feat: link decision outcomes to trades via decision_id (issue #92)

Add decision_id column to trades table, capture log_decision() return
value, and update original BUY decision outcome on SELL execution.

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

docs/requirements-log.md

@@ -64,3 +64,25 @@
 **참고:** Realtime 모드 전용. Daily 모드는 배치 효율성을 위해 정적 watchlist 사용.
 
 **이슈/PR:** #76, #77
+
+---
+
+## 2026-02-10
+
+### 코드 리뷰 시 플랜-구현 일치 검증 규칙
+
+**배경:**
+- 코드 리뷰 시 플랜(EnterPlanMode에서 승인된 계획)과 실제 구현이 일치하는지 확인하는 절차가 없었음
+- 플랜과 다른 구현이 리뷰 없이 통과될 위험
+
+**요구사항:**
+1. 모든 PR 리뷰에서 플랜-구현 일치 여부를 필수 체크
+2. 플랜에 없는 변경은 정당한 사유 필요
+3. 플랜 항목이 누락되면 PR 설명에 사유 기록
+4. 스코프가 플랜과 일치하는지 확인
+
+**구현 결과:**
+- `docs/workflow.md`에 Code Review Checklist 섹션 추가
+  - Plan Consistency (필수), Safety & Constraints, Quality, Workflow 4개 카테고리
+
+**이슈/PR:** #114

docs/workflow.md

@@ -74,3 +74,37 @@ task_tool(
 ```
 
 Use `run_in_background=True` for independent tasks that don't block subsequent work.
+
+## Code Review Checklist
+
+**CRITICAL: Every PR review MUST verify plan-implementation consistency.**
+
+Before approving any PR, the reviewer (human or agent) must check ALL of the following:
+
+### 1. Plan Consistency (MANDATORY)
+
+- [ ] **Implementation matches the approved plan** — Compare the actual code changes against the plan created during `EnterPlanMode`. Every item in the plan must be addressed.
+- [ ] **No unplanned changes** — If the implementation includes changes not in the plan, they must be explicitly justified.
+- [ ] **No plan items omitted** — If any planned item was skipped, the reason must be documented in the PR description.
+- [ ] **Scope matches** — The PR does not exceed or fall short of the planned scope.
+
+### 2. Safety & Constraints
+
+- [ ] `src/core/risk_manager.py` is unchanged (READ-ONLY)
+- [ ] Circuit breaker threshold not weakened (only stricter allowed)
+- [ ] Fat-finger protection (30% max order) still enforced
+- [ ] Confidence < 80 still forces HOLD
+- [ ] No hardcoded API keys or secrets
+
+### 3. Quality
+
+- [ ] All new/modified code has corresponding tests
+- [ ] Test coverage >= 80%
+- [ ] `ruff check src/ tests/` passes (no lint errors)
+- [ ] No `assert` statements removed from tests
+
+### 4. Workflow
+
+- [ ] PR references the Gitea issue number
+- [ ] Feature branch follows naming convention (`feature/issue-N-description`)
+- [ ] Commit messages are clear and descriptive

src/analysis/scanner.py

@@ -108,7 +108,7 @@ class MarketScanner:
             self.context_store.set_context(
                 ContextLayer.L7_REALTIME,
                 timeframe,
-                f"{market.code}_{stock_code}_volatility",
+                f"volatility_{market.code}_{stock_code}",
                 {
                     "price": metrics.current_price,
                     "atr": metrics.atr,
@@ -179,7 +179,7 @@ class MarketScanner:
         self.context_store.set_context(
             ContextLayer.L7_REALTIME,
             timeframe,
-            f"{market.code}_scan_result",
+            f"scan_result_{market.code}",
             {
                 "total_scanned": len(valid_metrics),
                 "top_movers": [m.stock_code for m in top_movers],

src/context/__init__.py

@@ -5,6 +5,7 @@ The context tree implements Pillar 2: hierarchical memory management across
 """
 
 from src.context.layer import ContextLayer
+from src.context.scheduler import ContextScheduler
 from src.context.store import ContextStore
 
-__all__ = ["ContextLayer", "ContextStore"]
+__all__ = ["ContextLayer", "ContextScheduler", "ContextStore"]

src/context/aggregator.py

@@ -18,16 +18,33 @@ class ContextAggregator:
         self.conn = conn
         self.store = ContextStore(conn)
 
-    def aggregate_daily_from_trades(self, date: str | None = None) -> None:
+    def aggregate_daily_from_trades(
+        self, date: str | None = None, market: str | None = None
+    ) -> None:
         """Aggregate L6 (daily) context from trades table.
 
         Args:
             date: Date in YYYY-MM-DD format. If None, uses today.
+            market: Market code filter (e.g., "KR", "US"). If None, aggregates all markets.
         """
         if date is None:
             date = datetime.now(UTC).date().isoformat()
 
-        # Calculate daily metrics from trades
+        if market is None:
+            cursor = self.conn.execute(
+                """
+                SELECT DISTINCT market
+                FROM trades
+                WHERE DATE(timestamp) = ?
+                """,
+                (date,),
+            )
+            markets = [row[0] for row in cursor.fetchall() if row[0]]
+        else:
+            markets = [market]
+
+        for market_code in markets:
+            # Calculate daily metrics from trades for the market
             cursor = self.conn.execute(
                 """
                 SELECT
@@ -41,29 +58,43 @@ class ContextAggregator:
                     SUM(CASE WHEN pnl > 0 THEN 1 ELSE 0 END) as wins,
                     SUM(CASE WHEN pnl < 0 THEN 1 ELSE 0 END) as losses
                 FROM trades
-            WHERE DATE(timestamp) = ?
+                WHERE DATE(timestamp) = ? AND market = ?
                 """,
-            (date,),
+                (date, market_code),
             )
             row = cursor.fetchone()
 
             if row and row[0] > 0:  # At least one trade
                 trade_count, buys, sells, holds, avg_conf, total_pnl, stocks, wins, losses = row
 
-            # Store daily metrics in L6
+                key_suffix = f"_{market_code}"
-            self.store.set_context(ContextLayer.L6_DAILY, date, "trade_count", trade_count)
+
-            self.store.set_context(ContextLayer.L6_DAILY, date, "buys", buys)
+                # Store daily metrics in L6 with market suffix
-            self.store.set_context(ContextLayer.L6_DAILY, date, "sells", sells)
-            self.store.set_context(ContextLayer.L6_DAILY, date, "holds", holds)
                 self.store.set_context(
-                ContextLayer.L6_DAILY, date, "avg_confidence", round(avg_conf, 2)
+                    ContextLayer.L6_DAILY, date, f"trade_count{key_suffix}", trade_count
+                )
+                self.store.set_context(ContextLayer.L6_DAILY, date, f"buys{key_suffix}", buys)
+                self.store.set_context(ContextLayer.L6_DAILY, date, f"sells{key_suffix}", sells)
+                self.store.set_context(ContextLayer.L6_DAILY, date, f"holds{key_suffix}", holds)
+                self.store.set_context(
+                    ContextLayer.L6_DAILY,
+                    date,
+                    f"avg_confidence{key_suffix}",
+                    round(avg_conf, 2),
                 )
                 self.store.set_context(
-                ContextLayer.L6_DAILY, date, "total_pnl", round(total_pnl, 2)
+                    ContextLayer.L6_DAILY,
+                    date,
+                    f"total_pnl{key_suffix}",
+                    round(total_pnl, 2),
+                )
+                self.store.set_context(
+                    ContextLayer.L6_DAILY, date, f"unique_stocks{key_suffix}", stocks
                 )
-            self.store.set_context(ContextLayer.L6_DAILY, date, "unique_stocks", stocks)
                 win_rate = round(wins / max(wins + losses, 1) * 100, 2)
-            self.store.set_context(ContextLayer.L6_DAILY, date, "win_rate", win_rate)
+                self.store.set_context(
+                    ContextLayer.L6_DAILY, date, f"win_rate{key_suffix}", win_rate
+                )
 
     def aggregate_weekly_from_daily(self, week: str | None = None) -> None:
         """Aggregate L5 (weekly) context from L6 (daily).
@@ -92,14 +123,25 @@ class ContextAggregator:
             daily_data[row[0]].append(json.loads(row[1]))
 
         if daily_data:
-            # Sum all PnL values
+            # Sum all PnL values (market-specific if suffixed)
             if "total_pnl" in daily_data:
                 total_pnl = sum(daily_data["total_pnl"])
                 self.store.set_context(
                     ContextLayer.L5_WEEKLY, week, "weekly_pnl", round(total_pnl, 2)
                 )
 
-            # Average all confidence values
+            for key, values in daily_data.items():
+                if key.startswith("total_pnl_"):
+                    market_code = key.split("total_pnl_", 1)[1]
+                    total_pnl = sum(values)
+                    self.store.set_context(
+                        ContextLayer.L5_WEEKLY,
+                        week,
+                        f"weekly_pnl_{market_code}",
+                        round(total_pnl, 2),
+                    )
+
+            # Average all confidence values (market-specific if suffixed)
             if "avg_confidence" in daily_data:
                 conf_values = daily_data["avg_confidence"]
                 avg_conf = sum(conf_values) / len(conf_values)
@@ -107,6 +149,17 @@ class ContextAggregator:
                     ContextLayer.L5_WEEKLY, week, "avg_confidence", round(avg_conf, 2)
                 )
 
+            for key, values in daily_data.items():
+                if key.startswith("avg_confidence_"):
+                    market_code = key.split("avg_confidence_", 1)[1]
+                    avg_conf = sum(values) / len(values)
+                    self.store.set_context(
+                        ContextLayer.L5_WEEKLY,
+                        week,
+                        f"avg_confidence_{market_code}",
+                        round(avg_conf, 2),
+                    )
+
     def aggregate_monthly_from_weekly(self, month: str | None = None) -> None:
         """Aggregate L4 (monthly) context from L5 (weekly).
 
@@ -135,8 +188,16 @@ class ContextAggregator:
 
         if weekly_data:
             # Sum all weekly PnL values
+            total_pnl_values: list[float] = []
             if "weekly_pnl" in weekly_data:
-                total_pnl = sum(weekly_data["weekly_pnl"])
+                total_pnl_values.extend(weekly_data["weekly_pnl"])
+
+            for key, values in weekly_data.items():
+                if key.startswith("weekly_pnl_"):
+                    total_pnl_values.extend(values)
+
+            if total_pnl_values:
+                total_pnl = sum(total_pnl_values)
                 self.store.set_context(
                     ContextLayer.L4_MONTHLY, month, "monthly_pnl", round(total_pnl, 2)
                 )

src/context/scheduler.py

@@ -0,0 +1,135 @@
+"""Context aggregation scheduler for periodic rollups and cleanup."""
+
+from __future__ import annotations
+
+import sqlite3
+from calendar import monthrange
+from dataclasses import dataclass
+from datetime import UTC, datetime
+
+from src.context.aggregator import ContextAggregator
+from src.context.store import ContextStore
+
+
+@dataclass(frozen=True)
+class ScheduleResult:
+    """Represents which scheduled tasks ran."""
+
+    weekly: bool = False
+    monthly: bool = False
+    quarterly: bool = False
+    annual: bool = False
+    legacy: bool = False
+    cleanup: bool = False
+
+
+class ContextScheduler:
+    """Run periodic context aggregations and cleanup when due."""
+
+    def __init__(
+        self,
+        conn: sqlite3.Connection | None = None,
+        aggregator: ContextAggregator | None = None,
+        store: ContextStore | None = None,
+    ) -> None:
+        if aggregator is None:
+            if conn is None:
+                raise ValueError("conn is required when aggregator is not provided")
+            aggregator = ContextAggregator(conn)
+        self.aggregator = aggregator
+
+        if store is None:
+            store = getattr(aggregator, "store", None)
+        if store is None:
+            if conn is None:
+                raise ValueError("conn is required when store is not provided")
+            store = ContextStore(conn)
+        self.store = store
+
+        self._last_run: dict[str, str] = {}
+
+    def run_if_due(self, now: datetime | None = None) -> ScheduleResult:
+        """Run scheduled aggregations if their schedule is due.
+
+        Args:
+            now: Current datetime (UTC). If None, uses current time.
+
+        Returns:
+            ScheduleResult indicating which tasks ran.
+        """
+        if now is None:
+            now = datetime.now(UTC)
+
+        today = now.date().isoformat()
+        result = ScheduleResult()
+
+        if self._should_run("cleanup", today):
+            self.store.cleanup_expired_contexts()
+            result = self._with(result, cleanup=True)
+
+        if self._is_sunday(now) and self._should_run("weekly", today):
+            week = now.strftime("%Y-W%V")
+            self.aggregator.aggregate_weekly_from_daily(week)
+            result = self._with(result, weekly=True)
+
+        if self._is_last_day_of_month(now) and self._should_run("monthly", today):
+            month = now.strftime("%Y-%m")
+            self.aggregator.aggregate_monthly_from_weekly(month)
+            result = self._with(result, monthly=True)
+
+        if self._is_last_day_of_quarter(now) and self._should_run("quarterly", today):
+            quarter = self._current_quarter(now)
+            self.aggregator.aggregate_quarterly_from_monthly(quarter)
+            result = self._with(result, quarterly=True)
+
+        if self._is_last_day_of_year(now) and self._should_run("annual", today):
+            year = str(now.year)
+            self.aggregator.aggregate_annual_from_quarterly(year)
+            result = self._with(result, annual=True)
+
+            # Legacy rollup runs after annual aggregation.
+            self.aggregator.aggregate_legacy_from_annual()
+            result = self._with(result, legacy=True)
+
+        return result
+
+    def _should_run(self, key: str, date_str: str) -> bool:
+        if self._last_run.get(key) == date_str:
+            return False
+        self._last_run[key] = date_str
+        return True
+
+    @staticmethod
+    def _is_sunday(now: datetime) -> bool:
+        return now.weekday() == 6
+
+    @staticmethod
+    def _is_last_day_of_month(now: datetime) -> bool:
+        last_day = monthrange(now.year, now.month)[1]
+        return now.day == last_day
+
+    @classmethod
+    def _is_last_day_of_quarter(cls, now: datetime) -> bool:
+        if now.month not in (3, 6, 9, 12):
+            return False
+        return cls._is_last_day_of_month(now)
+
+    @staticmethod
+    def _is_last_day_of_year(now: datetime) -> bool:
+        return now.month == 12 and now.day == 31
+
+    @staticmethod
+    def _current_quarter(now: datetime) -> str:
+        quarter = (now.month - 1) // 3 + 1
+        return f"{now.year}-Q{quarter}"
+
+    @staticmethod
+    def _with(result: ScheduleResult, **kwargs: bool) -> ScheduleResult:
+        return ScheduleResult(
+            weekly=kwargs.get("weekly", result.weekly),
+            monthly=kwargs.get("monthly", result.monthly),
+            quarterly=kwargs.get("quarterly", result.quarterly),
+            annual=kwargs.get("annual", result.annual),
+            legacy=kwargs.get("legacy", result.legacy),
+            cleanup=kwargs.get("cleanup", result.cleanup),
+        )

src/db.py

@@ -6,6 +6,7 @@ import json
 import sqlite3
 from datetime import UTC, datetime
 from pathlib import Path
+from typing import Any
 
 
 def init_db(db_path: str) -> sqlite3.Connection:
@@ -26,7 +27,8 @@ def init_db(db_path: str) -> sqlite3.Connection:
             price REAL,
             pnl REAL DEFAULT 0.0,
             market TEXT DEFAULT 'KR',
-            exchange_code TEXT DEFAULT 'KRX'
+            exchange_code TEXT DEFAULT 'KRX',
+            decision_id TEXT
         )
         """
     )
@@ -41,6 +43,8 @@ def init_db(db_path: str) -> sqlite3.Connection:
         conn.execute("ALTER TABLE trades ADD COLUMN exchange_code TEXT DEFAULT 'KRX'")
     if "selection_context" not in columns:
         conn.execute("ALTER TABLE trades ADD COLUMN selection_context TEXT")
+    if "decision_id" not in columns:
+        conn.execute("ALTER TABLE trades ADD COLUMN decision_id TEXT")
 
     # Context tree tables for multi-layered memory management
     conn.execute(
@@ -143,6 +147,7 @@ def log_trade(
     market: str = "KR",
     exchange_code: str = "KRX",
     selection_context: dict[str, any] | None = None,
+    decision_id: str | None = None,
 ) -> None:
     """Insert a trade record into the database.
 
@@ -166,9 +171,9 @@ def log_trade(
         """
         INSERT INTO trades (
             timestamp, stock_code, action, confidence, rationale,
-            quantity, price, pnl, market, exchange_code, selection_context
+            quantity, price, pnl, market, exchange_code, selection_context, decision_id
         )
-        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
         """,
         (
             datetime.now(UTC).isoformat(),
@@ -182,6 +187,30 @@ def log_trade(
             market,
             exchange_code,
             context_json,
+            decision_id,
         ),
     )
     conn.commit()
+
+
+def get_latest_buy_trade(
+    conn: sqlite3.Connection, stock_code: str, market: str
+) -> dict[str, Any] | None:
+    """Fetch the most recent BUY trade for a stock and market."""
+    cursor = conn.execute(
+        """
+        SELECT decision_id, price, quantity
+        FROM trades
+        WHERE stock_code = ?
+          AND market = ?
+          AND action = 'BUY'
+          AND decision_id IS NOT NULL
+        ORDER BY timestamp DESC
+        LIMIT 1
+        """,
+        (stock_code, market),
+    )
+    row = cursor.fetchone()
+    if not row:
+        return None
+    return {"decision_id": row[0], "price": row[1], "quantity": row[2]}

src/evolution/__init__.py

@@ -7,6 +7,7 @@ from src.evolution.performance_tracker import (
     PerformanceTracker,
     StrategyMetrics,
 )
+from src.evolution.scorecard import DailyScorecard
 
 __all__ = [
     "EvolutionOptimizer",
@@ -16,4 +17,5 @@ __all__ = [
     "PerformanceTracker",
     "PerformanceDashboard",
     "StrategyMetrics",
+    "DailyScorecard",
 ]

src/evolution/scorecard.py

@@ -0,0 +1,25 @@
+"""Daily scorecard model for end-of-day performance review."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class DailyScorecard:
+    """Structured daily performance snapshot for a single market."""
+
+    date: str
+    market: str
+    total_decisions: int
+    buys: int
+    sells: int
+    holds: int
+    total_pnl: float
+    win_rate: float
+    avg_confidence: float
+    scenario_match_rate: float
+    top_winners: list[str] = field(default_factory=list)
+    top_losers: list[str] = field(default_factory=list)
+    lessons: list[str] = field(default_factory=list)
+    cross_market_note: str = ""

src/main.py

@@ -13,7 +13,6 @@ import signal
 from datetime import UTC, datetime
 from typing import Any
 
-from src.analysis.scanner import MarketScanner
 from src.analysis.smart_scanner import ScanCandidate, SmartVolatilityScanner
 from src.analysis.volatility import VolatilityAnalyzer
 from src.brain.context_selector import ContextSelector
@@ -21,12 +20,13 @@ from src.brain.gemini_client import GeminiClient, TradeDecision
 from src.broker.kis_api import KISBroker
 from src.broker.overseas import OverseasBroker
 from src.config import Settings
+from src.context.aggregator import ContextAggregator
 from src.context.layer import ContextLayer
 from src.context.store import ContextStore
 from src.core.criticality import CriticalityAssessor
 from src.core.priority_queue import PriorityTaskQueue
 from src.core.risk_manager import CircuitBreakerTripped, FatFingerRejected, RiskManager
-from src.db import init_db, log_trade
+from src.db import get_latest_buy_trade, init_db, log_trade
 from src.logging.decision_logger import DecisionLogger
 from src.logging_config import setup_logging
 from src.markets.schedule import MarketInfo, get_next_market_open, get_open_markets
@@ -154,6 +154,38 @@ async def trading_cycle(
         market_data["rsi"] = candidate.rsi
         market_data["volume_ratio"] = candidate.volume_ratio
 
+    # 1.3. Record L7 real-time context (market-scoped keys)
+    timeframe = datetime.now(UTC).isoformat()
+    context_store.set_context(
+        ContextLayer.L7_REALTIME,
+        timeframe,
+        f"volatility_{market.code}_{stock_code}",
+        {
+            "momentum_score": 50.0,
+            "volume_surge": 1.0,
+            "price_change_1m": 0.0,
+        },
+    )
+    context_store.set_context(
+        ContextLayer.L7_REALTIME,
+        timeframe,
+        f"price_{market.code}_{stock_code}",
+        {"current_price": current_price},
+    )
+    if candidate:
+        context_store.set_context(
+            ContextLayer.L7_REALTIME,
+            timeframe,
+            f"rsi_{market.code}_{stock_code}",
+            {"rsi": candidate.rsi},
+        )
+        context_store.set_context(
+            ContextLayer.L7_REALTIME,
+            timeframe,
+            f"volume_ratio_{market.code}_{stock_code}",
+            {"volume_ratio": candidate.volume_ratio},
+        )
+
     # Build portfolio data for global rule evaluation
     portfolio_data = {
         "portfolio_pnl_pct": pnl_pct,
@@ -171,7 +203,7 @@ async def trading_cycle(
         volatility_data = context_store.get_context(
             ContextLayer.L7_REALTIME,
             latest_timeframe,
-            f"volatility_{stock_code}",
+            f"volatility_{market.code}_{stock_code}",
         )
         if volatility_data:
             volatility_score = volatility_data.get("momentum_score", 50.0)
@@ -247,7 +279,7 @@ async def trading_cycle(
         "pnl_pct": pnl_pct,
     }
 
-    decision_logger.log_decision(
+    decision_id = decision_logger.log_decision(
         stock_code=stock_code,
         market=market.code,
         exchange_code=market.exchange_code,
@@ -259,6 +291,9 @@ async def trading_cycle(
     )
 
     # 3. Execute if actionable
+    quantity = 0
+    trade_price = current_price
+    trade_pnl = 0.0
     if decision.action in ("BUY", "SELL"):
         # Determine order size (simplified: 1 lot)
         quantity = 1
@@ -314,6 +349,18 @@ async def trading_cycle(
         except Exception as exc:
             logger.warning("Telegram notification failed: %s", exc)
 
+        if decision.action == "SELL":
+            buy_trade = get_latest_buy_trade(db_conn, stock_code, market.code)
+            if buy_trade and buy_trade.get("price") is not None:
+                buy_price = float(buy_trade["price"])
+                buy_qty = int(buy_trade.get("quantity") or 1)
+                trade_pnl = (trade_price - buy_price) * buy_qty
+                decision_logger.update_outcome(
+                    decision_id=buy_trade["decision_id"],
+                    pnl=trade_pnl,
+                    accuracy=1 if trade_pnl > 0 else 0,
+                )
+
     # 6. Log trade with selection context
     selection_context = None
     if stock_code in market_candidates:
@@ -331,9 +378,13 @@ async def trading_cycle(
         action=decision.action,
         confidence=decision.confidence,
         rationale=decision.rationale,
+        quantity=quantity,
+        price=trade_price,
+        pnl=trade_pnl,
         market=market.code,
         exchange_code=market.exchange_code,
         selection_context=selection_context,
+        decision_id=decision_id,
     )
 
     # 7. Latency monitoring
@@ -568,7 +619,7 @@ async def run_daily_session(
                 "pnl_pct": pnl_pct,
             }
 
-            decision_logger.log_decision(
+            decision_id = decision_logger.log_decision(
                 stock_code=stock_code,
                 market=market.code,
                 exchange_code=market.exchange_code,
@@ -580,6 +631,9 @@ async def run_daily_session(
             )
 
             # Execute if actionable
+            quantity = 0
+            trade_price = stock_data["current_price"]
+            trade_pnl = 0.0
             if decision.action in ("BUY", "SELL"):
                 quantity = 1
                 order_amount = stock_data["current_price"] * quantity
@@ -652,6 +706,18 @@ async def run_daily_session(
                     )
                     continue
 
+                if decision.action == "SELL":
+                    buy_trade = get_latest_buy_trade(db_conn, stock_code, market.code)
+                    if buy_trade and buy_trade.get("price") is not None:
+                        buy_price = float(buy_trade["price"])
+                        buy_qty = int(buy_trade.get("quantity") or 1)
+                        trade_pnl = (trade_price - buy_price) * buy_qty
+                        decision_logger.update_outcome(
+                            decision_id=buy_trade["decision_id"],
+                            pnl=trade_pnl,
+                            accuracy=1 if trade_pnl > 0 else 0,
+                        )
+
             # Log trade
             log_trade(
                 conn=db_conn,
@@ -659,8 +725,12 @@ async def run_daily_session(
                 action=decision.action,
                 confidence=decision.confidence,
                 rationale=decision.rationale,
+                quantity=quantity,
+                price=trade_price,
+                pnl=trade_pnl,
                 market=market.code,
                 exchange_code=market.exchange_code,
+                decision_id=decision_id,
             )
 
     logger.info("Daily trading session completed")
@@ -675,6 +745,7 @@ async def run(settings: Settings) -> None:
     db_conn = init_db(settings.DB_PATH)
     decision_logger = DecisionLogger(db_conn)
     context_store = ContextStore(db_conn)
+    context_aggregator = ContextAggregator(db_conn)
 
     # V2 proactive strategy components
     context_selector = ContextSelector(context_store)
@@ -835,15 +906,6 @@ async def run(settings: Settings) -> None:
 
     # Initialize volatility hunter
     volatility_analyzer = VolatilityAnalyzer(min_volume_surge=2.0, min_price_change=1.0)
-    market_scanner = MarketScanner(
-        broker=broker,
-        overseas_broker=overseas_broker,
-        volatility_analyzer=volatility_analyzer,
-        context_store=context_store,
-        top_n=5,
-        max_concurrent_scans=1,  # Fully serialized to avoid EGW00201
-    )
-
     # Initialize smart scanner (Python-first, AI-last pipeline)
     smart_scanner = SmartVolatilityScanner(
         broker=broker,
@@ -968,6 +1030,13 @@ async def run(settings: Settings) -> None:
                                 market_info = MARKETS.get(market_code)
                                 if market_info:
                                     await telegram.notify_market_close(market_info.name, 0.0)
+                                    market_date = datetime.now(
+                                        market_info.timezone
+                                    ).date().isoformat()
+                                    context_aggregator.aggregate_daily_from_trades(
+                                        date=market_date,
+                                        market=market_code,
+                                    )
                             except Exception as exc:
                                 logger.warning("Market close notification failed: %s", exc)
                             _market_states[market_code] = False

tests/test_context.py

@@ -161,7 +161,7 @@ class TestContextAggregator:
         self, aggregator: ContextAggregator, db_conn: sqlite3.Connection
     ) -> None:
         """Test aggregating daily metrics from trades."""
-        date = "2026-02-04"
+        date = datetime.now(UTC).date().isoformat()
 
         # Create sample trades
         log_trade(db_conn, "005930", "BUY", 85, "Good signal", quantity=10, price=70000, pnl=500)
@@ -175,36 +175,44 @@ class TestContextAggregator:
         db_conn.commit()
 
         # Aggregate
-        aggregator.aggregate_daily_from_trades(date)
+        aggregator.aggregate_daily_from_trades(date, market="KR")
 
         # Verify L6 contexts
         store = aggregator.store
-        assert store.get_context(ContextLayer.L6_DAILY, date, "trade_count") == 3
+        assert store.get_context(ContextLayer.L6_DAILY, date, "trade_count_KR") == 3
-        assert store.get_context(ContextLayer.L6_DAILY, date, "buys") == 1
+        assert store.get_context(ContextLayer.L6_DAILY, date, "buys_KR") == 1
-        assert store.get_context(ContextLayer.L6_DAILY, date, "sells") == 1
+        assert store.get_context(ContextLayer.L6_DAILY, date, "sells_KR") == 1
-        assert store.get_context(ContextLayer.L6_DAILY, date, "holds") == 1
+        assert store.get_context(ContextLayer.L6_DAILY, date, "holds_KR") == 1
-        assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl") == 2000.0
+        assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl_KR") == 2000.0
-        assert store.get_context(ContextLayer.L6_DAILY, date, "unique_stocks") == 3
+        assert store.get_context(ContextLayer.L6_DAILY, date, "unique_stocks_KR") == 3
         # 2 wins, 0 losses
-        assert store.get_context(ContextLayer.L6_DAILY, date, "win_rate") == 100.0
+        assert store.get_context(ContextLayer.L6_DAILY, date, "win_rate_KR") == 100.0
 
     def test_aggregate_weekly_from_daily(self, aggregator: ContextAggregator) -> None:
         """Test aggregating weekly metrics from daily."""
         week = "2026-W06"
 
         # Set daily contexts
-        aggregator.store.set_context(ContextLayer.L6_DAILY, "2026-02-02", "total_pnl", 100.0)
+        aggregator.store.set_context(
-        aggregator.store.set_context(ContextLayer.L6_DAILY, "2026-02-03", "total_pnl", 200.0)
+            ContextLayer.L6_DAILY, "2026-02-02", "total_pnl_KR", 100.0
-        aggregator.store.set_context(ContextLayer.L6_DAILY, "2026-02-02", "avg_confidence", 80.0)
+        )
-        aggregator.store.set_context(ContextLayer.L6_DAILY, "2026-02-03", "avg_confidence", 85.0)
+        aggregator.store.set_context(
+            ContextLayer.L6_DAILY, "2026-02-03", "total_pnl_KR", 200.0
+        )
+        aggregator.store.set_context(
+            ContextLayer.L6_DAILY, "2026-02-02", "avg_confidence_KR", 80.0
+        )
+        aggregator.store.set_context(
+            ContextLayer.L6_DAILY, "2026-02-03", "avg_confidence_KR", 85.0
+        )
 
         # Aggregate
         aggregator.aggregate_weekly_from_daily(week)
 
         # Verify L5 contexts
         store = aggregator.store
-        weekly_pnl = store.get_context(ContextLayer.L5_WEEKLY, week, "weekly_pnl")
+        weekly_pnl = store.get_context(ContextLayer.L5_WEEKLY, week, "weekly_pnl_KR")
-        avg_conf = store.get_context(ContextLayer.L5_WEEKLY, week, "avg_confidence")
+        avg_conf = store.get_context(ContextLayer.L5_WEEKLY, week, "avg_confidence_KR")
 
         assert weekly_pnl == 300.0
         assert avg_conf == 82.5
@@ -214,9 +222,15 @@ class TestContextAggregator:
         month = "2026-02"
 
         # Set weekly contexts
-        aggregator.store.set_context(ContextLayer.L5_WEEKLY, "2026-W05", "weekly_pnl", 100.0)
+        aggregator.store.set_context(
-        aggregator.store.set_context(ContextLayer.L5_WEEKLY, "2026-W06", "weekly_pnl", 200.0)
+            ContextLayer.L5_WEEKLY, "2026-W05", "weekly_pnl_KR", 100.0
-        aggregator.store.set_context(ContextLayer.L5_WEEKLY, "2026-W07", "weekly_pnl", 150.0)
+        )
+        aggregator.store.set_context(
+            ContextLayer.L5_WEEKLY, "2026-W06", "weekly_pnl_KR", 200.0
+        )
+        aggregator.store.set_context(
+            ContextLayer.L5_WEEKLY, "2026-W07", "weekly_pnl_KR", 150.0
+        )
 
         # Aggregate
         aggregator.aggregate_monthly_from_weekly(month)
@@ -285,7 +299,7 @@ class TestContextAggregator:
         self, aggregator: ContextAggregator, db_conn: sqlite3.Connection
     ) -> None:
         """Test running all aggregations from L7 to L1."""
-        date = "2026-02-04"
+        date = datetime.now(UTC).date().isoformat()
 
         # Create sample trades
         log_trade(db_conn, "005930", "BUY", 85, "Good signal", quantity=10, price=70000, pnl=1000)
@@ -299,12 +313,12 @@ class TestContextAggregator:
 
         # Verify data exists in each layer
         store = aggregator.store
-        assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl") == 1000.0
+        assert store.get_context(ContextLayer.L6_DAILY, date, "total_pnl_KR") == 1000.0
         from datetime import date as date_cls
         trade_date = date_cls.fromisoformat(date)
         iso_year, iso_week, _ = trade_date.isocalendar()
         trade_week = f"{iso_year}-W{iso_week:02d}"
-        assert store.get_context(ContextLayer.L5_WEEKLY, trade_week, "weekly_pnl") is not None
+        assert store.get_context(ContextLayer.L5_WEEKLY, trade_week, "weekly_pnl_KR") is not None
         trade_month = f"{trade_date.year}-{trade_date.month:02d}"
         trade_quarter = f"{trade_date.year}-Q{(trade_date.month - 1) // 3 + 1}"
         trade_year = str(trade_date.year)

tests/test_context_scheduler.py

@@ -0,0 +1,104 @@
+"""Tests for ContextScheduler."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import UTC, datetime
+
+from src.context.scheduler import ContextScheduler
+
+
+@dataclass
+class StubAggregator:
+    """Stub aggregator that records calls."""
+
+    weekly_calls: list[str]
+    monthly_calls: list[str]
+    quarterly_calls: list[str]
+    annual_calls: list[str]
+    legacy_calls: int
+
+    def aggregate_weekly_from_daily(self, week: str) -> None:
+        self.weekly_calls.append(week)
+
+    def aggregate_monthly_from_weekly(self, month: str) -> None:
+        self.monthly_calls.append(month)
+
+    def aggregate_quarterly_from_monthly(self, quarter: str) -> None:
+        self.quarterly_calls.append(quarter)
+
+    def aggregate_annual_from_quarterly(self, year: str) -> None:
+        self.annual_calls.append(year)
+
+    def aggregate_legacy_from_annual(self) -> None:
+        self.legacy_calls += 1
+
+
+@dataclass
+class StubStore:
+    """Stub store that records cleanup calls."""
+
+    cleanup_calls: int = 0
+
+    def cleanup_expired_contexts(self) -> None:
+        self.cleanup_calls += 1
+
+
+def make_scheduler() -> tuple[ContextScheduler, StubAggregator, StubStore]:
+    aggregator = StubAggregator([], [], [], [], 0)
+    store = StubStore()
+    scheduler = ContextScheduler(aggregator=aggregator, store=store)
+    return scheduler, aggregator, store
+
+
+def test_run_if_due_weekly() -> None:
+    scheduler, aggregator, store = make_scheduler()
+    now = datetime(2026, 2, 8, 10, 0, tzinfo=UTC)  # Sunday
+
+    result = scheduler.run_if_due(now)
+
+    assert result.weekly is True
+    assert aggregator.weekly_calls == ["2026-W06"]
+    assert store.cleanup_calls == 1
+
+
+def test_run_if_due_monthly() -> None:
+    scheduler, aggregator, _store = make_scheduler()
+    now = datetime(2026, 2, 28, 12, 0, tzinfo=UTC)  # Last day of month
+
+    result = scheduler.run_if_due(now)
+
+    assert result.monthly is True
+    assert aggregator.monthly_calls == ["2026-02"]
+
+
+def test_run_if_due_quarterly() -> None:
+    scheduler, aggregator, _store = make_scheduler()
+    now = datetime(2026, 3, 31, 12, 0, tzinfo=UTC)  # Last day of Q1
+
+    result = scheduler.run_if_due(now)
+
+    assert result.quarterly is True
+    assert aggregator.quarterly_calls == ["2026-Q1"]
+
+
+def test_run_if_due_annual_and_legacy() -> None:
+    scheduler, aggregator, _store = make_scheduler()
+    now = datetime(2026, 12, 31, 12, 0, tzinfo=UTC)
+
+    result = scheduler.run_if_due(now)
+
+    assert result.annual is True
+    assert result.legacy is True
+    assert aggregator.annual_calls == ["2026"]
+    assert aggregator.legacy_calls == 1
+
+
+def test_cleanup_runs_once_per_day() -> None:
+    scheduler, _aggregator, store = make_scheduler()
+    now = datetime(2026, 2, 9, 9, 0, tzinfo=UTC)
+
+    scheduler.run_if_due(now)
+    scheduler.run_if_due(now)
+
+    assert store.cleanup_calls == 1

tests/test_main.py

@@ -1,11 +1,14 @@
 """Tests for main trading loop integration."""
 
 from datetime import date
-from unittest.mock import AsyncMock, MagicMock, patch
+from unittest.mock import ANY, AsyncMock, MagicMock, patch
 
 import pytest
 
+from src.context.layer import ContextLayer
 from src.core.risk_manager import CircuitBreakerTripped, FatFingerRejected
+from src.db import init_db, log_trade
+from src.logging.decision_logger import DecisionLogger
 from src.main import safe_float, trading_cycle
 from src.strategy.models import (
     DayPlaybook,
@@ -43,6 +46,17 @@ def _make_hold_match(stock_code: str = "005930") -> ScenarioMatch:
     )
 
 
+def _make_sell_match(stock_code: str = "005930") -> ScenarioMatch:
+    """Create a ScenarioMatch that returns SELL."""
+    return ScenarioMatch(
+        stock_code=stock_code,
+        matched_scenario=None,
+        action=ScenarioAction.SELL,
+        confidence=90,
+        rationale="Test sell",
+    )
+
+
 class TestSafeFloat:
     """Test safe_float() helper function."""
 
@@ -810,6 +824,69 @@ class TestScenarioEngineIntegration:
         assert "portfolio_pnl_pct" in portfolio_data
         assert "total_cash" in portfolio_data
 
+    @pytest.mark.asyncio
+    async def test_trading_cycle_sets_l7_context_keys(
+        self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
+    ) -> None:
+        """Test L7 context is written with market-scoped keys."""
+        from src.analysis.smart_scanner import ScanCandidate
+
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=_make_hold_match())
+        playbook = _make_playbook()
+        context_store = MagicMock(get_latest_timeframe=MagicMock(return_value=None))
+
+        candidate = ScanCandidate(
+            stock_code="005930", name="Samsung", price=50000,
+            volume=1000000, volume_ratio=3.5, rsi=25.0,
+            signal="oversold", score=85.0,
+        )
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=playbook,
+                risk=MagicMock(),
+                db_conn=MagicMock(),
+                decision_logger=MagicMock(),
+                context_store=context_store,
+                criticality_assessor=MagicMock(
+                    assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
+                    get_timeout=MagicMock(return_value=5.0),
+                ),
+                telegram=mock_telegram,
+                market=mock_market,
+                stock_code="005930",
+                scan_candidates={"KR": {"005930": candidate}},
+            )
+
+        context_store.set_context.assert_any_call(
+            ContextLayer.L7_REALTIME,
+            ANY,
+            "volatility_KR_005930",
+            {"momentum_score": 50.0, "volume_surge": 1.0, "price_change_1m": 0.0},
+        )
+        context_store.set_context.assert_any_call(
+            ContextLayer.L7_REALTIME,
+            ANY,
+            "price_KR_005930",
+            {"current_price": 50000.0},
+        )
+        context_store.set_context.assert_any_call(
+            ContextLayer.L7_REALTIME,
+            ANY,
+            "rsi_KR_005930",
+            {"rsi": 25.0},
+        )
+        context_store.set_context.assert_any_call(
+            ContextLayer.L7_REALTIME,
+            ANY,
+            "volume_ratio_KR_005930",
+            {"volume_ratio": 3.5},
+        )
+
     @pytest.mark.asyncio
     async def test_scan_candidates_market_scoped(
         self, mock_broker: MagicMock, mock_market: MagicMock, mock_telegram: MagicMock,
@@ -1049,3 +1126,96 @@ class TestScenarioEngineIntegration:
         # REDUCE_ALL is not BUY or SELL — no order sent
         mock_broker.send_order.assert_not_called()
         mock_telegram.notify_trade_execution.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_sell_updates_original_buy_decision_outcome() -> None:
+    """SELL should update the original BUY decision outcome in decision_logs."""
+    db_conn = init_db(":memory:")
+    decision_logger = DecisionLogger(db_conn)
+
+    buy_decision_id = decision_logger.log_decision(
+        stock_code="005930",
+        market="KR",
+        exchange_code="KRX",
+        action="BUY",
+        confidence=85,
+        rationale="Initial buy",
+        context_snapshot={},
+        input_data={},
+    )
+    log_trade(
+        conn=db_conn,
+        stock_code="005930",
+        action="BUY",
+        confidence=85,
+        rationale="Initial buy",
+        quantity=1,
+        price=100.0,
+        pnl=0.0,
+        market="KR",
+        exchange_code="KRX",
+        decision_id=buy_decision_id,
+    )
+
+    broker = MagicMock()
+    broker.get_orderbook = AsyncMock(
+        return_value={"output1": {"stck_prpr": "120", "frgn_ntby_qty": "0"}}
+    )
+    broker.get_balance = AsyncMock(
+        return_value={
+            "output2": [
+                {
+                    "tot_evlu_amt": "100000",
+                    "dnca_tot_amt": "10000",
+                    "pchs_amt_smtl_amt": "90000",
+                }
+            ]
+        }
+    )
+    broker.send_order = AsyncMock(return_value={"msg1": "OK"})
+
+    overseas_broker = MagicMock()
+    engine = MagicMock(spec=ScenarioEngine)
+    engine.evaluate = MagicMock(return_value=_make_sell_match())
+    risk = MagicMock()
+    context_store = MagicMock(
+        get_latest_timeframe=MagicMock(return_value=None),
+        set_context=MagicMock(),
+    )
+    criticality_assessor = MagicMock(
+        assess_market_conditions=MagicMock(return_value=MagicMock(value="NORMAL")),
+        get_timeout=MagicMock(return_value=5.0),
+    )
+    telegram = MagicMock()
+    telegram.notify_trade_execution = AsyncMock()
+    telegram.notify_fat_finger = AsyncMock()
+    telegram.notify_circuit_breaker = AsyncMock()
+    telegram.notify_scenario_matched = AsyncMock()
+
+    market = MagicMock()
+    market.name = "Korea"
+    market.code = "KR"
+    market.exchange_code = "KRX"
+    market.is_domestic = True
+
+    await trading_cycle(
+        broker=broker,
+        overseas_broker=overseas_broker,
+        scenario_engine=engine,
+        playbook=_make_playbook(),
+        risk=risk,
+        db_conn=db_conn,
+        decision_logger=decision_logger,
+        context_store=context_store,
+        criticality_assessor=criticality_assessor,
+        telegram=telegram,
+        market=market,
+        stock_code="005930",
+        scan_candidates={},
+    )
+
+    updated_buy = decision_logger.get_decision_by_id(buy_decision_id)
+    assert updated_buy is not None
+    assert updated_buy.outcome_pnl == 20.0
+    assert updated_buy.outcome_accuracy == 1

tests/test_scorecard.py

@@ -0,0 +1,81 @@
+"""Tests for DailyScorecard model."""
+
+from __future__ import annotations
+
+from src.evolution.scorecard import DailyScorecard
+
+
+def test_scorecard_initialization() -> None:
+    scorecard = DailyScorecard(
+        date="2026-02-08",
+        market="KR",
+        total_decisions=10,
+        buys=3,
+        sells=2,
+        holds=5,
+        total_pnl=1234.5,
+        win_rate=60.0,
+        avg_confidence=78.5,
+        scenario_match_rate=70.0,
+        top_winners=["005930", "000660"],
+        top_losers=["035420"],
+        lessons=["Avoid chasing breakouts"],
+        cross_market_note="US volatility spillover",
+    )
+
+    assert scorecard.market == "KR"
+    assert scorecard.total_decisions == 10
+    assert scorecard.total_pnl == 1234.5
+    assert scorecard.top_winners == ["005930", "000660"]
+    assert scorecard.lessons == ["Avoid chasing breakouts"]
+    assert scorecard.cross_market_note == "US volatility spillover"
+
+
+def test_scorecard_defaults() -> None:
+    scorecard = DailyScorecard(
+        date="2026-02-08",
+        market="US",
+        total_decisions=0,
+        buys=0,
+        sells=0,
+        holds=0,
+        total_pnl=0.0,
+        win_rate=0.0,
+        avg_confidence=0.0,
+        scenario_match_rate=0.0,
+    )
+
+    assert scorecard.top_winners == []
+    assert scorecard.top_losers == []
+    assert scorecard.lessons == []
+    assert scorecard.cross_market_note == ""
+
+
+def test_scorecard_list_isolation() -> None:
+    a = DailyScorecard(
+        date="2026-02-08",
+        market="KR",
+        total_decisions=1,
+        buys=1,
+        sells=0,
+        holds=0,
+        total_pnl=10.0,
+        win_rate=100.0,
+        avg_confidence=90.0,
+        scenario_match_rate=100.0,
+    )
+    b = DailyScorecard(
+        date="2026-02-08",
+        market="US",
+        total_decisions=1,
+        buys=0,
+        sells=1,
+        holds=0,
+        total_pnl=-5.0,
+        win_rate=0.0,
+        avg_confidence=60.0,
+        scenario_match_rate=50.0,
+    )
+
+    a.top_winners.append("005930")
+    assert b.top_winners == []

tests/test_volatility.py

@@ -412,7 +412,7 @@ class TestMarketScanner:
         scan_result = context_store.get_context(
             ContextLayer.L7_REALTIME,
             latest_timeframe,
-            "KR_scan_result",
+            "scan_result_KR",
         )
         assert scan_result is not None
         assert scan_result["total_scanned"] == 3