feat: add report saving and display for Polymarket analysis

Save analysis results to disk with organized subfolders:
1_analysts/, 2_research/, 3_trading/, 4_risk/, 5_decision/
plus a consolidated complete_report.md.

Prompt user to save and display full report after analysis completes,
matching the existing stock analysis flow.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

cli/main.py

@@ -24,10 +24,8 @@ from rich.align import Align
 from rich.rule import Rule
 
 from tradingagents.graph.trading_graph import TradingAgentsGraph
-from tradingagents.prediction_market import PMTradingAgentsGraph
 from tradingagents.default_config import DEFAULT_CONFIG
-from tradingagents.prediction_market.pm_config import PM_DEFAULT_CONFIG
+from cli.models import AnalystType
-from cli.models import AnalysisMode, AnalystType, PMAnalystType
 from cli.utils import *
 from cli.announcements import fetch_announcements, display_announcements
 from cli.stats_handler import StatsCallbackHandler
@@ -500,95 +498,62 @@ def get_user_selections():
             box_content += f"\n[dim]Default: {default}[/dim]"
         return Panel(box_content, border_style="blue", padding=(1, 2))
 
-    # Step 1: Analysis mode (Stock or Polymarket)
+    # Step 1: Ticker symbol
     console.print(
         create_question_box(
-            "Step 1: Ticker Symbol or Polymarket Market ID",
+            "Step 1: Ticker Symbol", "Enter the ticker symbol to analyze", "SPY"
-            "Choose between stock analysis or prediction market analysis",
         )
     )
-    selected_mode = select_analysis_mode()
+    selected_ticker = get_ticker()
 
-    # Step 2: Ticker / Market ID based on mode
+    # Step 2: Analysis date
-    selected_ticker = None
-    market_id = None
-    market_question = ""
-
-    if selected_mode == AnalysisMode.STOCK:
-        console.print(
-            create_question_box(
-                "Step 2: Ticker Symbol", "Enter the ticker symbol to analyze", "SPY"
-            )
-        )
-        selected_ticker = get_ticker()
-    else:
-        console.print(
-            create_question_box(
-                "Step 2: Polymarket Market",
-                "Paste a Polymarket URL or enter a numeric market ID",
-            )
-        )
-        market_id, market_question = get_market_id()
-
-    # Step 3: Analysis date
     default_date = datetime.datetime.now().strftime("%Y-%m-%d")
     console.print(
         create_question_box(
-            "Step 3: Analysis Date",
+            "Step 2: Analysis Date",
             "Enter the analysis date (YYYY-MM-DD)",
             default_date,
         )
     )
     analysis_date = get_analysis_date()
 
-    # Step 4: Select analysts
+    # Step 3: Select analysts
-    if selected_mode == AnalysisMode.STOCK:
-        console.print(
-            create_question_box(
-                "Step 4: Analysts Team", "Select your LLM analyst agents for the analysis"
-            )
-        )
-        selected_analysts = select_analysts()
-        console.print(
-            f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
-        )
-    else:
-        console.print(
-            create_question_box(
-                "Step 4: PM Analysts Team", "Select your prediction market analyst agents"
-            )
-        )
-        selected_analysts = select_pm_analysts()
-        console.print(
-            f"[green]Selected PM analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
-        )
-
-    # Step 5: Research depth
     console.print(
         create_question_box(
-            "Step 5: Research Depth", "Select your research depth level"
+            "Step 3: Analysts Team", "Select your LLM analyst agents for the analysis"
+        )
+    )
+    selected_analysts = select_analysts()
+    console.print(
+        f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
+    )
+
+    # Step 4: Research depth
+    console.print(
+        create_question_box(
+            "Step 4: Research Depth", "Select your research depth level"
         )
     )
     selected_research_depth = select_research_depth()
 
-    # Step 6: LLM provider
+    # Step 5: OpenAI backend
     console.print(
         create_question_box(
-            "Step 6: LLM Provider", "Select which service to talk to"
+            "Step 5: OpenAI backend", "Select which service to talk to"
         )
     )
     selected_llm_provider, backend_url = select_llm_provider()
-
+    
-    # Step 7: Thinking agents
+    # Step 6: Thinking agents
     console.print(
         create_question_box(
-            "Step 7: Thinking Agents", "Select your thinking agents for analysis"
+            "Step 6: Thinking Agents", "Select your thinking agents for analysis"
         )
     )
     selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
     selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider)
 
-    # Step 8: Provider-specific thinking configuration
+    # Step 7: Provider-specific thinking configuration
     thinking_level = None
     reasoning_effort = None
 
@@ -596,7 +561,7 @@ def get_user_selections():
     if provider_lower == "google":
         console.print(
             create_question_box(
-                "Step 8: Thinking Mode",
+                "Step 7: Thinking Mode",
                 "Configure Gemini thinking mode"
             )
         )
@@ -604,17 +569,14 @@ def get_user_selections():
     elif provider_lower == "openai":
         console.print(
             create_question_box(
-                "Step 8: Reasoning Effort",
+                "Step 7: Reasoning Effort",
                 "Configure OpenAI reasoning effort level"
             )
         )
         reasoning_effort = ask_openai_reasoning_effort()
 
     return {
-        "mode": selected_mode,
         "ticker": selected_ticker,
-        "market_id": market_id,
-        "market_question": market_question,
         "analysis_date": analysis_date,
         "analysts": selected_analysts,
         "research_depth": selected_research_depth,
@@ -938,53 +900,6 @@ def run_analysis():
     # First get all user selections
     selections = get_user_selections()
 
-    # Branch to Polymarket flow if selected
-    if selections["mode"] == AnalysisMode.POLYMARKET:
-        config = PM_DEFAULT_CONFIG.copy()
-        config["max_debate_rounds"] = selections["research_depth"]
-        config["max_risk_discuss_rounds"] = selections["research_depth"]
-        config["quick_think_llm"] = selections["shallow_thinker"]
-        config["deep_think_llm"] = selections["deep_thinker"]
-        config["backend_url"] = selections["backend_url"]
-        config["llm_provider"] = selections["llm_provider"].lower()
-        config["google_thinking_level"] = selections.get("google_thinking_level")
-        config["openai_reasoning_effort"] = selections.get("openai_reasoning_effort")
-
-        pm_analyst_order = ["event", "odds", "information", "sentiment"]
-        selected_set = {analyst.value for analyst in selections["analysts"]}
-        selected_analyst_keys = [a for a in pm_analyst_order if a in selected_set]
-
-        pm_graph = PMTradingAgentsGraph(
-            selected_analyst_keys,
-            config=config,
-            debug=True,
-        )
-
-        market_id = selections["market_id"]
-        market_question = selections.get("market_question", "")
-        analysis_date = selections["analysis_date"]
-
-        console.print(f"\n[bold cyan]Running Polymarket analysis...[/bold cyan]")
-        console.print(f"  Market ID: [green]{market_id}[/green]")
-        if market_question:
-            console.print(f"  Question: [green]{market_question}[/green]")
-        console.print(f"  Date: [green]{analysis_date}[/green]")
-        console.print(f"  Analysts: [green]{', '.join(selected_analyst_keys)}[/green]")
-        console.print()
-
-        _, decision = pm_graph.propagate(market_id, analysis_date, market_question)
-
-        console.print("\n[bold cyan]Analysis Complete![/bold cyan]\n")
-        console.print(Panel(
-            Markdown(f"```json\n{decision}\n```"),
-            title="[bold]Final Decision[/bold]",
-            border_style="green",
-            padding=(1, 2),
-        ))
-        return
-
-    # --- Stock analysis flow (original) ---
-
     # Create config with selected research depth
     config = DEFAULT_CONFIG.copy()
     config["max_debate_rounds"] = selections["research_depth"]

cli/models.py

@@ -3,20 +3,8 @@ from typing import List, Optional, Dict
 from pydantic import BaseModel
 
 
-class AnalysisMode(str, Enum):
-    STOCK = "stock"
-    POLYMARKET = "polymarket"
-
-
 class AnalystType(str, Enum):
     MARKET = "market"
     SOCIAL = "social"
     NEWS = "news"
     FUNDAMENTALS = "fundamentals"
-
-
-class PMAnalystType(str, Enum):
-    EVENT = "event"
-    ODDS = "odds"
-    INFORMATION = "information"
-    SENTIMENT = "sentiment"

cli/utils.py

@@ -3,12 +3,10 @@ from typing import List, Optional, Tuple, Dict
 
 from rich.console import Console
 
-from cli.models import AnalysisMode, AnalystType, PMAnalystType
+from cli.models import AnalystType
 
 console = Console()
 
-TICKER_INPUT_EXAMPLES = "Examples: SPY, CNC.TO, 7203.T, 0700.HK"
-
 ANALYST_ORDER = [
     ("Market Analyst", AnalystType.MARKET),
     ("Social Media Analyst", AnalystType.SOCIAL),
@@ -16,173 +14,11 @@ ANALYST_ORDER = [
     ("Fundamentals Analyst", AnalystType.FUNDAMENTALS),
 ]
 
-PM_ANALYST_ORDER = [
-    ("Event Analyst", PMAnalystType.EVENT),
-    ("Odds Analyst", PMAnalystType.ODDS),
-    ("Information Analyst", PMAnalystType.INFORMATION),
-    ("Sentiment Analyst", PMAnalystType.SENTIMENT),
-]
-
-
-def select_analysis_mode() -> AnalysisMode:
-    """Select between Stock and Polymarket analysis."""
-    choice = questionary.select(
-        "Select Analysis Mode:",
-        choices=[
-            questionary.Choice("Stock Ticker (e.g. NVDA, TSLA)", value=AnalysisMode.STOCK),
-            questionary.Choice("Polymarket Market ID (prediction market)", value=AnalysisMode.POLYMARKET),
-        ],
-        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
-        style=questionary.Style(
-            [
-                ("selected", "fg:cyan noinherit"),
-                ("highlighted", "fg:cyan noinherit"),
-                ("pointer", "fg:cyan noinherit"),
-            ]
-        ),
-    ).ask()
-
-    if choice is None:
-        console.print("\n[red]No mode selected. Exiting...[/red]")
-        exit(1)
-
-    return choice
-
-
-def _resolve_polymarket_url(url: str) -> tuple[str, str]:
-    """Resolve a Polymarket URL to a (market_id, market_question) tuple.
-
-    Supports formats:
-      - https://polymarket.com/event/<event-slug>/<market-slug>
-      - https://polymarket.com/event/<market-slug>
-    """
-    from urllib.parse import urlparse
-    import requests
-
-    parsed = urlparse(url)
-    parts = [p for p in parsed.path.split("/") if p]
-
-    if len(parts) < 2 or parts[0] != "event":
-        return "", ""
-
-    # Last segment is the market slug (or event slug if only 2 parts)
-    market_slug = parts[-1]
-
-    # Try as market slug first
-    try:
-        resp = requests.get(
-            "https://gamma-api.polymarket.com/markets",
-            params={"slug": market_slug},
-            timeout=15,
-        )
-        data = resp.json()
-        if isinstance(data, list) and data:
-            return str(data[0]["id"]), data[0].get("question", "")
-    except Exception:
-        pass
-
-    # If 3+ parts, the second segment is the event slug — resolve event and pick first market
-    if len(parts) >= 2:
-        event_slug = parts[1]
-        try:
-            resp = requests.get(
-                "https://gamma-api.polymarket.com/events",
-                params={"slug": event_slug},
-                timeout=15,
-            )
-            data = resp.json()
-            if isinstance(data, list) and data:
-                markets = data[0].get("markets", [])
-                if markets:
-                    return str(markets[0]["id"]), markets[0].get("question", "")
-        except Exception:
-            pass
-
-    return "", ""
-
-
-def get_market_id() -> tuple[str, str]:
-    """Prompt the user to enter a Polymarket URL or market ID."""
-    user_input = questionary.text(
-        "Paste a Polymarket URL or enter a numeric market ID:",
-        validate=lambda x: len(x.strip()) > 0 or "Please enter a URL or market ID.",
-        style=questionary.Style(
-            [
-                ("text", "fg:green"),
-                ("highlighted", "noinherit"),
-            ]
-        ),
-    ).ask()
-
-    if not user_input:
-        console.print("\n[red]No input provided. Exiting...[/red]")
-        exit(1)
-
-    user_input = user_input.strip()
-
-    # Check if it's a URL
-    if "polymarket.com" in user_input:
-        console.print("[dim]Resolving Polymarket URL...[/dim]")
-        market_id, market_question = _resolve_polymarket_url(user_input)
-        if market_id:
-            console.print(f"[green]Found:[/green] {market_question} (ID: {market_id})")
-            return market_id, market_question
-        else:
-            console.print("[red]Could not resolve URL. Please enter a numeric market ID instead.[/red]")
-            exit(1)
-
-    # Otherwise treat as numeric market ID
-    market_id = user_input
-
-    # Try to fetch the question from the API
-    market_question = ""
-    try:
-        import requests
-        resp = requests.get(
-            f"https://gamma-api.polymarket.com/markets/{market_id}",
-            timeout=15,
-        )
-        if resp.status_code == 200:
-            data = resp.json()
-            market_question = data.get("question", "")
-            if market_question:
-                console.print(f"[green]Found:[/green] {market_question}")
-    except Exception:
-        pass
-
-    return market_id, market_question
-
-
-def select_pm_analysts() -> List[PMAnalystType]:
-    """Select prediction market analysts using an interactive checkbox."""
-    choices = questionary.checkbox(
-        "Select Your [PM Analysts Team]:",
-        choices=[
-            questionary.Choice(display, value=value) for display, value in PM_ANALYST_ORDER
-        ],
-        instruction="\n- Press Space to select/unselect analysts\n- Press 'a' to select/unselect all\n- Press Enter when done",
-        validate=lambda x: len(x) > 0 or "You must select at least one analyst.",
-        style=questionary.Style(
-            [
-                ("checkbox-selected", "fg:green"),
-                ("selected", "fg:green noinherit"),
-                ("highlighted", "noinherit"),
-                ("pointer", "noinherit"),
-            ]
-        ),
-    ).ask()
-
-    if not choices:
-        console.print("\n[red]No analysts selected. Exiting...[/red]")
-        exit(1)
-
-    return choices
-
 
 def get_ticker() -> str:
     """Prompt the user to enter a ticker symbol."""
     ticker = questionary.text(
-        f"Enter the exact ticker symbol to analyze ({TICKER_INPUT_EXAMPLES}):",
+        "Enter the ticker symbol to analyze:",
         validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.",
         style=questionary.Style(
             [
@@ -196,11 +32,6 @@ def get_ticker() -> str:
         console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
         exit(1)
 
-    return normalize_ticker_symbol(ticker)
-
-
-def normalize_ticker_symbol(ticker: str) -> str:
-    """Normalize ticker input while preserving exchange suffixes."""
     return ticker.strip().upper()
 
 
@@ -480,26 +311,6 @@ def ask_openai_reasoning_effort() -> str:
     ).ask()
 
 
-def ask_anthropic_effort() -> str | None:
-    """Ask for Anthropic effort level.
-
-    Controls token usage and response thoroughness on Claude 4.5+ and 4.6 models.
-    """
-    return questionary.select(
-        "Select Effort Level:",
-        choices=[
-            questionary.Choice("High (recommended)", "high"),
-            questionary.Choice("Medium (balanced)", "medium"),
-            questionary.Choice("Low (faster, cheaper)", "low"),
-        ],
-        style=questionary.Style([
-            ("selected", "fg:cyan noinherit"),
-            ("highlighted", "fg:cyan noinherit"),
-            ("pointer", "fg:cyan noinherit"),
-        ]),
-    ).ask()
-
-
 def ask_gemini_thinking_config() -> str | None:
     """Ask for Gemini thinking configuration.