feat(data): add Polymarket prediction markets as a keyless vendor

Surface live, market-implied probabilities for forward-looking events (Fed
decisions, recession, elections, geopolitics, crypto) to the news analyst via a
new get_prediction_markets tool and a prediction_markets vendor category. Backed
by Polymarket's public Gamma API (no key). Results are filtered to open,
forward-looking markets (closed and past-dated events excluded), ranked by
traded volume, and rendered with implied probability, volume, resolution date,
and the recent move. External errors degrade to a clear unavailable message
rather than interrupting the analyst.

tradingagents/agents/analysts/news_analyst.py

@@ -5,6 +5,7 @@ from tradingagents.agents.utils.agent_utils import (
     get_language_instruction,
     get_macro_indicators,
     get_news,
+    get_prediction_markets,
 )
 from tradingagents.dataflows.config import get_config
 
@@ -20,10 +21,11 @@ def create_news_analyst(llm):
             get_news,
             get_global_news,
             get_macro_indicators,
+            get_prediction_markets,
         ]
 
         system_message = (
-            f"You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for {asset_label}-specific or targeted news searches, get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news, and get_macro_indicators(indicator, curr_date, look_back_days) to ground macro commentary in actual data from FRED (e.g. 'cpi', 'core_pce', 'unemployment', 'fed_funds_rate', '10y_treasury', 'yield_curve'). Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
+            f"You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for {asset_label}-specific or targeted news searches, get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news, get_macro_indicators(indicator, curr_date, look_back_days) to ground macro commentary in actual data from FRED (e.g. 'cpi', 'core_pce', 'unemployment', 'fed_funds_rate', '10y_treasury', 'yield_curve'), and get_prediction_markets(topic, limit) for live market-implied probabilities of forward-looking events (e.g. 'Fed rate cut', 'recession 2026', geopolitical or sector events). Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
             + """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
             + get_language_instruction()
         )

tradingagents/agents/utils/agent_utils.py

@@ -26,6 +26,9 @@ from tradingagents.agents.utils.news_data_tools import (
 from tradingagents.agents.utils.macro_data_tools import (
     get_macro_indicators
 )
+from tradingagents.agents.utils.prediction_markets_tools import (
+    get_prediction_markets
+)
 from tradingagents.agents.utils.market_data_validation_tools import (
     get_verified_market_snapshot
 )

tradingagents/agents/utils/prediction_markets_tools.py

@@ -0,0 +1,31 @@
+from typing import Annotated
+
+from langchain_core.tools import tool
+
+from tradingagents.dataflows.interface import route_to_vendor
+
+
+@tool
+def get_prediction_markets(
+    topic: Annotated[
+        str,
+        "Event topic/keyword, e.g. 'Fed rate cut', 'recession 2026', "
+        "'US election', or a sector/company event.",
+    ],
+    limit: Annotated[int | None, "Max markets to return; omit for a default of 6"] = None,
+) -> str:
+    """
+    Retrieve live, market-implied probabilities for forward-looking events from
+    prediction markets (Polymarket): Fed decisions, recession, elections,
+    geopolitics, crypto. Returns the most-traded open markets matching the
+    topic, each with its implied probability, traded volume, resolution date,
+    and recent move. Uses the configured prediction_markets vendor.
+
+    Args:
+        topic (str): Event keyword(s) to search
+        limit (int): Max markets to return; omit for a default of 6
+
+    Returns:
+        str: A formatted markdown report of matching prediction markets
+    """
+    return route_to_vendor("get_prediction_markets", topic, limit)

tradingagents/dataflows/interface.py

@@ -25,6 +25,7 @@ from .alpha_vantage import (
 )
 from .alpha_vantage_common import AlphaVantageRateLimitError
 from .fred import get_macro_data as get_fred_macro_data
+from .polymarket import get_prediction_markets as get_polymarket_prediction_markets
 from .symbol_utils import NoMarketDataError
 
 # Configuration and routing logic
@@ -68,12 +69,19 @@ TOOLS_CATEGORIES = {
         "tools": [
             "get_macro_indicators",
         ]
+    },
+    "prediction_markets": {
+        "description": "Market-implied probabilities for forward-looking events",
+        "tools": [
+            "get_prediction_markets",
+        ]
     }
 }
 
 VENDOR_LIST = [
     "yfinance",
     "fred",
+    "polymarket",
     "alpha_vantage",
 ]
 
@@ -123,6 +131,10 @@ VENDOR_METHODS = {
     "get_macro_indicators": {
         "fred": get_fred_macro_data,
     },
+    # prediction_markets
+    "get_prediction_markets": {
+        "polymarket": get_polymarket_prediction_markets,
+    },
 }
 
 def get_category_for_method(method: str) -> str:

tradingagents/dataflows/polymarket.py

@@ -0,0 +1,139 @@
+"""Polymarket prediction-market vendor.
+
+Surfaces live, market-implied probabilities for forward-looking events (Fed
+decisions, recession, elections, geopolitics, crypto) to the news analyst, as a
+complement to news (what happened) and FRED macro data (where things stand):
+what the crowd actually prices to happen next.
+
+Uses Polymarket's public Gamma API (https://gamma-api.polymarket.com) — no key,
+no auth. Each market's ``outcomePrices`` are the implied probabilities of its
+outcomes (a "Yes" at 0.76 means the market prices a 76% chance).
+"""
+import json
+import logging
+from datetime import datetime, timezone
+
+import requests
+
+logger = logging.getLogger(__name__)
+
+GAMMA_BASE = "https://gamma-api.polymarket.com"
+
+# Network timeout (seconds), consistent with the other vendors.
+REQUEST_TIMEOUT = 30
+
+# Default number of markets to return, ranked by traded volume.
+DEFAULT_LIMIT = 6
+
+
+def _request(path: str, params: dict) -> dict:
+    response = requests.get(
+        f"{GAMMA_BASE}/{path}", params=params, timeout=REQUEST_TIMEOUT
+    )
+    response.raise_for_status()
+    return response.json()
+
+
+def _parse_json_list(value) -> list:
+    """Gamma encodes ``outcomes``/``outcomePrices`` as JSON-string arrays."""
+    if isinstance(value, list):
+        return value
+    try:
+        return json.loads(value)
+    except (json.JSONDecodeError, TypeError):
+        return []
+
+
+def _is_forward_looking(market: dict, now: datetime) -> bool:
+    """Keep only open markets that resolve in the future.
+
+    ``closed`` is the reliable resolved flag (``active`` stays True even for
+    settled markets), and a past ``endDate`` means the event already resolved —
+    either way it is not a forward-looking signal.
+    """
+    if market.get("closed"):
+        return False
+    end_date = market.get("endDate")
+    if end_date:
+        try:
+            if datetime.fromisoformat(end_date.replace("Z", "+00:00")) < now:
+                return False
+        except ValueError:
+            pass
+    return bool(_parse_json_list(market.get("outcomePrices"))) and bool(
+        _parse_json_list(market.get("outcomes"))
+    )
+
+
+def get_prediction_markets(topic: str, limit: int | None = None) -> str:
+    """Return live prediction-market probabilities for an event topic.
+
+    Args:
+        topic: Event keyword(s), e.g. "Fed rate cut", "recession 2026",
+            "US election", or a sector/company event.
+        limit: Max markets to return (ranked by traded volume); ``None`` uses
+            DEFAULT_LIMIT.
+
+    Returns:
+        A markdown report of the most-traded open markets matching the topic,
+        each with its implied probability, traded volume, resolution date, and
+        recent (1-week) move.
+    """
+    if limit is None:
+        limit = DEFAULT_LIMIT
+
+    try:
+        data = _request("public-search", {"q": topic, "limit_per_type": 20})
+    except requests.RequestException as e:
+        logger.warning("Polymarket search failed for %r: %s", topic, e)
+        return (
+            f"Polymarket data is currently unavailable (network error: {e}). "
+            f"Proceed without prediction-market signal for '{topic}'."
+        )
+
+    now = datetime.now(timezone.utc)
+    candidates = [
+        m
+        for event in data.get("events", [])
+        for m in event.get("markets", [])
+        if _is_forward_looking(m, now)
+    ]
+    candidates.sort(key=lambda m: m.get("volumeNum") or 0, reverse=True)
+
+    header = (
+        f'## Polymarket prediction markets: "{topic}"\n'
+        f"Live, market-implied probabilities (higher traded volume = deeper, "
+        f"more reliable). A probability is the crowd's priced odds of the event, "
+        f"not a forecast you should take as certain.\n\n"
+    )
+
+    if not candidates:
+        return header + (
+            f"No open prediction markets matched '{topic}'. Polymarket coverage "
+            f"is concentrated in macro, political, geopolitical, and crypto "
+            f"events; a specific equity may have none."
+        )
+
+    lines = []
+    for m in candidates[:limit]:
+        prices = _parse_json_list(m.get("outcomePrices"))
+        outcomes = _parse_json_list(m.get("outcomes"))
+        try:
+            prob = float(prices[0])
+        except (ValueError, IndexError):
+            continue
+        label = outcomes[0] if outcomes else "Yes"
+        volume = m.get("volumeNum") or 0
+        end_date = (m.get("endDate") or "")[:10]
+        wk = m.get("oneWeekPriceChange")
+        wk_str = (
+            f", 1-week {wk * 100:+.1f}pp"
+            if isinstance(wk, (int, float)) and wk
+            else ""
+        )
+        lines.append(
+            f"- **{m.get('question')}** — {label} {prob:.0%} "
+            f"(${volume:,.0f} volume, resolves {end_date}{wk_str})"
+        )
+
+    return header + "\n".join(lines) + "\n"

tradingagents/default_config.py

@@ -107,6 +107,7 @@ DEFAULT_CONFIG = _apply_env_overrides({
         "fundamental_data": "yfinance",      # Options: alpha_vantage, yfinance
         "news_data": "yfinance",             # Options: alpha_vantage, yfinance
         "macro_data": "fred",                # Options: fred (needs FRED_API_KEY)
+        "prediction_markets": "polymarket",  # Options: polymarket (keyless)
     },
     # Tool-level configuration (takes precedence over category-level)
     "tool_vendors": {

tradingagents/graph/trading_graph.py

@@ -40,7 +40,8 @@ from tradingagents.agents.utils.agent_utils import (
     get_news,
     get_insider_transactions,
     get_global_news,
-    get_macro_indicators
+    get_macro_indicators,
+    get_prediction_markets
 )
 
 from .checkpointer import checkpoint_step, clear_checkpoint, get_checkpointer, thread_id
@@ -194,6 +195,7 @@ class TradingAgentsGraph:
                     get_global_news,
                     get_insider_transactions,
                     get_macro_indicators,
+                    get_prediction_markets,
                 ]
             ),
             "fundamentals": ToolNode(