feat: replace per-agent BM25 memory with persistent decision log (#578, #563, #564, #579)

The previous per-agent BM25 memory was effectively dead code — its only
caller was a commented-out line in main.py. Replace it with a single
append-only markdown decision log driven by the propagate() lifecycle.

Lifecycle:
- store_decision() appends a pending entry at the end of every run
- _resolve_pending_entries() runs at the start of the next same-ticker
  run, fetches yfinance returns + alpha vs SPY, and writes one LLM
  reflection per resolved entry through an atomic temp-file rename
- Portfolio Manager consumes state["past_context"] (5 most recent
  same-ticker entries plus 3 cross-ticker reflection-only excerpts)

Storage at ~/.tradingagents/memory/trading_memory.md
(override: TRADINGAGENTS_MEMORY_LOG_PATH).

Tag schema:
- Pending:  [YYYY-MM-DD | TICKER | Rating | pending]
- Resolved: [YYYY-MM-DD | TICKER | Rating | +X.X% | +Y.Y% | Nd]

Removes rank-bm25 dependency and the legacy reflect_and_remember()
plumbing across reflection.py, trading_graph.py, and the agent factories.

49 new tests in tests/test_memory_log.py cover the storage, deferred
reflection, prompt injection, and legacy-removal paths. Full suite
(58 tests) passes in under 2 seconds without API keys.

tradingagents/agents/managers/portfolio_manager.py

@@ -1,7 +1,7 @@
 from tradingagents.agents.utils.agent_utils import build_instrument_context, get_language_instruction
 
 
-def create_portfolio_manager(llm, memory):
+def create_portfolio_manager(llm):
     def portfolio_manager_node(state) -> dict:
 
         instrument_context = build_instrument_context(state["company_of_interest"])
@@ -15,21 +15,14 @@ def create_portfolio_manager(llm, memory):
         research_plan = state["investment_plan"]
         trader_plan = state["trader_investment_plan"]
 
-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
-
-        past_memory_str = ""
-        for i, rec in enumerate(past_memories, 1):
-            past_memory_str += rec["recommendation"] + "\n\n"
-
+        past_context = state.get("past_context", "")
         lessons_line = (
-            f"- Lessons from past decisions: **{past_memory_str.strip()}**\n"
-            if past_memories
-            else ""
+            f"- Lessons from prior decisions and outcomes:\n{past_context}\n"
+            if past_context else ""
         )
         thesis_instruction = (
-            "3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate and past reflections."
-            if past_memories
+            "3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate and the lessons from prior decisions."
+            if past_context
             else "3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate."
         )
 
@@ -50,6 +43,7 @@ def create_portfolio_manager(llm, memory):
 - Research Manager's investment plan: **{research_plan}**
 - Trader's transaction proposal: **{trader_plan}**
 {lessons_line}
+
 **Required Output Structure:**
 1. **Rating**: State one of Buy / Overweight / Hold / Underweight / Sell.
 2. **Executive Summary**: A concise action plan covering entry strategy, position sizing, key risk levels, and time horizon.