Initial commit: MCP server wrapping tradingview-screener as MCP tools
- 11 MCP tools: get_stock_quotes, screen_market, find_top_gainers, find_top_losers, find_most_active, technical_scan, fundamental_scan, list_markets, list_fields, set_session, clear_session - Structured filter conditions with nested And/Or groups - Session cookie resolution: per-call > in-memory > env var - auto-load .env via python-dotenv - 69 unit tests covering helpers, conditions, filters, and all tools
This commit is contained in:
achmad
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from __future__ import annotations
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import os
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from typing import Any, cast
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from dotenv import load_dotenv
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load_dotenv()
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from mcp.server.fastmcp import FastMCP
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from tradingview_screener.column import col
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from tradingview_screener.query import And, Or, Query
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from tradingview_screener.screeners import (
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bond,
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cfd,
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coin,
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crypto,
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crypto_dex,
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forex,
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futures,
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options,
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stocks,
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)
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mcp = FastMCP("tradingview-screener")
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_session_cookies: dict[str, str] | None = None
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def _resolve_cookies(sessionid: str | None = None) -> dict[str, str] | None:
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if sessionid:
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return {"sessionid": sessionid}
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if _session_cookies is not None:
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return _session_cookies
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env = os.environ.get("TV_SESSION_ID")
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if env:
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return {"sessionid": env}
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return None
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def _exec_query(
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q: Query,
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sessionid: str | None = None,
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) -> tuple[int, list[dict[str, Any]]]:
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cookies = _resolve_cookies(sessionid)
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total, df = q.get_scanner_data(cookies=cookies)
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return total, cast("list[dict[str, Any]]", df.to_dict(orient="records"))
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def _build_condition(f: dict[str, Any]) -> Any:
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if "field" in f:
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c = col(f["field"])
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op = f["operator"]
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if op in ("empty", "not_empty"):
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return c.empty() if op == "empty" else c.not_empty()
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val = f["value"]
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match op:
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case ">" | "greater":
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return c > val
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case ">=" | "egreater":
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return c >= val
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case "<" | "less":
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return c < val
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case "<=" | "eless":
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return c <= val
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case "==" | "equal":
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return c == val
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case "!=" | "nequal":
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return c != val
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case "between":
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return c.between(val[0], val[1])
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case "not_between":
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return c.not_between(val[0], val[1])
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case "isin":
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return c.isin(val)
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case "not_in":
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return c.not_in(val)
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case "has":
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return c.has(val)
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case "has_none_of":
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return c.has_none_of(val)
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case "like":
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return c.like(val)
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case "not_like":
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return c.not_like(val)
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case "crosses":
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return c.crosses(val)
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case "crosses_above":
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return c.crosses_above(val)
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case "crosses_below":
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return c.crosses_below(val)
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case "above_pct":
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return c.above_pct(val[0], val[1])
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case "below_pct":
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return c.below_pct(val[0], val[1])
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case "between_pct":
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return c.between_pct(val[0], val[1], val[2])
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case "in_day_range":
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return c.in_day_range(val[0], val[1])
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case "in_week_range":
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return c.in_week_range(val[0], val[1])
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case "in_month_range":
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return c.in_month_range(val[0], val[1])
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if "operator" in f and "filters" in f:
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sub = [_build_condition(sf) for sf in f["filters"]]
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if f["operator"] == "or":
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return Or(*sub)
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return And(*sub)
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msg = f"Invalid filter: {f}"
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raise ValueError(msg)
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def _apply_filters(q: Query, filters: list[dict[str, Any]] | None) -> Query:
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if not filters:
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return q
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simple: list[Any] = []
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nested: list[Any] = []
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for f in filters:
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if "operator" in f and "filters" in f:
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nested.append(_build_condition(f))
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else:
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simple.append(_build_condition(f))
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if simple:
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q = q.where(*simple)
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if nested:
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default_filter2 = q.query.get("filter2")
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user_op = nested[0] if len(nested) == 1 else And(*nested)
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user_filter2 = user_op["operation"]
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if default_filter2:
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q.query["filter2"] = {
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"operator": "and",
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"operands": [{"operation": user_filter2}, {"operation": default_filter2}],
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}
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else:
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q.query["filter2"] = user_filter2
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return q
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MARKET_FACTORIES: dict[str, Any] = {
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"stocks": stocks,
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"crypto": crypto,
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"crypto_dex": crypto_dex,
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"coin": coin,
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"forex": forex,
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"futures": futures,
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"bond": bond,
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"cfd": cfd,
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"options": options,
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}
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STOCK_COUNTRY_MARKETS: list[str] = [
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"america", "argentina", "australia", "austria", "bangladesh", "belgium",
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"brazil", "bulgaria", "canada", "chile", "china", "colombia", "croatia",
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"cyprus", "czech", "denmark", "egypt", "estonia", "finland", "france",
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"germany", "ghana", "greece", "hongkong", "hungary", "iceland", "india",
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"indonesia", "ireland", "israel", "italy", "japan", "jordan", "kazakhstan",
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"kenya", "kuwait", "latvia", "lithuania", "luxembourg", "malaysia",
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"malta", "mexico", "morocco", "netherlands", "newzealand", "nigeria",
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"norway", "oman", "pakistan", "peru", "philippines", "poland", "portugal",
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"qatar", "romania", "saudiarabia", "serbia", "singapore", "slovakia",
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"slovenia", "southafrica", "southkorea", "spain", "srilanka", "sweden",
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"switzerland", "taiwan", "thailand", "tunisia", "turkey", "uganda",
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"ukraine", "unitedarabemirates", "unitedkingdom", "vietnam",
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]
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FIELD_CATEGORIES: dict[str, list[dict[str, str]]] = {
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"price": [
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{"name": "open", "description": "Open price"},
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{"name": "high", "description": "High price"},
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{"name": "low", "description": "Low price"},
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{"name": "close", "description": "Close price"},
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{"name": "volume", "description": "Trading volume"},
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{"name": "change", "description": "Percent change"},
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{"name": "change_abs", "description": "Absolute change"},
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{"name": "premarket_change", "description": "Premarket percent change"},
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{"name": "postmarket_change", "description": "Postmarket percent change"},
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{"name": "VWAP", "description": "Volume Weighted Average Price"},
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{"name": "52 Week High", "description": "52-week high price"},
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{"name": "52 Week Low", "description": "52-week low price"},
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],
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"technical": [
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{"name": "RSI", "description": "Relative Strength Index (14)"},
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{"name": "RSI.5", "description": "RSI (5)"},
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{"name": "Stoch.K", "description": "Stochastic %K"},
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{"name": "Stoch.D", "description": "Stochastic %D"},
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{"name": "MACD.macd", "description": "MACD line"},
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{"name": "MACD.signal", "description": "MACD signal line"},
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{"name": "MACD.histogram", "description": "MACD histogram"},
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{"name": "BB.upper", "description": "Bollinger Band upper"},
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{"name": "BB.middle", "description": "Bollinger Band middle (SMA 20)"},
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{"name": "BB.lower", "description": "Bollinger Band lower"},
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{"name": "SMA", "description": "Simple Moving Average"},
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{"name": "EMA", "description": "Exponential Moving Average"},
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{"name": "SMA20", "description": "SMA 20"},
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{"name": "SMA50", "description": "SMA 50"},
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{"name": "SMA200", "description": "SMA 200"},
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{"name": "EMA5", "description": "EMA 5"},
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{"name": "EMA20", "description": "EMA 20"},
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{"name": "EMA50", "description": "EMA 50"},
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{"name": "EMA200", "description": "EMA 200"},
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{"name": "ADX", "description": "Average Directional Index (14)"},
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{"name": "ATR", "description": "Average True Range"},
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{"name": "AO", "description": "Awesome Oscillator"},
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{"name": "OBV", "description": "On Balance Volume"},
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{"name": "CCI20", "description": "Commodity Channel Index (20)"},
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{"name": "ROC", "description": "Rate of Change"},
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{"name": "Williams %R", "description": "Williams Percent Range (14)"},
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{"name": "relative_volume_10d_calc", "description": "Relative volume vs 10-day average"},
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{"name": "volume_ma_10", "description": "Volume moving average (10)"},
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{"name": "TechRating_1D", "description": "Technical rating (1 day)"},
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],
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"fundamental": [
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{"name": "market_cap_basic", "description": "Market cap"},
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{"name": "price_earnings_ttm", "description": "Price/Earnings (TTM)"},
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{"name": "earnings_per_share_diluted_ttm", "description": "EPS diluted (TTM)"},
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{"name": "earnings_per_share_diluted_yoy_growth_ttm", "description": "EPS YoY growth (TTM)"},
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{"name": "dividends_yield_current", "description": "Dividend yield"},
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{"name": "dividends_payout_ratio", "description": "Dividend payout ratio"},
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{"name": "price_to_book_fq", "description": "Price/Book"},
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{"name": "price_to_sales", "description": "Price/Sales"},
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{"name": "price_to_cash_flow", "description": "Price/Cash Flow"},
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{"name": "return_on_equity", "description": "Return on Equity"},
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{"name": "return_on_assets", "description": "Return on Assets"},
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{"name": "operating_margin", "description": "Operating margin"},
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{"name": "profit_margin", "description": "Profit margin"},
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{"name": "revenue_growth", "description": "Revenue growth"},
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{"name": "earnings_growth", "description": "Earnings growth"},
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{"name": "debt_to_equity", "description": "Debt/Equity"},
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{"name": "current_ratio_fq", "description": "Current ratio"},
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{"name": "beta_1_year", "description": "Beta (1 year)"},
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{"name": "float", "description": "Shares float"},
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{"name": "shares_outstanding", "description": "Shares outstanding"},
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{"name": "short_ratio_fq", "description": "Short ratio"},
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{"name": "short_float", "description": "Short % of float"},
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{"name": "insider_ownership", "description": "Insider ownership"},
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{"name": "institutional_ownership", "description": "Institutional ownership"},
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{"name": "price_earnings_ttm", "description": "P/E ratio (TTM)"},
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{"name": "AnalystRating", "description": "Analyst rating (numeric)"},
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],
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"general": [
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{"name": "name", "description": "Short name / ticker"},
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{"name": "description", "description": "Company description"},
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{"name": "ticker", "description": "Full ticker (EXCHANGE:SYMBOL)"},
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{"name": "exchange", "description": "Exchange name"},
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{"name": "market", "description": "Market/country"},
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{"name": "sector", "description": "Sector"},
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{"name": "industry", "description": "Industry"},
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{"name": "country", "description": "Country"},
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{"name": "currency", "description": "Quote currency"},
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{"name": "type", "description": "Instrument type"},
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{"name": "typespecs", "description": "Instrument sub-type"},
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{"name": "is_primary", "description": "Primary listing flag"},
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],
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}
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def _get_market_factory(market_type: str, country_or_param: str | None = None) -> Query:
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if market_type == "stocks" or market_type not in MARKET_FACTORIES:
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fact = stocks
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return fact(country_or_param or "america")
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fact = MARKET_FACTORIES[market_type]
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if market_type == "options":
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return fact(country_or_param or "CME_MINI:ESM2026")
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return fact()
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# ---------------------------------------------------------------------------
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# Tools
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# ---------------------------------------------------------------------------
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@mcp.tool()
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def get_stock_quotes(
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tickers: list[str],
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columns: list[str] | None = None,
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market_type: str = "stocks",
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market_country: str | None = None,
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sessionid: str | None = None,
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) -> str:
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"""Get quote data for specific ticker symbols across any market.
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Examples:
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- get_stock_quotes(tickers=["NASDAQ:NVDA", "NASDAQ:AAPL"])
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- get_stock_quotes(tickers=["BINANCE:BTCUSDT"], market_type="crypto")
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- get_stock_quotes(tickers=["NYSE:SPY"], columns=["name","close","volume","RSI"])
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"""
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q = _get_market_factory(market_type, market_country)
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if columns:
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q = q.select(*columns)
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q = q.set_tickers(*tickers)
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total, data = _exec_query(q, sessionid)
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return f"Found {total} result(s)\n{data}"
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@mcp.tool()
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def screen_market(
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columns: list[str] | None = None,
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filters: list[dict[str, Any]] | None = None,
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order_by: str | None = None,
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ascending: bool = False,
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limit: int = 50,
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offset: int = 0,
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market_type: str = "stocks",
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market_country: str | None = None,
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sessionid: str | None = None,
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) -> str:
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"""General-purpose screener across any market type.
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Parameters:
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columns: Field names to return (e.g. ["name","close","RSI","volume"]).
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filters: Structured filter conditions.
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Simple condition: {"field": "<name>", "operator": "<op>", "value": <value>}
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Nested group: {"operator": "and"|"or", "filters": [<conditions>]}
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Operators: >, >=, <, <=, ==, !=, between, not_between, isin, not_in,
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has, has_none_of, like, not_like, empty, not_empty,
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crosses, crosses_above, crosses_below,
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above_pct, below_pct, between_pct,
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in_day_range, in_week_range, in_month_range
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Examples:
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[{"field": "RSI", "operator": ">", "value": 70}]
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[{"field": "close", "operator": "between", "value": [100, 200]}]
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[{"field": "exchange", "operator": "isin", "value": ["NASDAQ","NYSE"]}]
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[{"field": "MACD.macd", "operator": "crosses_above", "value": "MACD.signal"}]
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Nested: [{"operator": "or", "filters": [
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{"field": "RSI", "operator": ">", "value": 70},
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{"field": "RSI", "operator": "<", "value": 30}
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]}]
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order_by: Field to sort by (e.g. "volume", "market_cap_basic").
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ascending: Sort ascending (default False = descending).
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limit: Max rows to return (1-1000, default 50).
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offset: Row offset for pagination.
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market_type: Market type: stocks, crypto, forex, futures, bond, cfd, coin, crypto_dex, options.
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market_country: For stocks, a country name (e.g. "america", "india", "germany").
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For options, the underlying symbol (e.g. "CME_MINI:ESM2026").
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sessionid: Optional TradingView session ID for real-time data.
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"""
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q = _get_market_factory(market_type, market_country)
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if columns:
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q = q.select(*columns)
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q = _apply_filters(q, filters)
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if order_by:
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q = q.order_by(order_by, ascending=ascending)
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q = q.limit(limit).offset(offset)
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total, data = _exec_query(q, sessionid)
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return f"Total: {total}\n{data}"
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@mcp.tool()
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def find_top_gainers(
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limit: int = 20,
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min_price: float | None = None,
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market_type: str = "stocks",
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market_country: str | None = None,
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sessionid: str | None = None,
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) -> str:
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"""Find top gainers by percent change in any market."""
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q = _get_market_factory(market_type, market_country)
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q = q.select("name", "close", "change", "change_abs", "volume")
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filters = []
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if min_price is not None:
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filters.append({"field": "close", "operator": ">", "value": min_price})
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q = _apply_filters(q, filters)
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q = q.order_by("change", ascending=False).limit(limit)
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total, data = _exec_query(q, sessionid)
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return f"Total: {total}\n{data}"
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@mcp.tool()
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def find_top_losers(
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limit: int = 20,
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min_price: float | None = None,
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market_type: str = "stocks",
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market_country: str | None = None,
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sessionid: str | None = None,
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) -> str:
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"""Find top losers by percent change in any market."""
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q = _get_market_factory(market_type, market_country)
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q = q.select("name", "close", "change", "change_abs", "volume")
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filters = []
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if min_price is not None:
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filters.append({"field": "close", "operator": ">", "value": min_price})
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q = _apply_filters(q, filters)
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q = q.order_by("change", ascending=True).limit(limit)
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total, data = _exec_query(q, sessionid)
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return f"Total: {total}\n{data}"
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@mcp.tool()
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def find_most_active(
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limit: int = 20,
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min_price: float | None = None,
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market_type: str = "stocks",
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market_country: str | None = None,
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sessionid: str | None = None,
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) -> str:
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"""Find most active instruments by volume."""
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q = _get_market_factory(market_type, market_country)
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q = q.select("name", "close", "change", "volume")
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filters = []
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||||
if min_price is not None:
|
||||
filters.append({"field": "close", "operator": ">", "value": min_price})
|
||||
q = _apply_filters(q, filters)
|
||||
q = q.order_by("volume", ascending=False).limit(limit)
|
||||
total, data = _exec_query(q, sessionid)
|
||||
return f"Total: {total}\n{data}"
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
def technical_scan(
|
||||
filters: list[dict[str, Any]],
|
||||
columns: list[str] | None = None,
|
||||
limit: int = 50,
|
||||
market_type: str = "stocks",
|
||||
market_country: str | None = None,
|
||||
sessionid: str | None = None,
|
||||
) -> str:
|
||||
"""Screen using technical indicator conditions.
|
||||
|
||||
Example filters:
|
||||
[{"field": "RSI", "operator": "<", "value": 30}, # oversold
|
||||
{"field": "MACD.macd", "operator": "crosses_above", "value": "MACD.signal"}, # bullish MACD
|
||||
{"field": "close", "operator": "above_pct", "value": ["SMA50", 1.02]}] # 2% above SMA50
|
||||
"""
|
||||
default_cols = ["name", "close", "change", "volume", "RSI", "MACD.macd", "SMA50", "VWAP"]
|
||||
q = _get_market_factory(market_type, market_country)
|
||||
q = q.select(*(columns or default_cols))
|
||||
q = _apply_filters(q, filters)
|
||||
q = q.limit(limit)
|
||||
total, data = _exec_query(q, sessionid)
|
||||
return f"Total: {total}\n{data}"
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
def fundamental_scan(
|
||||
filters: list[dict[str, Any]],
|
||||
columns: list[str] | None = None,
|
||||
limit: int = 50,
|
||||
market_type: str = "stocks",
|
||||
market_country: str | None = None,
|
||||
sessionid: str | None = None,
|
||||
) -> str:
|
||||
"""Screen by fundamental metrics (market cap, P/E, dividend, sector, etc.).
|
||||
|
||||
Example filters:
|
||||
[{"field": "market_cap_basic", "operator": "between", "value": [1e9, 1e11}],
|
||||
{"field": "price_earnings_ttm", "operator": "<", "value": 20},
|
||||
{"field": "dividends_yield_current", "operator": ">", "value": 2},
|
||||
{"field": "sector", "operator": "isin", "value": ["Technology", "Healthcare"]}]
|
||||
"""
|
||||
default_cols = ["name", "close", "market_cap_basic", "price_earnings_ttm",
|
||||
"dividends_yield_current", "sector", "industry"]
|
||||
q = _get_market_factory(market_type, market_country)
|
||||
q = q.select(*(columns or default_cols))
|
||||
q = _apply_filters(q, filters)
|
||||
q = q.limit(limit)
|
||||
total, data = _exec_query(q, sessionid)
|
||||
return f"Total: {total}\n{data}"
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
def list_markets() -> str:
|
||||
"""List all available market types and stock-country markets."""
|
||||
markets = {
|
||||
"asset_types": [
|
||||
{"id": "stocks", "description": "Stocks (common, preferred, DRs, funds) — use market_country param"},
|
||||
{"id": "crypto", "description": "Centralised-exchange crypto pairs"},
|
||||
{"id": "crypto_dex", "description": "Decentralised-exchange crypto pairs (USD)"},
|
||||
{"id": "coin", "description": "CoinMarketCap crypto coins"},
|
||||
{"id": "forex", "description": "Forex currency pairs"},
|
||||
{"id": "futures", "description": "Futures contracts"},
|
||||
{"id": "bond", "description": "Bonds"},
|
||||
{"id": "cfd", "description": "Contracts for Difference"},
|
||||
{"id": "options", "description": "Options (use market_country param for underlying symbol)"},
|
||||
],
|
||||
"stock_countries": STOCK_COUNTRY_MARKETS,
|
||||
}
|
||||
return str(markets)
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
def list_fields(category: str | None = None) -> str:
|
||||
"""List available screener fields, optionally filtered by category.
|
||||
|
||||
Categories: price, technical, fundamental, general
|
||||
If no category given, returns all categories.
|
||||
"""
|
||||
if category:
|
||||
cat = category.lower()
|
||||
if cat in FIELD_CATEGORIES:
|
||||
return str(FIELD_CATEGORIES[cat])
|
||||
return f"Category '{category}' not found. Available: price, technical, fundamental, general"
|
||||
return str(FIELD_CATEGORIES)
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
def set_session(sessionid: str) -> str:
|
||||
"""Store a TradingView session ID for real-time data access.
|
||||
|
||||
Get your sessionid from:
|
||||
1. Go to tradingview.com and log in
|
||||
2. Open DevTools > Application > Cookies > tradingview.com
|
||||
3. Copy the 'sessionid' cookie value
|
||||
"""
|
||||
global _session_cookies
|
||||
_session_cookies = {"sessionid": sessionid}
|
||||
return "Session ID stored successfully"
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
def clear_session() -> str:
|
||||
"""Clear stored TradingView session ID."""
|
||||
global _session_cookies
|
||||
_session_cookies = None
|
||||
return "Session ID cleared"
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
mcp.run(transport="stdio")
|
||||
Reference in New Issue
Block a user