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)