tradingview-mcp-india

A fork of tradingview-mcp-server by Atila Ahmettaner (MIT), with Indian stock-market (NSE / BSE) support added.

What this fork adds

• Registered NSE and BSE as stock exchanges mapped to TradingView's india market (src/tradingview_mcp/core/utils/validators.py).

• Bundled symbol lists coinlist/nse.txt and coinlist/bse.txt (top ~1,000 most-liquid tickers each), so symbol-iterating tools work too.

• Indian news: new india RSS category (Economic Times, Moneycontrol, LiveMint, Hindu BusinessLine) and a dedicated india_news MCP tool.

• Indian sentiment: new india Reddit group (r/IndianStockMarket, r/IndianStreetBets, r/DalalStreetTalks, r/StockMarketIndia, r/IndiaInvestments).

• Indian indices in market_snapshot: Nifty 50 (^NSEI), Sensex (^BSESN), Bank Nifty (^NSEBANK), plus USDINR FX.

• combined_analysis routing: NSE/BSE now pull Indian news + Indian sentiment (previously fell through to Reuters/US subreddits and returned nothing).

• SSL reliability fix (important): all outbound HTTPS (Yahoo Finance, Reddit, RSS) now uses a certifi-backed SSL context — via proxy_manager._https_handler() for the shared opener and a dedicated fetch in news_service, plus browser User-Agent and manual HTTP 308 redirect following. Without this, the macOS CERTIFICATE_VERIFY_FAILED error silently broke every network tool (Yahoo price, snapshot, backtest, sentiment, news) — not just India.

Everything else is unchanged from upstream v0.7.1.

Related MCP server: Indian Stock Exchange API2 MCP Server

Install (editable)

python3 -m venv venv && source venv/bin/activate
pip install -e .

Editable means your local edits to src/ are live immediately — no reinstall, and a pip install --upgrade of the original PyPI package can never overwrite this fork.

Usage for Indian markets

Screener / technical-analysis tools — pass exchange="NSE" (or "BSE"), use 1D/1W timeframes:

top_gainers(exchange="NSE", timeframe="1D")
coin_analysis(symbol="RELIANCE", exchange="NSE", timeframe="1D")
multi_agent_analysis(symbol="INFY", exchange="NSE", timeframe="1D")

Yahoo / backtest tools — use the .NS (NSE) or .BO (BSE) suffix:

backtest_strategy("TCS.NS", "rsi", "1y")
compare_strategies("INFY.NS")

Indian news:

india_news(limit=10)                 # all India market headlines
india_news(symbol="RELIANCE")        # only headlines mentioning RELIANCE
financial_news(category="india")     # same feeds via the generic tool

Stock suggestion engine (NSE/BSE)

AI-assisted LONG trade ideas — each with entry (CMP), stop-loss, two targets, risk/reward, a 0–100 conviction score, and a plain-English rationale:

india_swing_picks(exchange="NSE", top_n=5)                 # 2-7 day swing ideas (daily TF)
india_swing_picks(direction="short")                       # bearish setups
india_swing_picks(index_filter="NIFTY50")                  # restrict to an index universe
india_intraday_signals(exchange="NSE", top_n=5)            # same-session ideas (15m, VWAP-aware)
india_trade_plan("AXISBANK", mode="swing", direction="auto")  # full plan for one stock
india_backtest("TCS", period="2y")                         # validate vs history (6-strategy leaderboard)
india_swing_picks(capital=200000, risk_pct=1.5)            # add position sizing (qty + ₹ P&L)

• Position sizing: pass capital (INR) and risk_pct to any idea tool. Each idea then carries position_sizing: share quantity sized so a stop-out loses ~`risk_pctof capital (capped by capital), plusposition_value, rupee loss_at_stop, and profit_at_t1/t2`. Short sizing is notional (real shorting needs margin).

• direction: "auto" (long uptrends / short downtrends), "long", or "short". Short setups use a dedicated bearish momentum/quality scorer (the shared engine is long-biased) and inverted levels (stop above entry, targets below).

• index_filter: "NIFTY50", "NIFTYBANK", "NIFTYNEXT50" — constituents bundled in core/data/india_indices.py (refresh on NSE rebalance).

• india_backtest: maps the symbol to Yahoo (.NS/.BO) and runs all 6 strategies as a robustness check on whether the name respects technical setups.

How it works: scans the most-liquid NSE/BSE stocks, scores momentum (compute_stock_score) and setup tradability (compute_trade_quality), builds levels via compute_trade_setup, then layers on a conviction blend, a directional gate (long-only; downtrends filtered out), and a generated rationale. ATR and average volume are backfilled from tradingview_screener because tradingview_ta omits them — without this the trade-setup engine produced no levels at all (this gap also silently affected the upstream EGX setup path). Stops are floored (≥0.5% swing / ≥0.6% intraday) so signals aren't stopped out by noise. Educational analysis only — not investment advice.

The egx_* tools are Egypt-specific and do not apply to India.

Refreshing the symbol lists

python scripts/refresh_india_symbols.py    # NSE/BSE liquid universe (coinlist/*.txt)
python scripts/refresh_india_indices.py    # Nifty 50 / Bank Nifty / Next 50 constituents (official NSE CSVs)

refresh_india_indices.py writes core/data/india_indices.json, which overrides the bundled constituent lists. Run it after an NSE index rebalance.

Run the MCP server

tradingview-mcp            # stdio transport (default)
tradingview-mcp streamable-http --host 127.0.0.1 --port 8000