Reach by Panniantong, restricted to its zero-configuration channels.

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.