Merge pull request #5 from aguzererler/copilot/check-pr3-implementation

Port missing PR#3 implementations to main: MacroScannerGraph, scanner_setup, exception handling

tests/test_scanner_graph.py

@@ -0,0 +1,41 @@
+"""Tests for the MacroScannerGraph and scanner setup."""
+
+
+def test_scanner_graph_import():
+    """Verify that MacroScannerGraph can be imported."""
+    from tradingagents.graph.scanner_graph import MacroScannerGraph
+
+    assert MacroScannerGraph is not None
+
+
+def test_scanner_graph_instantiates():
+    """Verify that MacroScannerGraph can be instantiated with default config."""
+    from tradingagents.graph.scanner_graph import MacroScannerGraph
+
+    scanner = MacroScannerGraph()
+    assert scanner is not None
+    assert scanner.graph is not None
+
+
+def test_scanner_setup_compiles_graph():
+    """Verify that ScannerGraphSetup produces a compiled graph."""
+    from tradingagents.graph.scanner_setup import ScannerGraphSetup
+
+    setup = ScannerGraphSetup()
+    graph = setup.setup_graph()
+    assert graph is not None
+
+
+def test_scanner_states_import():
+    """Verify that ScannerState can be imported."""
+    from tradingagents.agents.utils.scanner_states import ScannerState
+
+    assert ScannerState is not None
+
+
+if __name__ == "__main__":
+    test_scanner_graph_import()
+    test_scanner_graph_instantiates()
+    test_scanner_setup_compiles_graph()
+    test_scanner_states_import()
+    print("All scanner graph tests passed.")

tradingagents/dataflows/alpha_vantage_scanner.py

@@ -25,7 +25,7 @@ def get_market_movers_alpha_vantage(
             return f"Invalid category '{category}'. Must be one of: day_gainers, day_losers, most_actives"
         
         if category == 'most_actives':
-            return "Error: Alpha Vantage does not support 'most_actives'. Use yfinance (default vendor) for this category."
+            return "Alpha Vantage does not support 'most_actives' category. Please use yfinance instead."
         
         # Make API request for TOP_GAINERS_LOSERS endpoint
         response = _make_api_request("TOP_GAINERS_LOSERS", {})
@@ -38,7 +38,7 @@ def get_market_movers_alpha_vantage(
             return f"Error from Alpha Vantage: {data['Error Message']}"
         
         if "Note" in data:
-            return f"Error: Alpha Vantage API limit reached: {data['Note']}"
+            return f"Alpha Vantage API limit reached: {data['Note']}"
         
         # Map category to Alpha Vantage response key
         if category == 'day_gainers':
@@ -46,7 +46,7 @@ def get_market_movers_alpha_vantage(
         elif category == 'day_losers':
             key = 'top_losers'
         else:
-            return f"Error: unsupported category '{category}'"
+            return f"Unsupported category: {category}"
         
         if key not in data:
             return f"No data found for {category}"
@@ -74,8 +74,8 @@ def get_market_movers_alpha_vantage(
             if isinstance(price, str):
                 try:
                     price = f"${float(price):.2f}"
-                except (ValueError, TypeError):
-                    pass
+                except ValueError:
+                    price = "N/A"
             if isinstance(change_pct, str):
                 change_pct = change_pct.rstrip('%')  # Remove % if present
             if isinstance(change_pct, (int, float)):
@@ -83,8 +83,8 @@ def get_market_movers_alpha_vantage(
             if isinstance(volume, (int, str)):
                 try:
                     volume = f"{int(volume):,}"
-                except (ValueError, TypeError):
-                    pass
+                except ValueError:
+                    volume = "N/A"
             
             result_str += f"| {symbol} | {price} | {change_pct} | {volume} |\n"
         

tradingagents/dataflows/interface.py

@@ -1,3 +1,4 @@
+import logging
 from typing import Annotated
 
 # Import from vendor-specific modules
@@ -191,7 +192,8 @@ def route_to_vendor(method: str, *args, **kwargs):
 
         try:
             return impl_func(*args, **kwargs)
-        except AlphaVantageRateLimitError:
-            continue  # Only rate limits trigger fallback
+        except (AlphaVantageRateLimitError, ConnectionError, TimeoutError) as e:
+            logging.warning(f"Vendor '{vendor}' failed for '{method}': {e}, trying next...")
+            continue
 
     raise RuntimeError(f"No available vendor for '{method}'")

tradingagents/dataflows/yfinance_scanner.py

@@ -18,28 +18,31 @@ def get_market_movers_yfinance(
         Formatted string containing top market movers
     """
     try:
-        # Map category to yfinance screener predefined screener
         screener_keys = {
-            "day_gainers": "DAY_GAINERS",
-            "day_losers": "DAY_LOSERS", 
-            "most_actives": "MOST_ACTIVES"
+            "day_gainers": "day_gainers",
+            "day_losers": "day_losers",
+            "most_actives": "most_actives"
         }
 
         if category not in screener_keys:
             return f"Invalid category '{category}'. Must be one of: {list(screener_keys.keys())}"
 
-        # Use yfinance screener module's screen function
-        data = yf.screener.screen(screener_keys[category], count=25)
+        screener = yf.Screener()
+        data = screener.get_screeners([screener_keys[category]], count=25)
 
-        if not data or not isinstance(data, dict) or 'quotes' not in data:
+        if not data or screener_keys[category] not in data:
             return f"No data found for {category}"
 
-        quotes = data['quotes']
+        movers = data[screener_keys[category]]
+
+        if not movers or 'quotes' not in movers:
+            return f"No movers found for {category}"
+
+        quotes = movers['quotes']
 
         if not quotes:
             return f"No quotes found for {category}"
 
-        # Format the output
         header = f"# Market Movers: {category.replace('_', ' ').title()}\n"
         header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
 
@@ -47,7 +50,7 @@ def get_market_movers_yfinance(
         result_str += "| Symbol | Name | Price | Change % | Volume | Market Cap |\n"
         result_str += "|--------|------|-------|----------|--------|------------|\n"
 
-        for quote in quotes[:15]:  # Top 15
+        for quote in quotes[:15]:
             symbol = quote.get('symbol', 'N/A')
             name = quote.get('shortName', quote.get('longName', 'N/A'))
             price = quote.get('regularMarketPrice', 'N/A')
@@ -55,7 +58,6 @@ def get_market_movers_yfinance(
             volume = quote.get('regularMarketVolume', 'N/A')
             market_cap = quote.get('marketCap', 'N/A')
 
-            # Format numbers
             if isinstance(price, (int, float)):
                 price = f"${price:.2f}"
             if isinstance(change_pct, (int, float)):
@@ -81,7 +83,6 @@ def get_market_indices_yfinance() -> str:
         Formatted string containing index values and daily changes
     """
     try:
-        # Major market indices
         indices = {
             "^GSPC": "S&P 500",
             "^DJI": "Dow Jones",
@@ -90,48 +91,50 @@ def get_market_indices_yfinance() -> str:
             "^RUT": "Russell 2000"
         }
 
-        header = f"# Major Market Indices\n"
+        header = "# Major Market Indices\n"
         header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
 
         result_str = header
         result_str += "| Index | Current Price | Change | Change % | 52W High | 52W Low |\n"
         result_str += "|-------|---------------|--------|----------|----------|----------|\n"
 
-        # Batch-download 1-day history for all symbols in a single request
+        # Batch download historical price data to avoid N+1 calls.
+        # yf.download() always returns multi-level columns when multiple symbols
+        # are requested (group_by="ticker"), so we access hist_batch[symbol].
         symbols = list(indices.keys())
-        indices_history = yf.download(symbols, period="2d", auto_adjust=True, progress=False, threads=True)
+        hist_batch = yf.download(
+            symbols,
+            period="2d",
+            group_by="ticker",
+            progress=False,
+            auto_adjust=True,
+        )
 
         for symbol, name in indices.items():
             try:
                 ticker = yf.Ticker(symbol)
-                # fast_info is a lightweight cached property (no extra HTTP call)
-                fast = ticker.fast_info
+                info = ticker.info
 
-                # Extract history for this symbol from the batch download
+                # Extract per-symbol slice from the batched result.
+                # With multiple symbols and group_by="ticker", the columns are
+                # a MultiIndex keyed by symbol.
                 try:
-                    if len(symbols) > 1:
-                        closes = indices_history["Close"][symbol].dropna()
-                    else:
-                        closes = indices_history["Close"].dropna()
+                    hist = hist_batch[symbol].dropna()
                 except KeyError:
-                    closes = None
+                    hist = ticker.history(period="1d")
 
-                if closes is None or len(closes) == 0:
-                    result_str += f"| {name} | N/A | - | - | - | - |\n"
+                if hist.empty:
+                    result_str += f"| {name} | No data | - | - | - | - |\n"
                     continue
 
-                current_price = closes.iloc[-1]
-                prev_close = closes.iloc[-2] if len(closes) >= 2 else fast.previous_close
-                if prev_close is None or prev_close == 0:
-                    prev_close = current_price
-
+                current_price = hist['Close'].iloc[-1]
+                prev_close = info.get('previousClose', current_price)
                 change = current_price - prev_close
                 change_pct = (change / prev_close * 100) if prev_close else 0
 
-                high_52w = fast.year_high
-                low_52w = fast.year_low
+                high_52w = info.get('fiftyTwoWeekHigh', 'N/A')
+                low_52w = info.get('fiftyTwoWeekLow', 'N/A')
 
-                # Format numbers
                 current_str = f"{current_price:.2f}"
                 change_str = f"{change:+.2f}"
                 change_pct_str = f"{change_pct:+.2f}%"
@@ -141,7 +144,7 @@ def get_market_indices_yfinance() -> str:
                 result_str += f"| {name} | {current_str} | {change_str} | {change_pct_str} | {high_str} | {low_str} |\n"
 
             except Exception as e:
-                result_str += f"| {name} | Error: {str(e)} | - | - | - | - |\n"
+                result_str += f"| {name} | Error: {str(e)[:40]} | - | - | - | - |\n"
 
         return result_str
 
@@ -157,9 +160,6 @@ def get_sector_performance_yfinance() -> str:
         Formatted string containing sector performance data
     """
     try:
-        # All 11 standard GICS (Global Industry Classification Standard) sectors.
-        # These keys are fixed by yfinance's Sector API and cannot be fetched
-        # dynamically; the GICS taxonomy is maintained by MSCI/S&P and is stable.
         sector_keys = [
             "communication-services",
             "consumer-cyclical",
@@ -174,7 +174,7 @@ def get_sector_performance_yfinance() -> str:
             "utilities"
         ]
 
-        header = f"# Sector Performance Overview\n"
+        header = "# Sector Performance Overview\n"
         header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
 
         result_str = header
@@ -186,21 +186,19 @@ def get_sector_performance_yfinance() -> str:
                 sector = yf.Sector(sector_key)
                 overview = sector.overview
 
-                if overview is None or not overview:
+                if overview is None or overview.empty:
                     continue
 
-                # Get performance metrics
                 sector_name = sector_key.replace("-", " ").title()
                 day_return = overview.get('oneDay', {}).get('percentChange', 'N/A')
                 week_return = overview.get('oneWeek', {}).get('percentChange', 'N/A')
                 month_return = overview.get('oneMonth', {}).get('percentChange', 'N/A')
                 ytd_return = overview.get('ytd', {}).get('percentChange', 'N/A')
 
-                # Format percentages
-                day_str = f"{day_return:.2f}%" if isinstance(day_return, (int, float)) else day_return
-                week_str = f"{week_return:.2f}%" if isinstance(week_return, (int, float)) else week_return
-                month_str = f"{month_return:.2f}%" if isinstance(month_return, (int, float)) else month_return
-                ytd_str = f"{ytd_return:.2f}%" if isinstance(ytd_return, (int, float)) else ytd_return
+                day_str = f"{day_return:.2f}%" if isinstance(day_return, (int, float)) else str(day_return)
+                week_str = f"{week_return:.2f}%" if isinstance(week_return, (int, float)) else str(week_return)
+                month_str = f"{month_return:.2f}%" if isinstance(month_return, (int, float)) else str(month_return)
+                ytd_str = f"{ytd_return:.2f}%" if isinstance(ytd_return, (int, float)) else str(ytd_return)
 
                 result_str += f"| {sector_name} | {day_str} | {week_str} | {month_str} | {ytd_str} |\n"
 
@@ -226,7 +224,6 @@ def get_industry_performance_yfinance(
         Formatted string containing industry performance data within the sector
     """
     try:
-        # Normalize sector key to yfinance format
         sector_key = sector_key.lower().replace(" ", "-")
 
         sector = yf.Sector(sector_key)
@@ -242,7 +239,6 @@ def get_industry_performance_yfinance(
         result_str += "| Company | Symbol | Industry | Market Cap | Change % |\n"
         result_str += "|---------|--------|----------|------------|----------|\n"
 
-        # Get top companies in the sector
         for idx, row in top_companies.head(20).iterrows():
             symbol = row.get('symbol', 'N/A')
             name = row.get('name', 'N/A')
@@ -250,7 +246,6 @@ def get_industry_performance_yfinance(
             market_cap = row.get('marketCap', 'N/A')
             change_pct = row.get('regularMarketChangePercent', 'N/A')
 
-            # Format numbers
             if isinstance(market_cap, (int, float)):
                 market_cap = f"${market_cap:,.0f}"
             if isinstance(change_pct, (int, float)):
@@ -297,7 +292,6 @@ def get_topic_news_yfinance(
         result_str = header
 
         for article in search.news[:limit]:
-            # Handle nested content structure
             if "content" in article:
                 content = article["content"]
                 title = content.get("title", "No title")
@@ -305,7 +299,6 @@ def get_topic_news_yfinance(
                 provider = content.get("provider", {})
                 publisher = provider.get("displayName", "Unknown")
 
-                # Get URL
                 url_obj = content.get("canonicalUrl") or content.get("clickThroughUrl") or {}
                 link = url_obj.get("url", "")
             else:
@@ -325,3 +318,4 @@ def get_topic_news_yfinance(
 
     except Exception as e:
         return f"Error fetching news for topic '{topic}': {str(e)}"
+

tradingagents/graph/scanner_graph.py

@@ -0,0 +1,73 @@
+# tradingagents/graph/scanner_graph.py
+
+import datetime
+from typing import Any, Dict, Optional
+
+from tradingagents.dataflows.config import set_config
+from tradingagents.default_config import DEFAULT_CONFIG
+
+from .scanner_setup import ScannerGraphSetup
+
+
+class MacroScannerGraph:
+    """Orchestrates the Global Macro Scanner workflow.
+
+    The scanner runs three parallel data-collection phases followed by a
+    synthesis phase:
+
+    Phase 1 (parallel):
+        - Geopolitical / macro news scanner
+        - Market movers + index performance scanner
+        - Sector performance scanner
+
+    Phase 2 (sequential):
+        - Industry deep dive (technology sector by default)
+
+    Phase 3 (sequential):
+        - Macro synthesis — combines all outputs into a single summary
+    """
+
+    def __init__(self, config: Optional[Dict[str, Any]] = None):
+        """Initialise the scanner graph.
+
+        Args:
+            config: Optional configuration dictionary.  Defaults to
+                ``DEFAULT_CONFIG`` when not provided.
+        """
+        self.config = config or DEFAULT_CONFIG
+        set_config(self.config)
+
+        self.graph_setup = ScannerGraphSetup()
+        self.graph = self.graph_setup.setup_graph()
+
+    def scan(self, scan_date: Optional[str] = None) -> Dict[str, Any]:
+        """Execute the macro scan and return the final state.
+
+        Args:
+            scan_date: Date string in ``YYYY-MM-DD`` format.  Defaults to
+                today's date when not provided.
+
+        Returns:
+            Final LangGraph state dictionary containing all scan reports and
+            the ``macro_scan_summary`` field.
+        """
+        if scan_date is None:
+            scan_date = datetime.date.today().isoformat()
+
+        initial_state = {
+            "messages": [],
+            "scan_date": scan_date,
+            "geopolitical_report": "",
+            "market_movers_report": "",
+            "sector_performance_report": "",
+            "industry_deep_dive_report": "",
+            "macro_scan_summary": "",
+            "sender": "",
+        }
+
+        final_state = self.graph.invoke(
+            initial_state,
+            {"recursion_limit": self.config.get("max_recur_limit", 100)},
+        )
+
+        return final_state

tradingagents/graph/scanner_setup.py

@@ -1,64 +1,78 @@
 # tradingagents/graph/scanner_setup.py
-from typing import Dict, Any
 from langgraph.graph import StateGraph, START, END
-from langgraph.prebuilt import ToolNode
 
-from tradingagents.agents.utils.scanner_tools import (
-    get_market_movers,
-    get_market_indices,
-    get_sector_performance,
-    get_industry_performance,
-    get_topic_news,
-)
-
-from .conditional_logic import ConditionalLogic
+from tradingagents.agents.utils.scanner_states import ScannerState
+from tradingagents.dataflows.interface import route_to_vendor
 
 
-def pass_through_node(state):
-    """Pass-through node that returns state unchanged."""
-    return state
+def geopolitical_scanner_node(state: ScannerState) -> dict:
+    """Phase 1: Fetch geopolitical and macro news."""
+    result = route_to_vendor("get_topic_news", "geopolitics global economy", 10)
+    return {"geopolitical_report": result}
+
+
+def market_movers_scanner_node(state: ScannerState) -> dict:
+    """Phase 1: Fetch market movers and index performance."""
+    movers = route_to_vendor("get_market_movers", "day_gainers")
+    indices = route_to_vendor("get_market_indices")
+    return {"market_movers_report": movers + "\n\n" + indices}
+
+
+def sector_scanner_node(state: ScannerState) -> dict:
+    """Phase 1: Fetch sector performance overview."""
+    result = route_to_vendor("get_sector_performance")
+    return {"sector_performance_report": result}
+
+
+def industry_deep_dive_node(state: ScannerState) -> dict:
+    """Phase 2: Drill down into the technology sector as a representative example."""
+    result = route_to_vendor("get_industry_performance", "technology")
+    return {"industry_deep_dive_report": result}
+
+
+def macro_synthesis_node(state: ScannerState) -> dict:
+    """Phase 3: Combine all scanner outputs into a final summary."""
+    parts = [
+        state.get("geopolitical_report", ""),
+        state.get("market_movers_report", ""),
+        state.get("sector_performance_report", ""),
+        state.get("industry_deep_dive_report", ""),
+    ]
+    summary = "\n\n---\n\n".join(p for p in parts if p)
+    return {"macro_scan_summary": summary}
 
 
 class ScannerGraphSetup:
     """Handles the setup and configuration of the scanner graph."""
 
-    def __init__(self, conditional_logic: ConditionalLogic):
-        self.conditional_logic = conditional_logic
-
     def setup_graph(self):
         """Set up and compile the scanner workflow graph."""
-        workflow = StateGraph(dict)
+        workflow = StateGraph(ScannerState)
 
-        # Add tool nodes
-        tool_nodes = {
-            "get_market_movers": ToolNode([get_market_movers]),
-            "get_market_indices": ToolNode([get_market_indices]),
-            "get_sector_performance": ToolNode([get_sector_performance]),
-            "get_industry_performance": ToolNode([get_industry_performance]),
-            "get_topic_news": ToolNode([get_topic_news]),
-        }
+        # Phase 1: parallel scanners
+        workflow.add_node("geopolitical_scanner", geopolitical_scanner_node)
+        workflow.add_node("market_movers_scanner", market_movers_scanner_node)
+        workflow.add_node("sector_scanner", sector_scanner_node)
 
-        for name, node in tool_nodes.items():
-            workflow.add_node(name, node)
+        # Phase 2: industry deep dive
+        workflow.add_node("industry_deep_dive", industry_deep_dive_node)
 
-        # Add conditional logic node
-        workflow.add_node("conditional_logic", self.conditional_logic)
+        # Phase 3: macro synthesis
+        workflow.add_node("macro_synthesis", macro_synthesis_node)
 
-        # Add pass-through nodes for industry deep dive and macro synthesis
-        workflow.add_node("industry_deep_dive", pass_through_node)
-        workflow.add_node("macro_synthesis", pass_through_node)
+        # Fan-out from START to 3 parallel scanners
+        workflow.add_edge(START, "geopolitical_scanner")
+        workflow.add_edge(START, "market_movers_scanner")
+        workflow.add_edge(START, "sector_scanner")
 
-        # Fan-out from START to 3 scanners
-        workflow.add_edge(START, "get_market_movers")
-        workflow.add_edge(START, "get_sector_performance")
-        workflow.add_edge(START, "get_topic_news")
+        # Fan-in: LangGraph's StateGraph guarantees that industry_deep_dive
+        # only executes after ALL three predecessor nodes have completed and
+        # their state updates have been merged.
+        workflow.add_edge("geopolitical_scanner", "industry_deep_dive")
+        workflow.add_edge("market_movers_scanner", "industry_deep_dive")
+        workflow.add_edge("sector_scanner", "industry_deep_dive")
 
-        # Fan-in to industry deep dive
-        workflow.add_edge("get_market_movers", "industry_deep_dive")
-        workflow.add_edge("get_sector_performance", "industry_deep_dive")
-        workflow.add_edge("get_topic_news", "industry_deep_dive")
-
-        # Then to synthesis
+        # Sequential: deep dive → synthesis → end
         workflow.add_edge("industry_deep_dive", "macro_synthesis")
         workflow.add_edge("macro_synthesis", END)