Merge remote-tracking branch 'upstream/main' into analyst-phase1-observability

# Conflicts:
#	tradingagents/default_config.py
#	tradingagents/graph/setup.py

tradingagents/graph/checkpointer.py

@@ -0,0 +1,90 @@
+"""LangGraph checkpoint support for resumable analysis runs.
+
+Per-ticker SQLite databases so concurrent tickers don't contend.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import sqlite3
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Generator
+
+from langgraph.checkpoint.sqlite import SqliteSaver
+
+from tradingagents.dataflows.utils import safe_ticker_component
+
+
+def _db_path(data_dir: str | Path, ticker: str) -> Path:
+    """Return the SQLite checkpoint DB path for a ticker."""
+    # Reject ticker values that would escape the checkpoints directory.
+    safe = safe_ticker_component(ticker).upper()
+    p = Path(data_dir) / "checkpoints"
+    p.mkdir(parents=True, exist_ok=True)
+    return p / f"{safe}.db"
+
+
+def thread_id(ticker: str, date: str) -> str:
+    """Deterministic thread ID for a ticker+date pair."""
+    return hashlib.sha256(f"{ticker.upper()}:{date}".encode()).hexdigest()[:16]
+
+
+@contextmanager
+def get_checkpointer(data_dir: str | Path, ticker: str) -> Generator[SqliteSaver, None, None]:
+    """Context manager yielding a SqliteSaver backed by a per-ticker DB."""
+    db = _db_path(data_dir, ticker)
+    conn = sqlite3.connect(str(db), check_same_thread=False)
+    try:
+        saver = SqliteSaver(conn)
+        saver.setup()
+        yield saver
+    finally:
+        conn.close()
+
+
+def has_checkpoint(data_dir: str | Path, ticker: str, date: str) -> bool:
+    """Check whether a resumable checkpoint exists for ticker+date."""
+    return checkpoint_step(data_dir, ticker, date) is not None
+
+
+def checkpoint_step(data_dir: str | Path, ticker: str, date: str) -> int | None:
+    """Return the step number of the latest checkpoint, or None if none exists."""
+    db = _db_path(data_dir, ticker)
+    if not db.exists():
+        return None
+    tid = thread_id(ticker, date)
+    with get_checkpointer(data_dir, ticker) as saver:
+        config = {"configurable": {"thread_id": tid}}
+        cp = saver.get_tuple(config)
+        if cp is None:
+            return None
+        return cp.metadata.get("step")
+
+
+def clear_all_checkpoints(data_dir: str | Path) -> int:
+    """Remove all checkpoint DBs. Returns number of files deleted."""
+    cp_dir = Path(data_dir) / "checkpoints"
+    if not cp_dir.exists():
+        return 0
+    dbs = list(cp_dir.glob("*.db"))
+    for db in dbs:
+        db.unlink()
+    return len(dbs)
+
+
+def clear_checkpoint(data_dir: str | Path, ticker: str, date: str) -> None:
+    """Remove checkpoint for a specific ticker+date by deleting the thread's rows."""
+    db = _db_path(data_dir, ticker)
+    if not db.exists():
+        return
+    tid = thread_id(ticker, date)
+    conn = sqlite3.connect(str(db))
+    try:
+        for table in ("writes", "checkpoints"):
+            conn.execute(f"DELETE FROM {table} WHERE thread_id = ?", (tid,))
+        conn.commit()
+    except sqlite3.OperationalError:
+        pass
+    finally:
+        conn.close()

tradingagents/graph/propagation.py

@@ -16,13 +16,14 @@ class Propagator:
         self.max_recur_limit = max_recur_limit
 
     def create_initial_state(
-        self, company_name: str, trade_date: str
+        self, company_name: str, trade_date: str, past_context: str = ""
     ) -> Dict[str, Any]:
         """Create the initial state for the agent graph."""
         return {
             "messages": [("human", company_name)],
             "company_of_interest": company_name,
             "trade_date": str(trade_date),
+            "past_context": past_context,
             "investment_debate_state": InvestDebateState(
                 {
                     "bull_history": "",

tradingagents/graph/reflection.py

@@ -1,121 +1,53 @@
 # TradingAgents/graph/reflection.py
 
-from typing import Dict, Any
-from langchain_openai import ChatOpenAI
+from typing import Any
 
 
 class Reflector:
-    """Handles reflection on decisions and updating memory."""
+    """Handles reflection on trading decisions."""
 
-    def __init__(self, quick_thinking_llm: ChatOpenAI):
+    def __init__(self, quick_thinking_llm: Any):
         """Initialize the reflector with an LLM."""
         self.quick_thinking_llm = quick_thinking_llm
-        self.reflection_system_prompt = self._get_reflection_prompt()
+        self.log_reflection_prompt = self._get_log_reflection_prompt()
 
-    def _get_reflection_prompt(self) -> str:
-        """Get the system prompt for reflection."""
-        return """
-You are an expert financial analyst tasked with reviewing trading decisions/analysis and providing a comprehensive, step-by-step analysis. 
-Your goal is to deliver detailed insights into investment decisions and highlight opportunities for improvement, adhering strictly to the following guidelines:
+    def _get_log_reflection_prompt(self) -> str:
+        """Concise prompt for reflect_on_final_decision (Phase B log entries).
 
-1. Reasoning:
-   - For each trading decision, determine whether it was correct or incorrect. A correct decision results in an increase in returns, while an incorrect decision does the opposite.
-   - Analyze the contributing factors to each success or mistake. Consider:
-     - Market intelligence.
-     - Technical indicators.
-     - Technical signals.
-     - Price movement analysis.
-     - Overall market data analysis 
-     - News analysis.
-     - Social media and sentiment analysis.
-     - Fundamental data analysis.
-     - Weight the importance of each factor in the decision-making process.
+        Produces 2-4 sentences of plain prose — compact enough to be re-injected
+        into future agent prompts without bloating the context window.
+        """
+        return (
+            "You are a trading analyst reviewing your own past decision now that the outcome is known.\n"
+            "Write exactly 2-4 sentences of plain prose (no bullets, no headers, no markdown).\n\n"
+            "Cover in order:\n"
+            "1. Was the directional call correct? (cite the alpha figure)\n"
+            "2. Which part of the investment thesis held or failed?\n"
+            "3. One concrete lesson to apply to the next similar analysis.\n\n"
+            "Be specific and terse. Your output will be stored verbatim in a decision log "
+            "and re-read by future analysts, so every word must earn its place."
+        )
 
-2. Improvement:
-   - For any incorrect decisions, propose revisions to maximize returns.
-   - Provide a detailed list of corrective actions or improvements, including specific recommendations (e.g., changing a decision from HOLD to BUY on a particular date).
-
-3. Summary:
-   - Summarize the lessons learned from the successes and mistakes.
-   - Highlight how these lessons can be adapted for future trading scenarios and draw connections between similar situations to apply the knowledge gained.
-
-4. Query:
-   - Extract key insights from the summary into a concise sentence of no more than 1000 tokens.
-   - Ensure the condensed sentence captures the essence of the lessons and reasoning for easy reference.
-
-Adhere strictly to these instructions, and ensure your output is detailed, accurate, and actionable. You will also be given objective descriptions of the market from a price movements, technical indicator, news, and sentiment perspective to provide more context for your analysis.
-"""
-
-    def _extract_current_situation(self, current_state: Dict[str, Any]) -> str:
-        """Extract the current market situation from the state."""
-        curr_market_report = current_state["market_report"]
-        curr_sentiment_report = current_state["sentiment_report"]
-        curr_news_report = current_state["news_report"]
-        curr_fundamentals_report = current_state["fundamentals_report"]
-
-        return f"{curr_market_report}\n\n{curr_sentiment_report}\n\n{curr_news_report}\n\n{curr_fundamentals_report}"
-
-    def _reflect_on_component(
-        self, component_type: str, report: str, situation: str, returns_losses
+    def reflect_on_final_decision(
+        self,
+        final_decision: str,
+        raw_return: float,
+        alpha_return: float,
     ) -> str:
-        """Generate reflection for a component."""
+        """Single reflection call on the final trade decision with outcome context.
+
+        Used by Phase B deferred reflection. The final_trade_decision already
+        synthesises all analyst insights, so no separate market context is needed.
+        """
         messages = [
-            ("system", self.reflection_system_prompt),
+            ("system", self.log_reflection_prompt),
             (
                 "human",
-                f"Returns: {returns_losses}\n\nAnalysis/Decision: {report}\n\nObjective Market Reports for Reference: {situation}",
+                (
+                    f"Raw return: {raw_return:+.1%}\n"
+                    f"Alpha vs SPY: {alpha_return:+.1%}\n\n"
+                    f"Final Decision:\n{final_decision}"
+                ),
             ),
         ]
-
-        result = self.quick_thinking_llm.invoke(messages).content
-        return result
-
-    def reflect_bull_researcher(self, current_state, returns_losses, bull_memory):
-        """Reflect on bull researcher's analysis and update memory."""
-        situation = self._extract_current_situation(current_state)
-        bull_debate_history = current_state["investment_debate_state"]["bull_history"]
-
-        result = self._reflect_on_component(
-            "BULL", bull_debate_history, situation, returns_losses
-        )
-        bull_memory.add_situations([(situation, result)])
-
-    def reflect_bear_researcher(self, current_state, returns_losses, bear_memory):
-        """Reflect on bear researcher's analysis and update memory."""
-        situation = self._extract_current_situation(current_state)
-        bear_debate_history = current_state["investment_debate_state"]["bear_history"]
-
-        result = self._reflect_on_component(
-            "BEAR", bear_debate_history, situation, returns_losses
-        )
-        bear_memory.add_situations([(situation, result)])
-
-    def reflect_trader(self, current_state, returns_losses, trader_memory):
-        """Reflect on trader's decision and update memory."""
-        situation = self._extract_current_situation(current_state)
-        trader_decision = current_state["trader_investment_plan"]
-
-        result = self._reflect_on_component(
-            "TRADER", trader_decision, situation, returns_losses
-        )
-        trader_memory.add_situations([(situation, result)])
-
-    def reflect_invest_judge(self, current_state, returns_losses, invest_judge_memory):
-        """Reflect on investment judge's decision and update memory."""
-        situation = self._extract_current_situation(current_state)
-        judge_decision = current_state["investment_debate_state"]["judge_decision"]
-
-        result = self._reflect_on_component(
-            "INVEST JUDGE", judge_decision, situation, returns_losses
-        )
-        invest_judge_memory.add_situations([(situation, result)])
-
-    def reflect_portfolio_manager(self, current_state, returns_losses, portfolio_manager_memory):
-        """Reflect on portfolio manager's decision and update memory."""
-        situation = self._extract_current_situation(current_state)
-        judge_decision = current_state["risk_debate_state"]["judge_decision"]
-
-        result = self._reflect_on_component(
-            "PORTFOLIO MANAGER", judge_decision, situation, returns_losses
-        )
-        portfolio_manager_memory.add_situations([(situation, result)])
+        return self.quick_thinking_llm.invoke(messages).content

tradingagents/graph/setup.py

@@ -1,8 +1,7 @@
 # TradingAgents/graph/setup.py
 
-from typing import Dict, Any
-from langchain_openai import ChatOpenAI
-from langgraph.graph import END, StateGraph, START
+from typing import Any, Dict
+from langgraph.graph import END, START, StateGraph
 from langgraph.prebuilt import ToolNode
 
 from tradingagents.agents import *
@@ -17,14 +16,9 @@ class GraphSetup:
 
     def __init__(
         self,
-        quick_thinking_llm: ChatOpenAI,
-        deep_thinking_llm: ChatOpenAI,
+        quick_thinking_llm: Any,
+        deep_thinking_llm: Any,
         tool_nodes: Dict[str, ToolNode],
-        bull_memory,
-        bear_memory,
-        trader_memory,
-        invest_judge_memory,
-        portfolio_manager_memory,
         conditional_logic: ConditionalLogic,
         analyst_concurrency_limit: int = 1,
     ):
@@ -32,11 +26,6 @@ class GraphSetup:
         self.quick_thinking_llm = quick_thinking_llm
         self.deep_thinking_llm = deep_thinking_llm
         self.tool_nodes = tool_nodes
-        self.bull_memory = bull_memory
-        self.bear_memory = bear_memory
-        self.trader_memory = trader_memory
-        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
 
@@ -59,30 +48,22 @@ class GraphSetup:
 
         analyst_factories = {
             "market": lambda: create_market_analyst(self.quick_thinking_llm),
-            "social": lambda: create_social_media_analyst(self.quick_thinking_llm),
+            "social": lambda: create_sentiment_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(
-            self.quick_thinking_llm, self.bull_memory
-        )
-        bear_researcher_node = create_bear_researcher(
-            self.quick_thinking_llm, self.bear_memory
-        )
-        research_manager_node = create_research_manager(
-            self.deep_thinking_llm, self.invest_judge_memory
-        )
-        trader_node = create_trader(self.quick_thinking_llm, self.trader_memory)
+        bull_researcher_node = create_bull_researcher(self.quick_thinking_llm)
+        bear_researcher_node = create_bear_researcher(self.quick_thinking_llm)
+        research_manager_node = create_research_manager(self.deep_thinking_llm)
+        trader_node = create_trader(self.quick_thinking_llm)
 
         # Create risk analysis nodes
         aggressive_analyst = create_aggressive_debator(self.quick_thinking_llm)
         neutral_analyst = create_neutral_debator(self.quick_thinking_llm)
         conservative_analyst = create_conservative_debator(self.quick_thinking_llm)
-        portfolio_manager_node = create_portfolio_manager(
-            self.deep_thinking_llm, self.portfolio_manager_memory
-        )
+        portfolio_manager_node = create_portfolio_manager(self.deep_thinking_llm)
 
         # Create workflow
         workflow = StateGraph(AgentState)
@@ -173,5 +154,4 @@ class GraphSetup:
 
         workflow.add_edge("Portfolio Manager", END)
 
-        # Compile and return
-        return workflow.compile()
+        return workflow

tradingagents/graph/signal_processing.py

@@ -1,33 +1,31 @@
-# TradingAgents/graph/signal_processing.py
+"""Extract the 5-tier portfolio rating from the Portfolio Manager's decision.
 
-from langchain_openai import ChatOpenAI
+The Portfolio Manager produces a typed ``PortfolioDecision`` via structured
+output and renders it to markdown that always carries a ``**Rating**: X``
+header (see :func:`tradingagents.agents.schemas.render_pm_decision`).  The
+deterministic heuristic in :mod:`tradingagents.agents.utils.rating` is more
+than sufficient to extract that rating; no extra LLM call is needed.
+
+This module exists for backwards compatibility with callers that expect a
+``SignalProcessor.process_signal(text)`` interface.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from tradingagents.agents.utils.rating import parse_rating
 
 
 class SignalProcessor:
-    """Processes trading signals to extract actionable decisions."""
+    """Read the 5-tier rating out of a Portfolio Manager decision."""
 
-    def __init__(self, quick_thinking_llm: ChatOpenAI):
-        """Initialize with an LLM for processing."""
+    def __init__(self, quick_thinking_llm: Any = None):
+        # The LLM argument is accepted for backwards compatibility but no
+        # longer used: the PM's structured output guarantees the rating is
+        # parseable from the rendered markdown without a second LLM call.
         self.quick_thinking_llm = quick_thinking_llm
 
     def process_signal(self, full_signal: str) -> str:
-        """
-        Process a full trading signal to extract the core decision.
-
-        Args:
-            full_signal: Complete trading signal text
-
-        Returns:
-            Extracted rating (BUY, OVERWEIGHT, HOLD, UNDERWEIGHT, or SELL)
-        """
-        messages = [
-            (
-                "system",
-                "You are an efficient assistant that extracts the trading decision from analyst reports. "
-                "Extract the rating as exactly one of: BUY, OVERWEIGHT, HOLD, UNDERWEIGHT, SELL. "
-                "Output only the single rating word, nothing else.",
-            ),
-            ("human", full_signal),
-        ]
-
-        return self.quick_thinking_llm.invoke(messages).content
+        """Return one of Buy / Overweight / Hold / Underweight / Sell."""
+        return parse_rating(full_signal)

tradingagents/graph/trading_graph.py

@@ -1,18 +1,24 @@
 # TradingAgents/graph/trading_graph.py
 
+import logging
 import os
 from pathlib import Path
 import json
-from datetime import date
+from datetime import datetime, timedelta
 from typing import Dict, Any, Tuple, List, Optional
 
+import yfinance as yf
+
+logger = logging.getLogger(__name__)
+
 from langgraph.prebuilt import ToolNode
 
 from tradingagents.llm_clients import create_llm_client
 
 from tradingagents.agents import *
 from tradingagents.default_config import DEFAULT_CONFIG
-from tradingagents.agents.utils.memory import FinancialSituationMemory
+from tradingagents.agents.utils.memory import TradingMemoryLog
+from tradingagents.dataflows.utils import safe_ticker_component
 from tradingagents.agents.utils.agent_states import (
     AgentState,
     InvestDebateState,
@@ -33,6 +39,7 @@ from tradingagents.agents.utils.agent_utils import (
     get_global_news
 )
 
+from .checkpointer import checkpoint_step, clear_checkpoint, get_checkpointer, thread_id
 from .conditional_logic import ConditionalLogic
 from .setup import GraphSetup
 from .propagation import Propagator
@@ -66,10 +73,8 @@ 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()
@@ -94,12 +99,7 @@ class TradingAgentsGraph:
         self.deep_thinking_llm = deep_client.get_llm()
         self.quick_thinking_llm = quick_client.get_llm()
         
-        # Initialize memories
-        self.bull_memory = FinancialSituationMemory("bull_memory", self.config)
-        self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
-        self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
-        self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
-        self.portfolio_manager_memory = FinancialSituationMemory("portfolio_manager_memory", self.config)
+        self.memory_log = TradingMemoryLog(self.config)
 
         # Create tool nodes
         self.tool_nodes = self._create_tool_nodes()
@@ -113,16 +113,13 @@ class TradingAgentsGraph:
             self.quick_thinking_llm,
             self.deep_thinking_llm,
             self.tool_nodes,
-            self.bull_memory,
-            self.bear_memory,
-            self.trader_memory,
-            self.invest_judge_memory,
-            self.portfolio_manager_memory,
             self.conditional_logic,
             analyst_concurrency_limit=self.config.get("analyst_concurrency_limit", 1),
         )
 
-        self.propagator = Propagator()
+        self.propagator = Propagator(
+            max_recur_limit=self.config.get("max_recur_limit", 100),
+        )
         self.reflector = Reflector(self.quick_thinking_llm)
         self.signal_processor = SignalProcessor(self.quick_thinking_llm)
 
@@ -131,8 +128,10 @@ class TradingAgentsGraph:
         self.ticker = None
         self.log_states_dict = {}  # date to full state dict
 
-        # Set up the graph
-        self.graph = self.graph_setup.setup_graph(selected_analysts)
+        # Set up the graph: keep the workflow for recompilation with a checkpointer.
+        self.workflow = self.graph_setup.setup_graph(selected_analysts)
+        self.graph = self.workflow.compile()
+        self._checkpointer_ctx = None
 
     def _get_provider_kwargs(self) -> Dict[str, Any]:
         """Get provider-specific kwargs for LLM client creation."""
@@ -192,19 +191,133 @@ class TradingAgentsGraph:
             ),
         }
 
-    def propagate(self, company_name, trade_date):
-        """Run the trading agents graph for a company on a specific date."""
+    def _fetch_returns(
+        self, ticker: str, trade_date: str, holding_days: int = 5
+    ) -> Tuple[Optional[float], Optional[float], Optional[int]]:
+        """Fetch raw and alpha return for ticker over holding_days from trade_date.
 
+        Returns (raw_return, alpha_return, actual_holding_days) or
+        (None, None, None) if price data is unavailable (too recent, delisted,
+        or network error).
+        """
+        try:
+            start = datetime.strptime(trade_date, "%Y-%m-%d")
+            end = start + timedelta(days=holding_days + 7)  # buffer for weekends/holidays
+            end_str = end.strftime("%Y-%m-%d")
+
+            stock = yf.Ticker(ticker).history(start=trade_date, end=end_str)
+            spy = yf.Ticker("SPY").history(start=trade_date, end=end_str)
+
+            if len(stock) < 2 or len(spy) < 2:
+                return None, None, None
+
+            actual_days = min(holding_days, len(stock) - 1, len(spy) - 1)
+            raw = float(
+                (stock["Close"].iloc[actual_days] - stock["Close"].iloc[0])
+                / stock["Close"].iloc[0]
+            )
+            spy_ret = float(
+                (spy["Close"].iloc[actual_days] - spy["Close"].iloc[0])
+                / spy["Close"].iloc[0]
+            )
+            alpha = raw - spy_ret
+            return raw, alpha, actual_days
+        except Exception as e:
+            logger.warning(
+                "Could not resolve outcome for %s on %s (will retry next run): %s",
+                ticker, trade_date, e,
+            )
+            return None, None, None
+
+    def _resolve_pending_entries(self, ticker: str) -> None:
+        """Resolve pending log entries for ticker at the start of a new run.
+
+        Fetches returns for each same-ticker pending entry, generates reflections,
+        then writes all updates in a single atomic batch write to avoid redundant I/O.
+        Skips entries whose price data is not yet available (too recent or delisted).
+
+        Trade-off: only same-ticker entries are resolved per run.  Entries for
+        other tickers accumulate until that ticker is run again.
+        """
+        pending = [e for e in self.memory_log.get_pending_entries() if e["ticker"] == ticker]
+        if not pending:
+            return
+
+        updates = []
+        for entry in pending:
+            raw, alpha, days = self._fetch_returns(ticker, entry["date"])
+            if raw is None:
+                continue  # price not available yet — try again next run
+            reflection = self.reflector.reflect_on_final_decision(
+                final_decision=entry.get("decision", ""),
+                raw_return=raw,
+                alpha_return=alpha,
+            )
+            updates.append({
+                "ticker": ticker,
+                "trade_date": entry["date"],
+                "raw_return": raw,
+                "alpha_return": alpha,
+                "holding_days": days,
+                "reflection": reflection,
+            })
+
+        if updates:
+            self.memory_log.batch_update_with_outcomes(updates)
+
+    def propagate(self, company_name, trade_date):
+        """Run the trading agents graph for a company on a specific date.
+
+        When ``checkpoint_enabled`` is set in config, the graph is recompiled
+        with a per-ticker SqliteSaver so a crashed run can resume from the last
+        successful node on a subsequent invocation with the same ticker+date.
+        """
         self.ticker = company_name
 
-        # Initialize state
+        # Resolve any pending memory-log entries for this ticker before the pipeline runs.
+        self._resolve_pending_entries(company_name)
+
+        # Recompile with a checkpointer if the user opted in.
+        if self.config.get("checkpoint_enabled"):
+            self._checkpointer_ctx = get_checkpointer(
+                self.config["data_cache_dir"], company_name
+            )
+            saver = self._checkpointer_ctx.__enter__()
+            self.graph = self.workflow.compile(checkpointer=saver)
+
+            step = checkpoint_step(
+                self.config["data_cache_dir"], company_name, str(trade_date)
+            )
+            if step is not None:
+                logger.info(
+                    "Resuming from step %d for %s on %s", step, company_name, trade_date
+                )
+            else:
+                logger.info("Starting fresh for %s on %s", company_name, trade_date)
+
+        try:
+            return self._run_graph(company_name, trade_date)
+        finally:
+            if self._checkpointer_ctx is not None:
+                self._checkpointer_ctx.__exit__(None, None, None)
+                self._checkpointer_ctx = None
+                self.graph = self.workflow.compile()
+
+    def _run_graph(self, company_name, trade_date):
+        """Execute the graph and write the resulting state to disk and memory log."""
+        # Initialize state — inject memory log context for PM.
+        past_context = self.memory_log.get_past_context(company_name)
         init_agent_state = self.propagator.create_initial_state(
-            company_name, trade_date
+            company_name, trade_date, past_context=past_context
         )
         args = self.propagator.get_graph_args()
 
+        # Inject thread_id so same ticker+date resumes, different date starts fresh.
+        if self.config.get("checkpoint_enabled"):
+            tid = thread_id(company_name, str(trade_date))
+            args.setdefault("config", {}).setdefault("configurable", {})["thread_id"] = tid
+
         if self.debug:
-            # Debug mode with tracing
             trace = []
             for chunk in self.graph.stream(init_agent_state, **args):
                 if len(chunk["messages"]) == 0:
@@ -212,19 +325,33 @@ class TradingAgentsGraph:
                 else:
                     chunk["messages"][-1].pretty_print()
                     trace.append(chunk)
-
-            final_state = trace[-1]
+            # Streamed chunks are per-node deltas. Merge them so the returned
+            # state matches what graph.invoke() yields in the non-debug path.
+            final_state = {}
+            for chunk in trace:
+                final_state.update(chunk)
         else:
-            # Standard mode without tracing
             final_state = self.graph.invoke(init_agent_state, **args)
 
-        # Store current state for reflection
+        # Store current state for reflection.
         self.curr_state = final_state
 
-        # Log state
+        # Log state to disk.
         self._log_state(trade_date, final_state)
 
-        # Return decision and processed signal
+        # Store decision for deferred reflection on the next same-ticker run.
+        self.memory_log.store_decision(
+            ticker=company_name,
+            trade_date=trade_date,
+            final_trade_decision=final_state["final_trade_decision"],
+        )
+
+        # Clear checkpoint on successful completion to avoid stale state.
+        if self.config.get("checkpoint_enabled"):
+            clear_checkpoint(
+                self.config["data_cache_dir"], company_name, str(trade_date)
+            )
+
         return final_state, self.process_signal(final_state["final_trade_decision"])
 
     def _log_state(self, trade_date, final_state):
@@ -259,34 +386,15 @@ class TradingAgentsGraph:
             "final_trade_decision": final_state["final_trade_decision"],
         }
 
-        # Save to file
-        directory = Path(f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/")
+        # Save to file. Reject ticker values that would escape the
+        # results directory when joined as a path component.
+        safe_ticker = safe_ticker_component(self.ticker)
+        directory = Path(self.config["results_dir"]) / safe_ticker / "TradingAgentsStrategy_logs"
         directory.mkdir(parents=True, exist_ok=True)
 
-        with open(
-            f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/full_states_log_{trade_date}.json",
-            "w",
-            encoding="utf-8",
-        ) as f:
-            json.dump(self.log_states_dict, f, indent=4)
-
-    def reflect_and_remember(self, returns_losses):
-        """Reflect on decisions and update memory based on returns."""
-        self.reflector.reflect_bull_researcher(
-            self.curr_state, returns_losses, self.bull_memory
-        )
-        self.reflector.reflect_bear_researcher(
-            self.curr_state, returns_losses, self.bear_memory
-        )
-        self.reflector.reflect_trader(
-            self.curr_state, returns_losses, self.trader_memory
-        )
-        self.reflector.reflect_invest_judge(
-            self.curr_state, returns_losses, self.invest_judge_memory
-        )
-        self.reflector.reflect_portfolio_manager(
-            self.curr_state, returns_losses, self.portfolio_manager_memory
-        )
+        log_path = directory / f"full_states_log_{trade_date}.json"
+        with open(log_path, "w", encoding="utf-8") as f:
+            json.dump(self.log_states_dict[str(trade_date)], f, indent=4)
 
     def process_signal(self, full_signal):
         """Process a signal to extract the core decision."""