Merge f4519bcb84 into fa4d01c23a

.env.enterprise.example

@@ -0,0 +1,5 @@
+# Azure OpenAI
+AZURE_OPENAI_API_KEY=
+AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/
+AZURE_OPENAI_DEPLOYMENT_NAME=
+# OPENAI_API_VERSION=2024-10-21  # optional, required for non-v1 API

.env.example

@@ -3,4 +3,7 @@ OPENAI_API_KEY=
 GOOGLE_API_KEY=
 ANTHROPIC_API_KEY=
 XAI_API_KEY=
+DEEPSEEK_API_KEY=
+DASHSCOPE_API_KEY=
+ZHIPU_API_KEY=
 OPENROUTER_API_KEY=

README.md

@@ -140,10 +140,15 @@ export OPENAI_API_KEY=...          # OpenAI (GPT)
 export GOOGLE_API_KEY=...          # Google (Gemini)
 export ANTHROPIC_API_KEY=...       # Anthropic (Claude)
 export XAI_API_KEY=...             # xAI (Grok)
+export DEEPSEEK_API_KEY=...        # DeepSeek
+export DASHSCOPE_API_KEY=...       # Qwen (Alibaba DashScope)
+export ZHIPU_API_KEY=...           # GLM (Zhipu)
 export OPENROUTER_API_KEY=...      # OpenRouter
 export ALPHA_VANTAGE_API_KEY=...   # Alpha Vantage
 ```
 
+For enterprise providers (e.g. Azure OpenAI, AWS Bedrock), copy `.env.enterprise.example` to `.env.enterprise` and fill in your credentials.
+
 For local models, configure Ollama with `llm_provider: "ollama"` in your config.
 
 Alternatively, copy `.env.example` to `.env` and fill in your keys:

cli/main.py

@@ -6,8 +6,9 @@ from functools import wraps
 from rich.console import Console
 from dotenv import load_dotenv
 
-# Load environment variables from .env file
+# Load environment variables
 load_dotenv()
+load_dotenv(".env.enterprise", override=False)
 from rich.panel import Panel
 from rich.spinner import Spinner
 from rich.live import Live
@@ -24,6 +25,12 @@ from rich.align import Align
 from rich.rule import Rule
 
 from tradingagents.graph.trading_graph import TradingAgentsGraph
+from tradingagents.graph.analyst_execution import (
+    AnalystWallTimeTracker,
+    build_analyst_execution_plan,
+    get_initial_analyst_node,
+    sync_analyst_tracker_from_chunk,
+)
 from tradingagents.default_config import DEFAULT_CONFIG
 from cli.models import AnalystType
 from cli.utils import *
@@ -79,7 +86,7 @@ class MessageBuffer:
         self.current_agent = None
         self.report_sections = {}
         self.selected_analysts = []
-        self._last_message_id = None
+        self._processed_message_ids = set()
 
     def init_for_analysis(self, selected_analysts):
         """Initialize agent status and report sections based on selected analysts.
@@ -114,7 +121,7 @@ class MessageBuffer:
         self.current_agent = None
         self.messages.clear()
         self.tool_calls.clear()
-        self._last_message_id = None
+        self._processed_message_ids.clear()
 
     def get_completed_reports_count(self):
         """Count reports that are finalized (their finalizing agent is completed).
@@ -809,7 +816,7 @@ ANALYST_REPORT_MAP = {
 }
 
 
-def update_analyst_statuses(message_buffer, chunk):
+def update_analyst_statuses(message_buffer, chunk, wall_time_tracker=None):
     """Update analyst statuses based on accumulated report state.
 
     Logic:
@@ -823,6 +830,9 @@ def update_analyst_statuses(message_buffer, chunk):
     selected = message_buffer.selected_analysts
     found_active = False
 
+    if wall_time_tracker is not None:
+        sync_analyst_tracker_from_chunk(wall_time_tracker, chunk)
+
     for analyst_key in ANALYST_ORDER:
         if analyst_key not in selected:
             continue
@@ -949,6 +959,11 @@ def run_analysis():
     # Normalize analyst selection to predefined order (selection is a 'set', order is fixed)
     selected_set = {analyst.value for analyst in selections["analysts"]}
     selected_analyst_keys = [a for a in ANALYST_ORDER if a in selected_set]
+    analyst_execution_plan = build_analyst_execution_plan(
+        selected_analyst_keys,
+        concurrency_limit=config["analyst_concurrency_limit"],
+    )
+    analyst_wall_time_tracker = AnalystWallTimeTracker(analyst_execution_plan)
 
     # Initialize the graph with callbacks bound to LLMs
     graph = TradingAgentsGraph(
@@ -1031,8 +1046,9 @@ def run_analysis():
         update_display(layout, stats_handler=stats_handler, start_time=start_time)
 
         # Update agent status to in_progress for the first analyst
-        first_analyst = f"{selections['analysts'][0].value.capitalize()} Analyst"
+        first_analyst = get_initial_analyst_node(analyst_execution_plan)
         message_buffer.update_agent_status(first_analyst, "in_progress")
+        analyst_wall_time_tracker.mark_started(selected_analyst_keys[0])
         update_display(layout, stats_handler=stats_handler, start_time=start_time)
 
         # Create spinner text
@@ -1052,31 +1068,31 @@ def run_analysis():
         # Stream the analysis
         trace = []
         for chunk in graph.graph.stream(init_agent_state, **args):
-            # Process messages if present (skip duplicates via message ID)
-            if len(chunk["messages"]) > 0:
-                last_message = chunk["messages"][-1]
-                msg_id = getattr(last_message, "id", None)
+            # Process all messages in chunk, deduplicating by message ID
+            for message in chunk.get("messages", []):
+                msg_id = getattr(message, "id", None)
+                if msg_id is not None:
+                    if msg_id in message_buffer._processed_message_ids:
+                        continue
+                    message_buffer._processed_message_ids.add(msg_id)
 
-                if msg_id != message_buffer._last_message_id:
-                    message_buffer._last_message_id = msg_id
+                msg_type, content = classify_message_type(message)
+                if content and content.strip():
+                    message_buffer.add_message(msg_type, content)
 
-                    # Add message to buffer
-                    msg_type, content = classify_message_type(last_message)
-                    if content and content.strip():
-                        message_buffer.add_message(msg_type, content)
-
-                    # Handle tool calls
-                    if hasattr(last_message, "tool_calls") and last_message.tool_calls:
-                        for tool_call in last_message.tool_calls:
-                            if isinstance(tool_call, dict):
-                                message_buffer.add_tool_call(
-                                    tool_call["name"], tool_call["args"]
-                                )
-                            else:
-                                message_buffer.add_tool_call(tool_call.name, tool_call.args)
+                if hasattr(message, "tool_calls") and message.tool_calls:
+                    for tool_call in message.tool_calls:
+                        if isinstance(tool_call, dict):
+                            message_buffer.add_tool_call(tool_call["name"], tool_call["args"])
+                        else:
+                            message_buffer.add_tool_call(tool_call.name, tool_call.args)
 
             # Update analyst statuses based on report state (runs on every chunk)
-            update_analyst_statuses(message_buffer, chunk)
+            update_analyst_statuses(
+                message_buffer,
+                chunk,
+                wall_time_tracker=analyst_wall_time_tracker,
+            )
 
             # Research Team - Handle Investment Debate State
             if chunk.get("investment_debate_state"):
@@ -1165,6 +1181,7 @@ def run_analysis():
         message_buffer.add_message(
             "System", f"Completed analysis for {selections['analysis_date']}"
         )
+        message_buffer.add_message("System", analyst_wall_time_tracker.format_summary())
 
         # Update final report sections
         for section in message_buffer.report_sections.keys():
@@ -1175,6 +1192,7 @@ def run_analysis():
 
     # Post-analysis prompts (outside Live context for clean interaction)
     console.print("\n[bold cyan]Analysis Complete![/bold cyan]\n")
+    console.print(f"[dim]{analyst_wall_time_tracker.format_summary()}[/dim]")
 
     # Prompt to save report
     save_choice = typer.prompt("Save report?", default="Y").strip().upper()

cli/utils.py

@@ -174,17 +174,30 @@ def select_openrouter_model() -> str:
     return choice
 
 
-def select_shallow_thinking_agent(provider) -> str:
-    """Select shallow thinking llm engine using an interactive selection."""
+def _prompt_custom_model_id() -> str:
+    """Prompt user to type a custom model ID."""
+    return questionary.text(
+        "Enter model ID:",
+        validate=lambda x: len(x.strip()) > 0 or "Please enter a model ID.",
+    ).ask().strip()
 
+
+def _select_model(provider: str, mode: str) -> str:
+    """Select a model for the given provider and mode (quick/deep)."""
     if provider.lower() == "openrouter":
         return select_openrouter_model()
 
+    if provider.lower() == "azure":
+        return questionary.text(
+            f"Enter Azure deployment name ({mode}-thinking):",
+            validate=lambda x: len(x.strip()) > 0 or "Please enter a deployment name.",
+        ).ask().strip()
+
     choice = questionary.select(
-        "Select Your [Quick-Thinking LLM Engine]:",
+        f"Select Your [{mode.title()}-Thinking LLM Engine]:",
         choices=[
             questionary.Choice(display, value=value)
-            for display, value in get_model_options(provider, "quick")
+            for display, value in get_model_options(provider, mode)
         ],
         instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
         style=questionary.Style(
@@ -197,58 +210,45 @@ def select_shallow_thinking_agent(provider) -> str:
     ).ask()
 
     if choice is None:
-        console.print(
-            "\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
-        )
+        console.print(f"\n[red]No {mode} thinking llm engine selected. Exiting...[/red]")
         exit(1)
 
+    if choice == "custom":
+        return _prompt_custom_model_id()
+
     return choice
 
 
+def select_shallow_thinking_agent(provider) -> str:
+    """Select shallow thinking llm engine using an interactive selection."""
+    return _select_model(provider, "quick")
+
+
 def select_deep_thinking_agent(provider) -> str:
     """Select deep thinking llm engine using an interactive selection."""
-
-    if provider.lower() == "openrouter":
-        return select_openrouter_model()
-
-    choice = questionary.select(
-        "Select Your [Deep-Thinking LLM Engine]:",
-        choices=[
-            questionary.Choice(display, value=value)
-            for display, value in get_model_options(provider, "deep")
-        ],
-        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
-        style=questionary.Style(
-            [
-                ("selected", "fg:magenta noinherit"),
-                ("highlighted", "fg:magenta noinherit"),
-                ("pointer", "fg:magenta noinherit"),
-            ]
-        ),
-    ).ask()
-
-    if choice is None:
-        console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
-        exit(1)
-
-    return choice
+    return _select_model(provider, "deep")
 
 def select_llm_provider() -> tuple[str, str | None]:
     """Select the LLM provider and its API endpoint."""
-    BASE_URLS = [
-        ("OpenAI", "https://api.openai.com/v1"),
-        ("Google", None),  # google-genai SDK manages its own endpoint
-        ("Anthropic", "https://api.anthropic.com/"),
-        ("xAI", "https://api.x.ai/v1"),
-        ("Openrouter", "https://openrouter.ai/api/v1"),
-        ("Ollama", "http://localhost:11434/v1"),
+    # (display_name, provider_key, base_url)
+    PROVIDERS = [
+        ("OpenAI", "openai", "https://api.openai.com/v1"),
+        ("Google", "google", None),
+        ("Anthropic", "anthropic", "https://api.anthropic.com/"),
+        ("xAI", "xai", "https://api.x.ai/v1"),
+        ("DeepSeek", "deepseek", "https://api.deepseek.com"),
+        ("Qwen", "qwen", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
+        ("GLM", "glm", "https://open.bigmodel.cn/api/paas/v4/"),
+        ("OpenRouter", "openrouter", "https://openrouter.ai/api/v1"),
+        ("Azure OpenAI", "azure", None),
+        ("Ollama", "ollama", "http://localhost:11434/v1"),
     ]
-    
+
     choice = questionary.select(
         "Select your LLM Provider:",
         choices=[
-            questionary.Choice(display, value=(display, value))
-            for display, value in BASE_URLS
+            questionary.Choice(display, value=(provider_key, url))
+            for display, provider_key, url in PROVIDERS
         ],
         instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
         style=questionary.Style(
@@ -261,13 +261,11 @@ def select_llm_provider() -> tuple[str, str | None]:
     ).ask()
     
     if choice is None:
-        console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
+        console.print("\n[red]No LLM provider selected. Exiting...[/red]")
         exit(1)
-    
-    display_name, url = choice
-    print(f"You selected: {display_name}\tURL: {url}")
 
-    return display_name, url
+    provider, url = choice
+    return provider, url
 
 
 def ask_openai_reasoning_effort() -> str:

docker-compose.yml

@@ -4,7 +4,7 @@ services:
     env_file:
       - .env
     volumes:
-      - ./results:/home/appuser/app/results
+      - tradingagents_data:/home/appuser/.tradingagents
     tty: true
     stdin_open: true
 
@@ -22,7 +22,7 @@ services:
     environment:
       - LLM_PROVIDER=ollama
     volumes:
-      - ./results:/home/appuser/app/results
+      - tradingagents_data:/home/appuser/.tradingagents
     depends_on:
       - ollama
     tty: true
@@ -31,4 +31,5 @@ services:
       - ollama
 
 volumes:
+  tradingagents_data:
   ollama_data:

tests/test_analyst_execution.py

@@ -0,0 +1,84 @@
+import unittest
+
+from tradingagents.graph.analyst_execution import (
+    AnalystWallTimeTracker,
+    build_analyst_execution_plan,
+    get_initial_analyst_node,
+    sync_analyst_tracker_from_chunk,
+)
+
+
+class AnalystExecutionPlanTests(unittest.TestCase):
+    def test_build_plan_preserves_selected_order(self):
+        plan = build_analyst_execution_plan(["news", "market"], concurrency_limit=2)
+
+        self.assertEqual([spec.key for spec in plan.specs], ["news", "market"])
+        self.assertEqual(plan.concurrency_limit, 2)
+        self.assertEqual(plan.specs[0].agent_node, "News Analyst")
+        self.assertEqual(plan.specs[0].tool_node, "tools_news")
+        self.assertEqual(plan.specs[0].clear_node, "Msg Clear News")
+
+    def test_rejects_unknown_analyst_keys(self):
+        with self.assertRaises(ValueError):
+            build_analyst_execution_plan(["market", "macro"])
+
+    def test_requires_positive_concurrency_limit(self):
+        with self.assertRaises(ValueError):
+            build_analyst_execution_plan(["market"], concurrency_limit=0)
+
+    def test_get_initial_analyst_node_uses_plan_metadata(self):
+        plan = build_analyst_execution_plan(["fundamentals", "news"])
+
+        self.assertEqual(
+            get_initial_analyst_node(plan),
+            "Fundamentals Analyst",
+        )
+
+
+class AnalystWallTimeTrackerTests(unittest.TestCase):
+    def test_records_wall_time_when_analyst_completes(self):
+        plan = build_analyst_execution_plan(["market", "news"])
+        tracker = AnalystWallTimeTracker(plan)
+
+        tracker.mark_started("market", started_at=10.0)
+        tracker.mark_completed("market", completed_at=13.5)
+
+        self.assertEqual(tracker.get_wall_times(), {"market": 3.5})
+
+    def test_formats_summary_in_plan_order(self):
+        plan = build_analyst_execution_plan(["news", "market"])
+        tracker = AnalystWallTimeTracker(plan)
+
+        tracker.mark_started("market", started_at=20.0)
+        tracker.mark_completed("market", completed_at=22.25)
+        tracker.mark_started("news", started_at=10.0)
+        tracker.mark_completed("news", completed_at=14.0)
+
+        self.assertEqual(
+            tracker.format_summary(),
+            "Analyst wall time: News 4.00s | Market 2.25s",
+        )
+
+    def test_syncs_wall_time_from_sequential_chunks(self):
+        plan = build_analyst_execution_plan(["market", "news"])
+        tracker = AnalystWallTimeTracker(plan)
+
+        sync_analyst_tracker_from_chunk(tracker, {}, now=10.0)
+        self.assertEqual(tracker.get_wall_times(), {})
+
+        sync_analyst_tracker_from_chunk(
+            tracker,
+            {"market_report": "done"},
+            now=13.0,
+        )
+        self.assertEqual(tracker.get_wall_times(), {"market": 3.0})
+
+        sync_analyst_tracker_from_chunk(
+            tracker,
+            {"market_report": "done", "news_report": "done"},
+            now=18.0,
+        )
+        self.assertEqual(
+            tracker.get_wall_times(),
+            {"market": 3.0, "news": 5.0},
+        )

tradingagents/agents/utils/memory.py

@@ -78,7 +78,7 @@ class FinancialSituationMemory:
 
         # Build results
         results = []
-        max_score = max(scores) if max(scores) > 0 else 1  # Normalize scores
+        max_score = float(scores.max()) if len(scores) > 0 and scores.max() > 0 else 1.0
 
         for idx in top_indices:
             # Normalize score to 0-1 range for consistency

tradingagents/default_config.py

@@ -1,12 +1,11 @@
 import os
 
+_TRADINGAGENTS_HOME = os.path.join(os.path.expanduser("~"), ".tradingagents")
+
 DEFAULT_CONFIG = {
     "project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
-    "results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
-    "data_cache_dir": os.path.join(
-        os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
-        "dataflows/data_cache",
-    ),
+    "results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", os.path.join(_TRADINGAGENTS_HOME, "logs")),
+    "data_cache_dir": os.getenv("TRADINGAGENTS_CACHE_DIR", os.path.join(_TRADINGAGENTS_HOME, "cache")),
     # LLM settings
     "llm_provider": "openai",
     "deep_think_llm": "gpt-5.4",
@@ -23,6 +22,7 @@ DEFAULT_CONFIG = {
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,
     "max_recur_limit": 100,
+    "analyst_concurrency_limit": 1,
     # Data vendor configuration
     # Category-level configuration (default for all tools in category)
     "data_vendors": {

tradingagents/graph/analyst_execution.py

@@ -0,0 +1,136 @@
+from dataclasses import dataclass
+from time import monotonic
+from typing import Dict, Iterable, List, Optional
+
+
+@dataclass(frozen=True)
+class AnalystNodeSpec:
+    key: str
+    agent_node: str
+    clear_node: str
+    tool_node: str
+    report_key: str
+
+
+@dataclass(frozen=True)
+class AnalystExecutionPlan:
+    specs: List[AnalystNodeSpec]
+    concurrency_limit: int
+
+
+ANALYST_NODE_SPECS: Dict[str, AnalystNodeSpec] = {
+    "market": AnalystNodeSpec(
+        key="market",
+        agent_node="Market Analyst",
+        clear_node="Msg Clear Market",
+        tool_node="tools_market",
+        report_key="market_report",
+    ),
+    "social": AnalystNodeSpec(
+        key="social",
+        agent_node="Social Analyst",
+        clear_node="Msg Clear Social",
+        tool_node="tools_social",
+        report_key="sentiment_report",
+    ),
+    "news": AnalystNodeSpec(
+        key="news",
+        agent_node="News Analyst",
+        clear_node="Msg Clear News",
+        tool_node="tools_news",
+        report_key="news_report",
+    ),
+    "fundamentals": AnalystNodeSpec(
+        key="fundamentals",
+        agent_node="Fundamentals Analyst",
+        clear_node="Msg Clear Fundamentals",
+        tool_node="tools_fundamentals",
+        report_key="fundamentals_report",
+    ),
+}
+
+
+def build_analyst_execution_plan(
+    selected_analysts: Iterable[str],
+    concurrency_limit: int = 1,
+) -> AnalystExecutionPlan:
+    if concurrency_limit < 1:
+        raise ValueError("analyst concurrency limit must be >= 1")
+
+    specs: List[AnalystNodeSpec] = []
+    for analyst_key in selected_analysts:
+        spec = ANALYST_NODE_SPECS.get(analyst_key)
+        if spec is None:
+            raise ValueError(f"unknown analyst key: {analyst_key}")
+        specs.append(spec)
+
+    if not specs:
+        raise ValueError("at least one analyst must be selected")
+
+    return AnalystExecutionPlan(specs=specs, concurrency_limit=concurrency_limit)
+
+
+def get_initial_analyst_node(plan: AnalystExecutionPlan) -> str:
+    return plan.specs[0].agent_node
+
+
+class AnalystWallTimeTracker:
+    def __init__(self, plan: AnalystExecutionPlan):
+        self.plan = plan
+        self._started_at: Dict[str, float] = {}
+        self._wall_times: Dict[str, float] = {}
+
+    def mark_started(self, analyst_key: str, started_at: Optional[float] = None) -> None:
+        if analyst_key not in ANALYST_NODE_SPECS:
+            raise ValueError(f"unknown analyst key: {analyst_key}")
+        self._started_at.setdefault(analyst_key, monotonic() if started_at is None else started_at)
+
+    def mark_completed(
+        self,
+        analyst_key: str,
+        completed_at: Optional[float] = None,
+    ) -> None:
+        if analyst_key not in ANALYST_NODE_SPECS:
+            raise ValueError(f"unknown analyst key: {analyst_key}")
+        if analyst_key in self._wall_times:
+            return
+        started_at = self._started_at.get(analyst_key)
+        if started_at is None:
+            return
+        finished_at = monotonic() if completed_at is None else completed_at
+        self._wall_times[analyst_key] = max(0.0, finished_at - started_at)
+
+    def get_wall_times(self) -> Dict[str, float]:
+        return dict(self._wall_times)
+
+    def format_summary(self) -> str:
+        parts = []
+        for spec in self.plan.specs:
+            duration = self._wall_times.get(spec.key)
+            if duration is not None:
+                label = spec.agent_node.removesuffix(" Analyst")
+                parts.append(f"{label} {duration:.2f}s")
+        if not parts:
+            return "Analyst wall time: pending"
+        return "Analyst wall time: " + " | ".join(parts)
+
+
+def sync_analyst_tracker_from_chunk(
+    tracker: AnalystWallTimeTracker,
+    chunk: Dict[str, str],
+    now: Optional[float] = None,
+) -> None:
+    current_time = monotonic() if now is None else now
+    active_found = False
+
+    for spec in tracker.plan.specs:
+        has_report = bool(chunk.get(spec.report_key))
+
+        if has_report:
+            tracker.mark_started(spec.key, started_at=current_time)
+            tracker.mark_completed(spec.key, completed_at=current_time)
+            continue
+
+        if not active_found:
+            tracker.mark_started(spec.key, started_at=current_time)
+            active_found = True

tradingagents/graph/setup.py

@@ -7,6 +7,7 @@ from langgraph.prebuilt import ToolNode
 from tradingagents.agents import *
 from tradingagents.agents.utils.agent_states import AgentState
 
+from .analyst_execution import build_analyst_execution_plan
 from .conditional_logic import ConditionalLogic
 
 
@@ -24,6 +25,7 @@ class GraphSetup:
         invest_judge_memory,
         portfolio_manager_memory,
         conditional_logic: ConditionalLogic,
+        analyst_concurrency_limit: int = 1,
     ):
         """Initialize with required components."""
         self.quick_thinking_llm = quick_thinking_llm
@@ -35,6 +37,7 @@ class GraphSetup:
         self.invest_judge_memory = invest_judge_memory
         self.portfolio_manager_memory = portfolio_manager_memory
         self.conditional_logic = conditional_logic
+        self.analyst_concurrency_limit = analyst_concurrency_limit
 
     def setup_graph(
         self, selected_analysts=["market", "social", "news", "fundamentals"]
@@ -48,41 +51,17 @@ class GraphSetup:
                 - "news": News analyst
                 - "fundamentals": Fundamentals analyst
         """
-        if len(selected_analysts) == 0:
-            raise ValueError("Trading Agents Graph Setup Error: no analysts selected!")
+        plan = build_analyst_execution_plan(
+            selected_analysts,
+            concurrency_limit=self.analyst_concurrency_limit,
+        )
 
-        # Create analyst nodes
-        analyst_nodes = {}
-        delete_nodes = {}
-        tool_nodes = {}
-
-        if "market" in selected_analysts:
-            analyst_nodes["market"] = create_market_analyst(
-                self.quick_thinking_llm
-            )
-            delete_nodes["market"] = create_msg_delete()
-            tool_nodes["market"] = self.tool_nodes["market"]
-
-        if "social" in selected_analysts:
-            analyst_nodes["social"] = create_social_media_analyst(
-                self.quick_thinking_llm
-            )
-            delete_nodes["social"] = create_msg_delete()
-            tool_nodes["social"] = self.tool_nodes["social"]
-
-        if "news" in selected_analysts:
-            analyst_nodes["news"] = create_news_analyst(
-                self.quick_thinking_llm
-            )
-            delete_nodes["news"] = create_msg_delete()
-            tool_nodes["news"] = self.tool_nodes["news"]
-
-        if "fundamentals" in selected_analysts:
-            analyst_nodes["fundamentals"] = create_fundamentals_analyst(
-                self.quick_thinking_llm
-            )
-            delete_nodes["fundamentals"] = create_msg_delete()
-            tool_nodes["fundamentals"] = self.tool_nodes["fundamentals"]
+        analyst_factories = {
+            "market": lambda: create_market_analyst(self.quick_thinking_llm),
+            "social": lambda: create_social_media_analyst(self.quick_thinking_llm),
+            "news": lambda: create_news_analyst(self.quick_thinking_llm),
+            "fundamentals": lambda: create_fundamentals_analyst(self.quick_thinking_llm),
+        }
 
         # Create researcher and manager nodes
         bull_researcher_node = create_bull_researcher(
@@ -108,12 +87,10 @@ class GraphSetup:
         workflow = StateGraph(AgentState)
 
         # Add analyst nodes to the graph
-        for analyst_type, node in analyst_nodes.items():
-            workflow.add_node(f"{analyst_type.capitalize()} Analyst", node)
-            workflow.add_node(
-                f"Msg Clear {analyst_type.capitalize()}", delete_nodes[analyst_type]
-            )
-            workflow.add_node(f"tools_{analyst_type}", tool_nodes[analyst_type])
+        for spec in plan.specs:
+            workflow.add_node(spec.agent_node, analyst_factories[spec.key]())
+            workflow.add_node(spec.clear_node, create_msg_delete())
+            workflow.add_node(spec.tool_node, self.tool_nodes[spec.key])
 
         # Add other nodes
         workflow.add_node("Bull Researcher", bull_researcher_node)
@@ -127,27 +104,25 @@ class GraphSetup:
 
         # Define edges
         # Start with the first analyst
-        first_analyst = selected_analysts[0]
-        workflow.add_edge(START, f"{first_analyst.capitalize()} Analyst")
+        workflow.add_edge(START, plan.specs[0].agent_node)
 
         # Connect analysts in sequence
-        for i, analyst_type in enumerate(selected_analysts):
-            current_analyst = f"{analyst_type.capitalize()} Analyst"
-            current_tools = f"tools_{analyst_type}"
-            current_clear = f"Msg Clear {analyst_type.capitalize()}"
+        for i, spec in enumerate(plan.specs):
+            current_analyst = spec.agent_node
+            current_tools = spec.tool_node
+            current_clear = spec.clear_node
 
             # Add conditional edges for current analyst
             workflow.add_conditional_edges(
                 current_analyst,
-                getattr(self.conditional_logic, f"should_continue_{analyst_type}"),
+                getattr(self.conditional_logic, f"should_continue_{spec.key}"),
                 [current_tools, current_clear],
             )
             workflow.add_edge(current_tools, current_analyst)
 
             # Connect to next analyst or to Bull Researcher if this is the last analyst
-            if i < len(selected_analysts) - 1:
-                next_analyst = f"{selected_analysts[i+1].capitalize()} Analyst"
-                workflow.add_edge(current_clear, next_analyst)
+            if i < len(plan.specs) - 1:
+                workflow.add_edge(current_clear, plan.specs[i + 1].agent_node)
             else:
                 workflow.add_edge(current_clear, "Bull Researcher")
 

tradingagents/graph/trading_graph.py

@@ -66,10 +66,8 @@ class TradingAgentsGraph:
         set_config(self.config)
 
         # Create necessary directories
-        os.makedirs(
-            os.path.join(self.config["project_dir"], "dataflows/data_cache"),
-            exist_ok=True,
-        )
+        os.makedirs(self.config["data_cache_dir"], exist_ok=True)
+        os.makedirs(self.config["results_dir"], exist_ok=True)
 
         # Initialize LLMs with provider-specific thinking configuration
         llm_kwargs = self._get_provider_kwargs()
@@ -119,6 +117,7 @@ class TradingAgentsGraph:
             self.invest_judge_memory,
             self.portfolio_manager_memory,
             self.conditional_logic,
+            analyst_concurrency_limit=self.config.get("analyst_concurrency_limit", 1),
         )
 
         self.propagator = Propagator()

tradingagents/llm_clients/azure_client.py

@@ -0,0 +1,52 @@
+import os
+from typing import Any, Optional
+
+from langchain_openai import AzureChatOpenAI
+
+from .base_client import BaseLLMClient, normalize_content
+from .validators import validate_model
+
+_PASSTHROUGH_KWARGS = (
+    "timeout", "max_retries", "api_key", "reasoning_effort",
+    "callbacks", "http_client", "http_async_client",
+)
+
+
+class NormalizedAzureChatOpenAI(AzureChatOpenAI):
+    """AzureChatOpenAI with normalized content output."""
+
+    def invoke(self, input, config=None, **kwargs):
+        return normalize_content(super().invoke(input, config, **kwargs))
+
+
+class AzureOpenAIClient(BaseLLMClient):
+    """Client for Azure OpenAI deployments.
+
+    Requires environment variables:
+        AZURE_OPENAI_API_KEY: API key
+        AZURE_OPENAI_ENDPOINT: Endpoint URL (e.g. https://<resource>.openai.azure.com/)
+        AZURE_OPENAI_DEPLOYMENT_NAME: Deployment name
+        OPENAI_API_VERSION: API version (e.g. 2025-03-01-preview)
+    """
+
+    def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
+        super().__init__(model, base_url, **kwargs)
+
+    def get_llm(self) -> Any:
+        """Return configured AzureChatOpenAI instance."""
+        self.warn_if_unknown_model()
+
+        llm_kwargs = {
+            "model": self.model,
+            "azure_deployment": os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME", self.model),
+        }
+
+        for key in _PASSTHROUGH_KWARGS:
+            if key in self.kwargs:
+                llm_kwargs[key] = self.kwargs[key]
+
+        return NormalizedAzureChatOpenAI(**llm_kwargs)
+
+    def validate_model(self) -> bool:
+        """Azure accepts any deployed model name."""
+        return True

tradingagents/llm_clients/factory.py

@@ -4,6 +4,12 @@ from .base_client import BaseLLMClient
 from .openai_client import OpenAIClient
 from .anthropic_client import AnthropicClient
 from .google_client import GoogleClient
+from .azure_client import AzureOpenAIClient
+
+# Providers that use the OpenAI-compatible chat completions API
+_OPENAI_COMPATIBLE = (
+    "openai", "xai", "deepseek", "qwen", "glm", "ollama", "openrouter",
+)
 
 
 def create_llm_client(
@@ -15,16 +21,10 @@ def create_llm_client(
     """Create an LLM client for the specified provider.
 
     Args:
-        provider: LLM provider (openai, anthropic, google, xai, ollama, openrouter)
+        provider: LLM provider name
         model: Model name/identifier
         base_url: Optional base URL for API endpoint
         **kwargs: Additional provider-specific arguments
-            - http_client: Custom httpx.Client for SSL proxy or certificate customization
-            - http_async_client: Custom httpx.AsyncClient for async operations
-            - timeout: Request timeout in seconds
-            - max_retries: Maximum retry attempts
-            - api_key: API key for the provider
-            - callbacks: LangChain callbacks
 
     Returns:
         Configured BaseLLMClient instance
@@ -34,16 +34,16 @@ def create_llm_client(
     """
     provider_lower = provider.lower()
 
-    if provider_lower in ("openai", "ollama", "openrouter"):
+    if provider_lower in _OPENAI_COMPATIBLE:
         return OpenAIClient(model, base_url, provider=provider_lower, **kwargs)
 
-    if provider_lower == "xai":
-        return OpenAIClient(model, base_url, provider="xai", **kwargs)
-
     if provider_lower == "anthropic":
         return AnthropicClient(model, base_url, **kwargs)
 
     if provider_lower == "google":
         return GoogleClient(model, base_url, **kwargs)
 
+    if provider_lower == "azure":
+        return AzureOpenAIClient(model, base_url, **kwargs)
+
     raise ValueError(f"Unsupported LLM provider: {provider}")

tradingagents/llm_clients/model_catalog.py

@@ -63,8 +63,43 @@ MODEL_OPTIONS: ProviderModeOptions = {
             ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
         ],
     },
-    # OpenRouter models are fetched dynamically at CLI runtime.
-    # No static entries needed; any model ID is accepted by the validator.
+    "deepseek": {
+        "quick": [
+            ("DeepSeek V3.2", "deepseek-chat"),
+            ("Custom model ID", "custom"),
+        ],
+        "deep": [
+            ("DeepSeek V3.2 (thinking)", "deepseek-reasoner"),
+            ("DeepSeek V3.2", "deepseek-chat"),
+            ("Custom model ID", "custom"),
+        ],
+    },
+    "qwen": {
+        "quick": [
+            ("Qwen 3.5 Flash", "qwen3.5-flash"),
+            ("Qwen Plus", "qwen-plus"),
+            ("Custom model ID", "custom"),
+        ],
+        "deep": [
+            ("Qwen 3.6 Plus", "qwen3.6-plus"),
+            ("Qwen 3.5 Plus", "qwen3.5-plus"),
+            ("Qwen 3 Max", "qwen3-max"),
+            ("Custom model ID", "custom"),
+        ],
+    },
+    "glm": {
+        "quick": [
+            ("GLM-4.7", "glm-4.7"),
+            ("GLM-5", "glm-5"),
+            ("Custom model ID", "custom"),
+        ],
+        "deep": [
+            ("GLM-5.1", "glm-5.1"),
+            ("GLM-5", "glm-5"),
+            ("Custom model ID", "custom"),
+        ],
+    },
+    # OpenRouter: fetched dynamically. Azure: any deployed model name.
     "ollama": {
         "quick": [
             ("Qwen3:latest (8B, local)", "qwen3:latest"),

tradingagents/llm_clients/openai_client.py

@@ -27,6 +27,9 @@ _PASSTHROUGH_KWARGS = (
 # Provider base URLs and API key env vars
 _PROVIDER_CONFIG = {
     "xai": ("https://api.x.ai/v1", "XAI_API_KEY"),
+    "deepseek": ("https://api.deepseek.com", "DEEPSEEK_API_KEY"),
+    "qwen": ("https://dashscope-intl.aliyuncs.com/compatible-mode/v1", "DASHSCOPE_API_KEY"),
+    "glm": ("https://api.z.ai/api/paas/v4/", "ZHIPU_API_KEY"),
     "openrouter": ("https://openrouter.ai/api/v1", "OPENROUTER_API_KEY"),
     "ollama": ("http://localhost:11434/v1", None),
 }