feat: replace per-agent BM25 memory with persistent append-only decision log

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tradingagents/graph/trading_graph.py

@@ -1,18 +1,23 @@
 # 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.agents.utils.agent_states import (
     AgentState,
     InvestDebateState,
@@ -92,12 +97,7 @@ 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()
@@ -111,11 +111,6 @@ 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,
         )
 
@@ -189,14 +184,90 @@ class TradingAgentsGraph:
             ),
         }
 
+    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.debug("_fetch_returns failed for %s@%s: %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."""
 
         self.ticker = company_name
 
-        # Initialize state
+        # Resolve any pending log entries for this ticker before the pipeline runs.
+        # This adds the outcome + reflection from the previous run at zero latency cost.
+        self._resolve_pending_entries(company_name)
+
+        # 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()
 
@@ -221,6 +292,13 @@ class TradingAgentsGraph:
         # Log state
         self._log_state(trade_date, final_state)
 
+        # Store decision for deferred reflection.
+        self.memory_log.store_decision(
+            ticker=company_name,
+            trade_date=trade_date,
+            final_trade_decision=final_state["final_trade_decision"],
+        )
+
         # Return decision and processed signal
         return final_state, self.process_signal(final_state["final_trade_decision"])
 
@@ -264,24 +342,6 @@ class TradingAgentsGraph:
         with open(log_path, "w", encoding="utf-8") as f:
             json.dump(self.log_states_dict[str(trade_date)], 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
-        )
-
     def process_signal(self, full_signal):
         """Process a signal to extract the core decision."""
         return self.signal_processor.process_signal(full_signal)