fix: uvicorn 미설치 시 dashboard 실패를 동기적으로 감지하여 오해 없는 로그 출력 (#178)

스레드 시작 전에 uvicorn import를 검증하도록 _start_dashboard_server 수정.
uvicorn 미설치 시 "started" 로그 없이 즉시 WARNING 출력 후 None 반환.

- 사전 import 검증으로 "started" → "failed" 오해 소지 있는 로그 쌍 제거
- uvicorn 미설치 시 명확한 경고 메시지 출력
- test_start_dashboard_server_returns_none_when_uvicorn_missing 테스트 추가

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

src/main.py

@@ -42,7 +42,7 @@ 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
 from src.notifications.telegram_client import NotificationFilter, TelegramClient, TelegramCommandHandler
-from src.strategy.models import DayPlaybook
+from src.strategy.models import DayPlaybook, MarketOutlook
 from src.strategy.playbook_store import PlaybookStore
 from src.strategy.pre_market_planner import PreMarketPlanner
 from src.strategy.scenario_engine import ScenarioEngine
@@ -106,6 +106,82 @@ def _extract_symbol_from_holding(item: dict[str, Any]) -> str:
     return ""
 
 
+def _extract_held_codes_from_balance(
+    balance_data: dict[str, Any],
+    *,
+    is_domestic: bool,
+) -> list[str]:
+    """Return stock codes with a positive orderable quantity from a balance response.
+
+    Uses the broker's live output1 as the source of truth so that partial fills
+    and manual external trades are always reflected correctly.
+    """
+    output1 = balance_data.get("output1", [])
+    if isinstance(output1, dict):
+        output1 = [output1]
+    if not isinstance(output1, list):
+        return []
+
+    codes: list[str] = []
+    for holding in output1:
+        if not isinstance(holding, dict):
+            continue
+        code_key = "pdno" if is_domestic else "ovrs_pdno"
+        code = str(holding.get(code_key, "")).strip().upper()
+        if not code:
+            continue
+        if is_domestic:
+            qty = int(holding.get("ord_psbl_qty") or holding.get("hldg_qty") or 0)
+        else:
+            qty = int(holding.get("ovrs_cblc_qty") or holding.get("hldg_qty") or 0)
+        if qty > 0:
+            codes.append(code)
+    return codes
+
+
+def _extract_held_qty_from_balance(
+    balance_data: dict[str, Any],
+    stock_code: str,
+    *,
+    is_domestic: bool,
+) -> int:
+    """Extract the broker-confirmed orderable quantity for a stock.
+
+    Uses the broker's live balance response (output1) as the source of truth
+    rather than the local DB, because DB records reflect order quantity which
+    may differ from actual fill quantity due to partial fills.
+
+    Domestic fields (VTTC8434R output1):
+        pdno          — 종목코드
+        ord_psbl_qty  — 주문가능수량 (preferred: excludes unsettled)
+        hldg_qty      — 보유수량 (fallback)
+
+    Overseas fields (output1):
+        ovrs_pdno     — 종목코드
+        ovrs_cblc_qty — 해외잔고수량 (preferred)
+        hldg_qty      — 보유수량 (fallback)
+    """
+    output1 = balance_data.get("output1", [])
+    if isinstance(output1, dict):
+        output1 = [output1]
+    if not isinstance(output1, list):
+        return 0
+
+    for holding in output1:
+        if not isinstance(holding, dict):
+            continue
+        code_key = "pdno" if is_domestic else "ovrs_pdno"
+        held_code = str(holding.get(code_key, "")).strip().upper()
+        if held_code != stock_code.strip().upper():
+            continue
+        if is_domestic:
+            qty = int(holding.get("ord_psbl_qty") or holding.get("hldg_qty") or 0)
+        else:
+            qty = int(holding.get("ovrs_cblc_qty") or holding.get("hldg_qty") or 0)
+        return qty
+    return 0
+
+
 def _determine_order_quantity(
     *,
     action: str,
@@ -113,16 +189,40 @@ def _determine_order_quantity(
     total_cash: float,
     candidate: ScanCandidate | None,
     settings: Settings | None,
+    broker_held_qty: int = 0,
+    playbook_allocation_pct: float | None = None,
+    scenario_confidence: int = 80,
 ) -> int:
-    """Determine order quantity using volatility-aware position sizing."""
+    """Determine order quantity using volatility-aware position sizing.
-    if action != "BUY":
+
-        return 1
+    Priority:
+    1. playbook_allocation_pct (AI-specified) scaled by scenario_confidence
+    2. Fallback: volatility-score-based allocation from scanner candidate
+    """
+    if action == "SELL":
+        return broker_held_qty
     if current_price <= 0 or total_cash <= 0:
         return 0
 
     if settings is None or not settings.POSITION_SIZING_ENABLED:
         return 1
 
+    # Use AI-specified allocation_pct if available
+    if playbook_allocation_pct is not None:
+        # Confidence scaling: confidence 80 → 1.0x, confidence 95 → 1.19x
+        confidence_scale = scenario_confidence / 80.0
+        effective_pct = min(
+            settings.POSITION_MAX_ALLOCATION_PCT,
+            max(
+                settings.POSITION_MIN_ALLOCATION_PCT,
+                playbook_allocation_pct * confidence_scale,
+            ),
+        )
+        budget = total_cash * (effective_pct / 100.0)
+        quantity = int(budget // current_price)
+        return max(0, quantity)
+
+    # Fallback: volatility-score-based allocation
     target_score = max(1.0, settings.POSITION_VOLATILITY_TARGET_SCORE)
     observed_score = candidate.score if candidate else target_score
     observed_score = max(1.0, min(100.0, observed_score))
@@ -380,6 +480,34 @@ async def trading_cycle(
     )
     stock_playbook = playbook.get_stock_playbook(stock_code)
 
+    # 2.1. Apply market_outlook-based BUY confidence threshold
+    if decision.action == "BUY":
+        base_threshold = (settings.CONFIDENCE_THRESHOLD if settings else 80)
+        outlook = playbook.market_outlook
+        if outlook == MarketOutlook.BEARISH:
+            min_confidence = 90
+        elif outlook == MarketOutlook.BULLISH:
+            min_confidence = 75
+        else:
+            min_confidence = base_threshold
+        if match.confidence < min_confidence:
+            logger.info(
+                "BUY suppressed for %s (%s): confidence %d < %d (market_outlook=%s)",
+                stock_code,
+                market.name,
+                match.confidence,
+                min_confidence,
+                outlook.value,
+            )
+            decision = TradeDecision(
+                action="HOLD",
+                confidence=match.confidence,
+                rationale=(
+                    f"BUY confidence {match.confidence} < {min_confidence} "
+                    f"(market_outlook={outlook.value})"
+                ),
+            )
+
     if decision.action == "HOLD":
         open_position = get_open_position(db_conn, stock_code, market.code)
         if open_position:
@@ -484,12 +612,23 @@ async def trading_cycle(
     trade_price = current_price
     trade_pnl = 0.0
     if decision.action in ("BUY", "SELL"):
+        broker_held_qty = (
+            _extract_held_qty_from_balance(
+                balance_data, stock_code, is_domestic=market.is_domestic
+            )
+            if decision.action == "SELL"
+            else 0
+        )
+        matched_scenario = match.matched_scenario
         quantity = _determine_order_quantity(
             action=decision.action,
             current_price=current_price,
             total_cash=total_cash,
             candidate=candidate,
             settings=settings,
+            broker_held_qty=broker_held_qty,
+            playbook_allocation_pct=matched_scenario.allocation_pct if matched_scenario else None,
+            scenario_confidence=match.confidence,
         )
         if quantity <= 0:
             logger.info(
@@ -909,12 +1048,20 @@ async def run_daily_session(
             trade_pnl = 0.0
             order_succeeded = True
             if decision.action in ("BUY", "SELL"):
+                daily_broker_held_qty = (
+                    _extract_held_qty_from_balance(
+                        balance_data, stock_code, is_domestic=market.is_domestic
+                    )
+                    if decision.action == "SELL"
+                    else 0
+                )
                 quantity = _determine_order_quantity(
                     action=decision.action,
                     current_price=stock_data["current_price"],
                     total_cash=total_cash,
                     candidate=candidate_map.get(stock_code),
                     settings=settings,
+                    broker_held_qty=daily_broker_held_qty,
                 )
                 if quantity <= 0:
                     logger.info(
@@ -1153,10 +1300,18 @@ def _start_dashboard_server(settings: Settings) -> threading.Thread | None:
     if not settings.DASHBOARD_ENABLED:
         return None
 
+    # Validate dependencies before spawning the thread so startup failures are
+    # reported synchronously (avoids the misleading "started" → "failed" log pair).
+    try:
+        import uvicorn  # noqa: F401
+        from src.dashboard import create_dashboard_app  # noqa: F401
+    except ImportError as exc:
+        logger.warning("Dashboard server unavailable (missing dependency): %s", exc)
+        return None
+
     def _serve() -> None:
         try:
             import uvicorn
-
             from src.dashboard import create_dashboard_app
 
             app = create_dashboard_app(settings.DB_PATH)
@@ -1167,7 +1322,7 @@ def _start_dashboard_server(settings: Settings) -> threading.Thread | None:
                 log_level="info",
             )
         except Exception as exc:
-            logger.warning("Dashboard server failed to start: %s", exc)
+            logger.warning("Dashboard server stopped unexpectedly: %s", exc)
 
     thread = threading.Thread(
         target=_serve,
@@ -1881,8 +2036,38 @@ async def run(settings: Settings) -> None:
                         except Exception as exc:
                             logger.error("Smart Scanner failed for %s: %s", market.name, exc)
 
-                    # Get active stocks from scanner (dynamic, no static fallback)
+                    # Get active stocks from scanner (dynamic, no static fallback).
-                    stock_codes = active_stocks.get(market.code, [])
+                    # Also include currently-held positions so stop-loss /
+                    # take-profit can fire even when a holding drops off the
+                    # scanner.  Broker balance is the source of truth here —
+                    # unlike the local DB it reflects actual fills and any
+                    # manual trades done outside the bot.
+                    scanner_codes = active_stocks.get(market.code, [])
+                    try:
+                        if market.is_domestic:
+                            held_balance = await broker.get_balance()
+                        else:
+                            held_balance = await overseas_broker.get_overseas_balance(
+                                market.exchange_code
+                            )
+                        held_codes = _extract_held_codes_from_balance(
+                            held_balance, is_domestic=market.is_domestic
+                        )
+                    except Exception as exc:
+                        logger.warning(
+                            "Failed to fetch holdings for %s: %s — skipping holdings merge",
+                            market.name, exc,
+                        )
+                        held_codes = []
+
+                    stock_codes = list(dict.fromkeys(scanner_codes + held_codes))
+                    extra_held = [c for c in held_codes if c not in set(scanner_codes)]
+                    if extra_held:
+                        logger.info(
+                            "Holdings added to loop for %s (not in scanner): %s",
+                            market.name, extra_held,
+                        )
+
                     if not stock_codes:
                         logger.debug("No active stocks for market %s", market.code)
                         continue

src/strategy/models.py

@@ -46,6 +46,18 @@ class StockCondition(BaseModel):
 
     The ScenarioEngine evaluates all non-None fields as AND conditions.
     A condition matches only if ALL specified fields are satisfied.
+
+    Technical indicator fields:
+        rsi_below / rsi_above — RSI threshold
+        volume_ratio_above / volume_ratio_below — volume vs previous day
+        price_above / price_below — absolute price level
+        price_change_pct_above / price_change_pct_below — intraday % change
+
+    Position-aware fields (require market_data enrichment from open position):
+        unrealized_pnl_pct_above — matches if unrealized P&L > threshold (e.g. 3.0 → +3%)
+        unrealized_pnl_pct_below — matches if unrealized P&L < threshold (e.g. -2.0 → -2%)
+        holding_days_above — matches if position held for more than N days
+        holding_days_below — matches if position held for fewer than N days
     """
 
     rsi_below: float | None = None
@@ -56,6 +68,10 @@ class StockCondition(BaseModel):
     price_below: float | None = None
     price_change_pct_above: float | None = None
     price_change_pct_below: float | None = None
+    unrealized_pnl_pct_above: float | None = None
+    unrealized_pnl_pct_below: float | None = None
+    holding_days_above: int | None = None
+    holding_days_below: int | None = None
 
     def has_any_condition(self) -> bool:
         """Check if at least one condition field is set."""
@@ -70,6 +86,10 @@ class StockCondition(BaseModel):
                 self.price_below,
                 self.price_change_pct_above,
                 self.price_change_pct_below,
+                self.unrealized_pnl_pct_above,
+                self.unrealized_pnl_pct_below,
+                self.holding_days_above,
+                self.holding_days_below,
             )
         )
 

src/strategy/pre_market_planner.py

@@ -75,6 +75,7 @@ class PreMarketPlanner:
         market: str,
         candidates: list[ScanCandidate],
         today: date | None = None,
+        current_holdings: list[dict] | None = None,
     ) -> DayPlaybook:
         """Generate a DayPlaybook for a market using Gemini.
 
@@ -82,6 +83,10 @@ class PreMarketPlanner:
             market: Market code ("KR" or "US")
             candidates: Stock candidates from SmartVolatilityScanner
             today: Override date (defaults to date.today()). Use market-local date.
+            current_holdings: Currently held positions with entry_price and unrealized_pnl_pct.
+                Each dict: {"stock_code": str, "name": str, "qty": int,
+                            "entry_price": float, "unrealized_pnl_pct": float,
+                            "holding_days": int}
 
         Returns:
             DayPlaybook with scenarios. Empty/defensive if no candidates or failure.
@@ -106,6 +111,7 @@ class PreMarketPlanner:
                 context_data,
                 self_market_scorecard,
                 cross_market,
+                current_holdings=current_holdings,
             )
 
             # 3. Call Gemini
@@ -118,7 +124,8 @@ class PreMarketPlanner:
 
             # 4. Parse response
             playbook = self._parse_response(
-                decision.rationale, today, market, candidates, cross_market
+                decision.rationale, today, market, candidates, cross_market,
+                current_holdings=current_holdings,
             )
             playbook_with_tokens = playbook.model_copy(
                 update={"token_count": decision.token_count}
@@ -230,6 +237,7 @@ class PreMarketPlanner:
         context_data: dict[str, Any],
         self_market_scorecard: dict[str, Any] | None,
         cross_market: CrossMarketContext | None,
+        current_holdings: list[dict] | None = None,
     ) -> str:
         """Build a structured prompt for Gemini to generate scenario JSON."""
         max_scenarios = self._settings.MAX_SCENARIOS_PER_STOCK
@@ -241,6 +249,26 @@ class PreMarketPlanner:
             for c in candidates
         )
 
+        holdings_text = ""
+        if current_holdings:
+            lines = []
+            for h in current_holdings:
+                code = h.get("stock_code", "")
+                name = h.get("name", "")
+                qty = h.get("qty", 0)
+                entry_price = h.get("entry_price", 0.0)
+                pnl_pct = h.get("unrealized_pnl_pct", 0.0)
+                holding_days = h.get("holding_days", 0)
+                lines.append(
+                    f"  - {code} ({name}): {qty}주 @ {entry_price:,.0f}, "
+                    f"미실현손익 {pnl_pct:+.2f}%, 보유 {holding_days}일"
+                )
+            holdings_text = (
+                "\n## Current Holdings (보유 중 — SELL/HOLD 전략 고려 필요)\n"
+                + "\n".join(lines)
+                + "\n"
+            )
+
         cross_market_text = ""
         if cross_market:
             cross_market_text = (
@@ -273,10 +301,20 @@ class PreMarketPlanner:
                     for key, value in list(layer_data.items())[:5]:
                         context_text += f"  - {key}: {value}\n"
 
+        holdings_instruction = ""
+        if current_holdings:
+            holding_codes = [h.get("stock_code", "") for h in current_holdings]
+            holdings_instruction = (
+                f"- Also include SELL/HOLD scenarios for held stocks: "
+                f"{', '.join(holding_codes)} "
+                f"(even if not in candidates list)\n"
+            )
+
         return (
             f"You are a pre-market trading strategist for the {market} market.\n"
             f"Generate structured trading scenarios for today.\n\n"
             f"## Candidates (from volatility scanner)\n{candidates_text}\n"
+            f"{holdings_text}"
             f"{self_market_text}"
             f"{cross_market_text}"
             f"{context_text}\n"
@@ -294,7 +332,8 @@ class PreMarketPlanner:
             f'      "stock_code": "...",\n'
             f'      "scenarios": [\n'
             f'        {{\n'
-            f'          "condition": {{"rsi_below": 30, "volume_ratio_above": 2.0}},\n'
+            f'          "condition": {{"rsi_below": 30, "volume_ratio_above": 2.0,'
+            f' "unrealized_pnl_pct_above": 3.0, "holding_days_above": 5}},\n'
             f'          "action": "BUY|SELL|HOLD",\n'
             f'          "confidence": 85,\n'
             f'          "allocation_pct": 10.0,\n'
@@ -308,7 +347,8 @@ class PreMarketPlanner:
             f'}}\n\n'
             f"Rules:\n"
             f"- Max {max_scenarios} scenarios per stock\n"
-            f"- Only use stocks from the candidates list\n"
+            f"- Candidates list is the primary source for BUY candidates\n"
+            f"{holdings_instruction}"
             f"- Confidence 0-100 (80+ for actionable trades)\n"
             f"- stop_loss_pct must be <= 0, take_profit_pct must be >= 0\n"
             f"- Return ONLY the JSON, no markdown fences or explanation\n"
@@ -321,12 +361,19 @@ class PreMarketPlanner:
         market: str,
         candidates: list[ScanCandidate],
         cross_market: CrossMarketContext | None,
+        current_holdings: list[dict] | None = None,
     ) -> DayPlaybook:
         """Parse Gemini's JSON response into a validated DayPlaybook."""
         cleaned = self._extract_json(response_text)
         data = json.loads(cleaned)
 
         valid_codes = {c.stock_code for c in candidates}
+        # Holdings are also valid — AI may generate SELL/HOLD scenarios for them
+        if current_holdings:
+            for h in current_holdings:
+                code = h.get("stock_code", "")
+                if code:
+                    valid_codes.add(code)
 
         # Parse market outlook
         outlook_str = data.get("market_outlook", "neutral")
@@ -390,6 +437,10 @@ class PreMarketPlanner:
                 price_below=cond_data.get("price_below"),
                 price_change_pct_above=cond_data.get("price_change_pct_above"),
                 price_change_pct_below=cond_data.get("price_change_pct_below"),
+                unrealized_pnl_pct_above=cond_data.get("unrealized_pnl_pct_above"),
+                unrealized_pnl_pct_below=cond_data.get("unrealized_pnl_pct_below"),
+                holding_days_above=cond_data.get("holding_days_above"),
+                holding_days_below=cond_data.get("holding_days_below"),
             )
 
             if not condition.has_any_condition():

src/strategy/scenario_engine.py

@@ -206,6 +206,37 @@ class ScenarioEngine:
         if condition.price_change_pct_below is not None:
             checks.append(price_change_pct is not None and price_change_pct < condition.price_change_pct_below)
 
+        # Position-aware conditions
+        unrealized_pnl_pct = self._safe_float(market_data.get("unrealized_pnl_pct"))
+        if condition.unrealized_pnl_pct_above is not None or condition.unrealized_pnl_pct_below is not None:
+            if "unrealized_pnl_pct" not in market_data:
+                self._warn_missing_key("unrealized_pnl_pct")
+        if condition.unrealized_pnl_pct_above is not None:
+            checks.append(
+                unrealized_pnl_pct is not None
+                and unrealized_pnl_pct > condition.unrealized_pnl_pct_above
+            )
+        if condition.unrealized_pnl_pct_below is not None:
+            checks.append(
+                unrealized_pnl_pct is not None
+                and unrealized_pnl_pct < condition.unrealized_pnl_pct_below
+            )
+
+        holding_days = self._safe_float(market_data.get("holding_days"))
+        if condition.holding_days_above is not None or condition.holding_days_below is not None:
+            if "holding_days" not in market_data:
+                self._warn_missing_key("holding_days")
+        if condition.holding_days_above is not None:
+            checks.append(
+                holding_days is not None
+                and holding_days > condition.holding_days_above
+            )
+        if condition.holding_days_below is not None:
+            checks.append(
+                holding_days is not None
+                and holding_days < condition.holding_days_below
+            )
+
         return len(checks) > 0 and all(checks)
 
     def _evaluate_global_condition(
@@ -266,5 +297,9 @@ class ScenarioEngine:
             details["current_price"] = self._safe_float(market_data.get("current_price"))
         if condition.price_change_pct_above is not None or condition.price_change_pct_below is not None:
             details["price_change_pct"] = self._safe_float(market_data.get("price_change_pct"))
+        if condition.unrealized_pnl_pct_above is not None or condition.unrealized_pnl_pct_below is not None:
+            details["unrealized_pnl_pct"] = self._safe_float(market_data.get("unrealized_pnl_pct"))
+        if condition.holding_days_above is not None or condition.holding_days_below is not None:
+            details["holding_days"] = self._safe_float(market_data.get("holding_days"))
 
         return details

tests/test_main.py

@@ -14,6 +14,9 @@ from src.evolution.scorecard import DailyScorecard
 from src.logging.decision_logger import DecisionLogger
 from src.main import (
     _apply_dashboard_flag,
+    _determine_order_quantity,
+    _extract_held_codes_from_balance,
+    _extract_held_qty_from_balance,
     _handle_market_close,
     _run_context_scheduler,
     _run_evolution_loop,
@@ -68,6 +71,219 @@ def _make_sell_match(stock_code: str = "005930") -> ScenarioMatch:
     )
 
 
+class TestExtractHeldQtyFromBalance:
+    """Tests for _extract_held_qty_from_balance()."""
+
+    def _domestic_balance(self, stock_code: str, ord_psbl_qty: int) -> dict:
+        return {
+            "output1": [{"pdno": stock_code, "ord_psbl_qty": str(ord_psbl_qty)}],
+            "output2": [{"dnca_tot_amt": "1000000"}],
+        }
+
+    def test_domestic_returns_ord_psbl_qty(self) -> None:
+        balance = self._domestic_balance("005930", 7)
+        assert _extract_held_qty_from_balance(balance, "005930", is_domestic=True) == 7
+
+    def test_domestic_fallback_to_hldg_qty(self) -> None:
+        balance = {"output1": [{"pdno": "005930", "hldg_qty": "3"}]}
+        assert _extract_held_qty_from_balance(balance, "005930", is_domestic=True) == 3
+
+    def test_domestic_returns_zero_when_not_found(self) -> None:
+        balance = self._domestic_balance("005930", 5)
+        assert _extract_held_qty_from_balance(balance, "000660", is_domestic=True) == 0
+
+    def test_domestic_returns_zero_when_output1_empty(self) -> None:
+        balance = {"output1": [], "output2": [{}]}
+        assert _extract_held_qty_from_balance(balance, "005930", is_domestic=True) == 0
+
+    def test_overseas_returns_ovrs_cblc_qty(self) -> None:
+        balance = {"output1": [{"ovrs_pdno": "AAPL", "ovrs_cblc_qty": "10"}]}
+        assert _extract_held_qty_from_balance(balance, "AAPL", is_domestic=False) == 10
+
+    def test_overseas_fallback_to_hldg_qty(self) -> None:
+        balance = {"output1": [{"ovrs_pdno": "AAPL", "hldg_qty": "4"}]}
+        assert _extract_held_qty_from_balance(balance, "AAPL", is_domestic=False) == 4
+
+    def test_case_insensitive_match(self) -> None:
+        balance = {"output1": [{"pdno": "005930", "ord_psbl_qty": "2"}]}
+        assert _extract_held_qty_from_balance(balance, "005930", is_domestic=True) == 2
+
+
+class TestExtractHeldCodesFromBalance:
+    """Tests for _extract_held_codes_from_balance()."""
+
+    def test_returns_codes_with_positive_qty(self) -> None:
+        balance = {
+            "output1": [
+                {"pdno": "005930", "ord_psbl_qty": "5"},
+                {"pdno": "000660", "ord_psbl_qty": "3"},
+            ]
+        }
+        result = _extract_held_codes_from_balance(balance, is_domestic=True)
+        assert set(result) == {"005930", "000660"}
+
+    def test_excludes_zero_qty_holdings(self) -> None:
+        balance = {
+            "output1": [
+                {"pdno": "005930", "ord_psbl_qty": "0"},
+                {"pdno": "000660", "ord_psbl_qty": "2"},
+            ]
+        }
+        result = _extract_held_codes_from_balance(balance, is_domestic=True)
+        assert "005930" not in result
+        assert "000660" in result
+
+    def test_returns_empty_when_output1_missing(self) -> None:
+        balance: dict = {}
+        assert _extract_held_codes_from_balance(balance, is_domestic=True) == []
+
+    def test_overseas_uses_ovrs_pdno(self) -> None:
+        balance = {"output1": [{"ovrs_pdno": "AAPL", "ovrs_cblc_qty": "3"}]}
+        result = _extract_held_codes_from_balance(balance, is_domestic=False)
+        assert result == ["AAPL"]
+
+
+class TestDetermineOrderQuantity:
+    """Test _determine_order_quantity() — SELL uses broker_held_qty."""
+
+    def test_sell_returns_broker_held_qty(self) -> None:
+        result = _determine_order_quantity(
+            action="SELL",
+            current_price=105.0,
+            total_cash=50000.0,
+            candidate=None,
+            settings=None,
+            broker_held_qty=7,
+        )
+        assert result == 7
+
+    def test_sell_returns_zero_when_broker_qty_zero(self) -> None:
+        result = _determine_order_quantity(
+            action="SELL",
+            current_price=105.0,
+            total_cash=50000.0,
+            candidate=None,
+            settings=None,
+            broker_held_qty=0,
+        )
+        assert result == 0
+
+    def test_buy_without_position_sizing_returns_one(self) -> None:
+        result = _determine_order_quantity(
+            action="BUY",
+            current_price=50000.0,
+            total_cash=1000000.0,
+            candidate=None,
+            settings=None,
+        )
+        assert result == 1
+
+    def test_buy_with_zero_cash_returns_zero(self) -> None:
+        result = _determine_order_quantity(
+            action="BUY",
+            current_price=50000.0,
+            total_cash=0.0,
+            candidate=None,
+            settings=None,
+        )
+        assert result == 0
+
+    def test_buy_with_position_sizing_calculates_correctly(self) -> None:
+        settings = MagicMock(spec=Settings)
+        settings.POSITION_SIZING_ENABLED = True
+        settings.POSITION_VOLATILITY_TARGET_SCORE = 50.0
+        settings.POSITION_BASE_ALLOCATION_PCT = 10.0
+        settings.POSITION_MAX_ALLOCATION_PCT = 30.0
+        settings.POSITION_MIN_ALLOCATION_PCT = 1.0
+        # 1,000,000 * 10% = 100,000 budget // 50,000 price = 2 shares
+        result = _determine_order_quantity(
+            action="BUY",
+            current_price=50000.0,
+            total_cash=1000000.0,
+            candidate=None,
+            settings=settings,
+        )
+        assert result == 2
+
+    def test_determine_order_quantity_uses_playbook_allocation_pct(self) -> None:
+        """playbook_allocation_pct should take priority over volatility-based sizing."""
+        settings = MagicMock(spec=Settings)
+        settings.POSITION_SIZING_ENABLED = True
+        settings.POSITION_MAX_ALLOCATION_PCT = 30.0
+        settings.POSITION_MIN_ALLOCATION_PCT = 1.0
+        # playbook says 20%, confidence 80 → scale=1.0 → 20%
+        # 1,000,000 * 20% = 200,000 // 50,000 price = 4 shares
+        result = _determine_order_quantity(
+            action="BUY",
+            current_price=50000.0,
+            total_cash=1000000.0,
+            candidate=None,
+            settings=settings,
+            playbook_allocation_pct=20.0,
+            scenario_confidence=80,
+        )
+        assert result == 4
+
+    def test_determine_order_quantity_confidence_scales_allocation(self) -> None:
+        """Higher confidence should produce a larger allocation (up to max)."""
+        settings = MagicMock(spec=Settings)
+        settings.POSITION_SIZING_ENABLED = True
+        settings.POSITION_MAX_ALLOCATION_PCT = 30.0
+        settings.POSITION_MIN_ALLOCATION_PCT = 1.0
+        # confidence 96 → scale=1.2 → 10% * 1.2 = 12%
+        # 1,000,000 * 12% = 120,000 // 50,000 price = 2 shares
+        result = _determine_order_quantity(
+            action="BUY",
+            current_price=50000.0,
+            total_cash=1000000.0,
+            candidate=None,
+            settings=settings,
+            playbook_allocation_pct=10.0,
+            scenario_confidence=96,
+        )
+        # scale = 96/80 = 1.2 → effective_pct = 12.0
+        # budget = 1_000_000 * 0.12 = 120_000 → qty = 120_000 // 50_000 = 2
+        assert result == 2
+
+    def test_determine_order_quantity_confidence_clamped_to_max(self) -> None:
+        """Confidence scaling should not exceed POSITION_MAX_ALLOCATION_PCT."""
+        settings = MagicMock(spec=Settings)
+        settings.POSITION_SIZING_ENABLED = True
+        settings.POSITION_MAX_ALLOCATION_PCT = 15.0
+        settings.POSITION_MIN_ALLOCATION_PCT = 1.0
+        # playbook 20% * scale 1.5 = 30% → clamped to 15%
+        # 1,000,000 * 15% = 150,000 // 50,000 price = 3 shares
+        result = _determine_order_quantity(
+            action="BUY",
+            current_price=50000.0,
+            total_cash=1000000.0,
+            candidate=None,
+            settings=settings,
+            playbook_allocation_pct=20.0,
+            scenario_confidence=120,  # extreme → scale = 1.5
+        )
+        assert result == 3
+
+    def test_determine_order_quantity_fallback_when_no_playbook(self) -> None:
+        """Without playbook_allocation_pct, falls back to volatility-based sizing."""
+        settings = MagicMock(spec=Settings)
+        settings.POSITION_SIZING_ENABLED = True
+        settings.POSITION_VOLATILITY_TARGET_SCORE = 50.0
+        settings.POSITION_BASE_ALLOCATION_PCT = 10.0
+        settings.POSITION_MAX_ALLOCATION_PCT = 30.0
+        settings.POSITION_MIN_ALLOCATION_PCT = 1.0
+        # Same as test_buy_with_position_sizing_calculates_correctly (no playbook)
+        result = _determine_order_quantity(
+            action="BUY",
+            current_price=50000.0,
+            total_cash=1000000.0,
+            candidate=None,
+            settings=settings,
+            playbook_allocation_pct=None,  # explicit None → fallback
+        )
+        assert result == 2
+
+
 class TestSafeFloat:
     """Test safe_float() helper function."""
 
@@ -1240,13 +1456,14 @@ async def test_sell_updates_original_buy_decision_outcome() -> None:
     broker.get_current_price = AsyncMock(return_value=(120.0, 0.0, 0.0))
     broker.get_balance = AsyncMock(
         return_value={
+            "output1": [{"pdno": "005930", "ord_psbl_qty": "1"}],
             "output2": [
                 {
                     "tot_evlu_amt": "100000",
                     "dnca_tot_amt": "10000",
                     "pchs_amt_smtl_amt": "90000",
                 }
-            ]
+            ],
         }
     )
     broker.send_order = AsyncMock(return_value={"msg1": "OK"})
@@ -1330,13 +1547,14 @@ async def test_hold_overridden_to_sell_when_stop_loss_triggered() -> None:
     broker.get_current_price = AsyncMock(return_value=(95.0, -5.0, 0.0))
     broker.get_balance = AsyncMock(
         return_value={
+            "output1": [{"pdno": "005930", "ord_psbl_qty": "1"}],
             "output2": [
                 {
                     "tot_evlu_amt": "100000",
                     "dnca_tot_amt": "10000",
                     "pchs_amt_smtl_amt": "90000",
                 }
-            ]
+            ],
         }
     )
     broker.send_order = AsyncMock(return_value={"msg1": "OK"})
@@ -1430,13 +1648,14 @@ async def test_hold_overridden_to_sell_when_take_profit_triggered() -> None:
     broker.get_current_price = AsyncMock(return_value=(106.0, 6.0, 0.0))
     broker.get_balance = AsyncMock(
         return_value={
+            "output1": [{"pdno": "005930", "ord_psbl_qty": "1"}],
             "output2": [
                 {
                     "tot_evlu_amt": "100000",
                     "dnca_tot_amt": "10000",
                     "pchs_amt_smtl_amt": "90000",
                 }
-            ]
+            ],
         }
     )
     broker.send_order = AsyncMock(return_value={"msg1": "OK"})
@@ -1597,6 +1816,116 @@ async def test_hold_not_overridden_when_between_stop_loss_and_take_profit() -> N
     broker.send_order.assert_not_called()
 
 
+@pytest.mark.asyncio
+async def test_sell_order_uses_broker_balance_qty_not_db() -> None:
+    """SELL quantity must come from broker balance output1, not DB.
+
+    The DB records order quantity which may differ from actual fill quantity.
+    This test verifies that we use the broker-confirmed orderable quantity.
+    """
+    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=90,
+        rationale="entry",
+        context_snapshot={},
+        input_data={},
+    )
+    # DB records 10 shares ordered — but only 5 actually filled (partial fill scenario)
+    log_trade(
+        conn=db_conn,
+        stock_code="005930",
+        action="BUY",
+        confidence=90,
+        rationale="entry",
+        quantity=10,  # ordered quantity (may differ from fill)
+        price=100.0,
+        market="KR",
+        exchange_code="KRX",
+        decision_id=buy_decision_id,
+    )
+
+    broker = MagicMock()
+    # Stop-loss triggers (price dropped below -2%)
+    broker.get_current_price = AsyncMock(return_value=(95.0, -5.0, 0.0))
+    broker.get_balance = AsyncMock(
+        return_value={
+            # Broker confirms only 5 shares are actually orderable (partial fill)
+            "output1": [{"pdno": "005930", "ord_psbl_qty": "5"}],
+            "output2": [
+                {
+                    "tot_evlu_amt": "100000",
+                    "dnca_tot_amt": "10000",
+                    "pchs_amt_smtl_amt": "90000",
+                }
+            ],
+        }
+    )
+    broker.send_order = AsyncMock(return_value={"msg1": "OK"})
+
+    scenario = StockScenario(
+        condition=StockCondition(rsi_below=30),
+        action=ScenarioAction.BUY,
+        confidence=88,
+        stop_loss_pct=-2.0,
+        rationale="stop loss policy",
+    )
+    playbook = DayPlaybook(
+        date=date(2026, 2, 8),
+        market="KR",
+        stock_playbooks=[
+            {"stock_code": "005930", "stock_name": "Samsung", "scenarios": [scenario]}
+        ],
+    )
+    engine = MagicMock(spec=ScenarioEngine)
+    engine.evaluate = MagicMock(return_value=_make_hold_match())
+
+    market = MagicMock()
+    market.name = "Korea"
+    market.code = "KR"
+    market.exchange_code = "KRX"
+    market.is_domestic = True
+
+    telegram = MagicMock()
+    telegram.notify_trade_execution = AsyncMock()
+    telegram.notify_fat_finger = AsyncMock()
+    telegram.notify_circuit_breaker = AsyncMock()
+    telegram.notify_scenario_matched = AsyncMock()
+
+    await trading_cycle(
+        broker=broker,
+        overseas_broker=MagicMock(),
+        scenario_engine=engine,
+        playbook=playbook,
+        risk=MagicMock(),
+        db_conn=db_conn,
+        decision_logger=decision_logger,
+        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=telegram,
+        market=market,
+        stock_code="005930",
+        scan_candidates={},
+    )
+
+    broker.send_order.assert_called_once()
+    call_kwargs = broker.send_order.call_args.kwargs
+    assert call_kwargs["order_type"] == "SELL"
+    # Must use broker-confirmed qty (5), NOT DB-recorded ordered qty (10)
+    assert call_kwargs["quantity"] == 5
+
+
 @pytest.mark.asyncio
 async def test_handle_market_close_runs_daily_review_flow() -> None:
     """Market close should aggregate, create scorecard, lessons, and notify."""
@@ -1863,3 +2192,307 @@ def test_start_dashboard_server_enabled_starts_thread() -> None:
     assert thread == mock_thread
     mock_thread_cls.assert_called_once()
     mock_thread.start.assert_called_once()
+
+
+def test_start_dashboard_server_returns_none_when_uvicorn_missing() -> None:
+    """Returns None (no thread) and logs a warning when uvicorn is not installed."""
+    settings = Settings(
+        KIS_APP_KEY="test_key",
+        KIS_APP_SECRET="test_secret",
+        KIS_ACCOUNT_NO="12345678-01",
+        GEMINI_API_KEY="test_gemini_key",
+        DASHBOARD_ENABLED=True,
+    )
+    import builtins
+    real_import = builtins.__import__
+
+    def mock_import(name: str, *args: object, **kwargs: object) -> object:
+        if name == "uvicorn":
+            raise ImportError("No module named 'uvicorn'")
+        return real_import(name, *args, **kwargs)
+
+    with patch("builtins.__import__", side_effect=mock_import):
+        thread = _start_dashboard_server(settings)
+
+    assert thread is None
+
+
+# ---------------------------------------------------------------------------
+# market_outlook BUY confidence threshold tests (#173)
+# ---------------------------------------------------------------------------
+
+
+class TestMarketOutlookConfidenceThreshold:
+    """Tests for market_outlook-based BUY confidence suppression in trading_cycle."""
+
+    @pytest.fixture
+    def mock_broker(self) -> MagicMock:
+        broker = MagicMock()
+        broker.get_current_price = AsyncMock(return_value=(50000.0, 1.0, 0.0))
+        broker.get_balance = AsyncMock(
+            return_value={
+                "output2": [
+                    {
+                        "tot_evlu_amt": "10000000",
+                        "dnca_tot_amt": "5000000",
+                        "pchs_amt_smtl_amt": "9500000",
+                    }
+                ]
+            }
+        )
+        broker.send_order = AsyncMock(return_value={"msg1": "OK"})
+        return broker
+
+    @pytest.fixture
+    def mock_market(self) -> MagicMock:
+        market = MagicMock()
+        market.name = "Korea"
+        market.code = "KR"
+        market.exchange_code = "KRX"
+        market.is_domestic = True
+        return market
+
+    @pytest.fixture
+    def mock_telegram(self) -> MagicMock:
+        telegram = MagicMock()
+        telegram.notify_trade_execution = AsyncMock()
+        telegram.notify_scenario_matched = AsyncMock()
+        telegram.notify_fat_finger = AsyncMock()
+        return telegram
+
+    def _make_buy_match_with_confidence(
+        self, confidence: int, stock_code: str = "005930"
+    ) -> ScenarioMatch:
+        from src.strategy.models import StockScenario
+        scenario = StockScenario(
+            condition=StockCondition(rsi_below=30),
+            action=ScenarioAction.BUY,
+            confidence=confidence,
+            allocation_pct=10.0,
+        )
+        return ScenarioMatch(
+            stock_code=stock_code,
+            matched_scenario=scenario,
+            action=ScenarioAction.BUY,
+            confidence=confidence,
+            rationale="Test buy",
+        )
+
+    def _make_playbook_with_outlook(
+        self, outlook_str: str, market: str = "KR"
+    ) -> DayPlaybook:
+        from src.strategy.models import MarketOutlook
+        outlook_map = {
+            "bearish": MarketOutlook.BEARISH,
+            "bullish": MarketOutlook.BULLISH,
+            "neutral": MarketOutlook.NEUTRAL,
+            "neutral_to_bullish": MarketOutlook.NEUTRAL_TO_BULLISH,
+            "neutral_to_bearish": MarketOutlook.NEUTRAL_TO_BEARISH,
+        }
+        return DayPlaybook(
+            date=date(2026, 2, 20),
+            market=market,
+            market_outlook=outlook_map[outlook_str],
+        )
+
+    @pytest.mark.asyncio
+    async def test_bearish_outlook_raises_buy_confidence_threshold(
+        self,
+        mock_broker: MagicMock,
+        mock_market: MagicMock,
+        mock_telegram: MagicMock,
+    ) -> None:
+        """BUY with confidence 85 should be suppressed to HOLD in bearish market."""
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=self._make_buy_match_with_confidence(85))
+        playbook = self._make_playbook_with_outlook("bearish")
+
+        decision_logger = MagicMock()
+        decision_logger.log_decision = MagicMock(return_value="decision-id")
+
+        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=decision_logger,
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                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={},
+            )
+
+        # HOLD should be logged (not BUY) — check decision_logger was called with HOLD
+        call_args = decision_logger.log_decision.call_args
+        assert call_args is not None
+        assert call_args.kwargs["action"] == "HOLD"
+
+    @pytest.mark.asyncio
+    async def test_bearish_outlook_allows_high_confidence_buy(
+        self,
+        mock_broker: MagicMock,
+        mock_market: MagicMock,
+        mock_telegram: MagicMock,
+    ) -> None:
+        """BUY with confidence 92 should proceed in bearish market (threshold=90)."""
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=self._make_buy_match_with_confidence(92))
+        playbook = self._make_playbook_with_outlook("bearish")
+        risk = MagicMock()
+        risk.validate_order = MagicMock()
+
+        decision_logger = MagicMock()
+        decision_logger.log_decision = MagicMock(return_value="decision-id")
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=playbook,
+                risk=risk,
+                db_conn=MagicMock(),
+                decision_logger=decision_logger,
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                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={},
+            )
+
+        call_args = decision_logger.log_decision.call_args
+        assert call_args is not None
+        assert call_args.kwargs["action"] == "BUY"
+
+    @pytest.mark.asyncio
+    async def test_bullish_outlook_lowers_buy_confidence_threshold(
+        self,
+        mock_broker: MagicMock,
+        mock_market: MagicMock,
+        mock_telegram: MagicMock,
+    ) -> None:
+        """BUY with confidence 77 should proceed in bullish market (threshold=75)."""
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=self._make_buy_match_with_confidence(77))
+        playbook = self._make_playbook_with_outlook("bullish")
+        risk = MagicMock()
+        risk.validate_order = MagicMock()
+
+        decision_logger = MagicMock()
+        decision_logger.log_decision = MagicMock(return_value="decision-id")
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=playbook,
+                risk=risk,
+                db_conn=MagicMock(),
+                decision_logger=decision_logger,
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                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={},
+            )
+
+        call_args = decision_logger.log_decision.call_args
+        assert call_args is not None
+        assert call_args.kwargs["action"] == "BUY"
+
+    @pytest.mark.asyncio
+    async def test_bullish_outlook_suppresses_very_low_confidence_buy(
+        self,
+        mock_broker: MagicMock,
+        mock_market: MagicMock,
+        mock_telegram: MagicMock,
+    ) -> None:
+        """BUY with confidence 70 should be suppressed even in bullish market (threshold=75)."""
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=self._make_buy_match_with_confidence(70))
+        playbook = self._make_playbook_with_outlook("bullish")
+
+        decision_logger = MagicMock()
+        decision_logger.log_decision = MagicMock(return_value="decision-id")
+
+        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=decision_logger,
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                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={},
+            )
+
+        call_args = decision_logger.log_decision.call_args
+        assert call_args is not None
+        assert call_args.kwargs["action"] == "HOLD"
+
+    @pytest.mark.asyncio
+    async def test_neutral_outlook_uses_default_threshold(
+        self,
+        mock_broker: MagicMock,
+        mock_market: MagicMock,
+        mock_telegram: MagicMock,
+    ) -> None:
+        """BUY with confidence 82 should proceed in neutral market (default=80)."""
+        engine = MagicMock(spec=ScenarioEngine)
+        engine.evaluate = MagicMock(return_value=self._make_buy_match_with_confidence(82))
+        playbook = self._make_playbook_with_outlook("neutral")
+        risk = MagicMock()
+        risk.validate_order = MagicMock()
+
+        decision_logger = MagicMock()
+        decision_logger.log_decision = MagicMock(return_value="decision-id")
+
+        with patch("src.main.log_trade"):
+            await trading_cycle(
+                broker=mock_broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=engine,
+                playbook=playbook,
+                risk=risk,
+                db_conn=MagicMock(),
+                decision_logger=decision_logger,
+                context_store=MagicMock(get_latest_timeframe=MagicMock(return_value=None)),
+                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={},
+            )
+
+        call_args = decision_logger.log_decision.call_args
+        assert call_args is not None
+        assert call_args.kwargs["action"] == "BUY"

tests/test_pre_market_planner.py

@@ -830,3 +830,171 @@ class TestSmartFallbackPlaybook:
         ]
         assert len(buy_scenarios) == 1
         assert buy_scenarios[0].condition.volume_ratio_above == 2.0  # VOL_MULTIPLIER default
+
+
+# ---------------------------------------------------------------------------
+# Holdings in prompt (#170)
+# ---------------------------------------------------------------------------
+
+
+class TestHoldingsInPrompt:
+    """Tests for current_holdings parameter in generate_playbook / _build_prompt."""
+
+    def _make_holdings(self) -> list[dict]:
+        return [
+            {
+                "stock_code": "005930",
+                "name": "Samsung",
+                "qty": 10,
+                "entry_price": 71000.0,
+                "unrealized_pnl_pct": 2.3,
+                "holding_days": 3,
+            }
+        ]
+
+    def test_build_prompt_includes_holdings_section(self) -> None:
+        """Prompt should contain a Current Holdings section when holdings are given."""
+        planner = _make_planner()
+        candidates = [_candidate()]
+        holdings = self._make_holdings()
+
+        prompt = planner._build_prompt(
+            "KR",
+            candidates,
+            context_data={},
+            self_market_scorecard=None,
+            cross_market=None,
+            current_holdings=holdings,
+        )
+
+        assert "## Current Holdings" in prompt
+        assert "005930" in prompt
+        assert "+2.30%" in prompt
+        assert "보유 3일" in prompt
+
+    def test_build_prompt_no_holdings_omits_section(self) -> None:
+        """Prompt should NOT contain a Current Holdings section when holdings=None."""
+        planner = _make_planner()
+        candidates = [_candidate()]
+
+        prompt = planner._build_prompt(
+            "KR",
+            candidates,
+            context_data={},
+            self_market_scorecard=None,
+            cross_market=None,
+            current_holdings=None,
+        )
+
+        assert "## Current Holdings" not in prompt
+
+    def test_build_prompt_empty_holdings_omits_section(self) -> None:
+        """Empty list should also omit the holdings section."""
+        planner = _make_planner()
+        candidates = [_candidate()]
+
+        prompt = planner._build_prompt(
+            "KR",
+            candidates,
+            context_data={},
+            self_market_scorecard=None,
+            cross_market=None,
+            current_holdings=[],
+        )
+
+        assert "## Current Holdings" not in prompt
+
+    def test_build_prompt_holdings_instruction_included(self) -> None:
+        """Prompt should include instruction to generate scenarios for held stocks."""
+        planner = _make_planner()
+        candidates = [_candidate()]
+        holdings = self._make_holdings()
+
+        prompt = planner._build_prompt(
+            "KR",
+            candidates,
+            context_data={},
+            self_market_scorecard=None,
+            cross_market=None,
+            current_holdings=holdings,
+        )
+
+        assert "005930" in prompt
+        assert "SELL/HOLD" in prompt
+
+    @pytest.mark.asyncio
+    async def test_generate_playbook_passes_holdings_to_prompt(self) -> None:
+        """generate_playbook should pass current_holdings through to the prompt."""
+        planner = _make_planner()
+        candidates = [_candidate()]
+        holdings = self._make_holdings()
+
+        # Capture the actual prompt sent to Gemini
+        captured_prompts: list[str] = []
+        original_decide = planner._gemini.decide
+
+        async def capture_and_call(data: dict) -> TradeDecision:
+            captured_prompts.append(data.get("prompt_override", ""))
+            return await original_decide(data)
+
+        planner._gemini.decide = capture_and_call  # type: ignore[method-assign]
+
+        await planner.generate_playbook(
+            "KR", candidates, today=date(2026, 2, 8), current_holdings=holdings
+        )
+
+        assert len(captured_prompts) == 1
+        assert "## Current Holdings" in captured_prompts[0]
+        assert "005930" in captured_prompts[0]
+
+    @pytest.mark.asyncio
+    async def test_holdings_stock_allowed_in_parse_response(self) -> None:
+        """Holdings stocks not in candidates list should be accepted in the response."""
+        holding_code = "000660"  # Not in candidates
+        stocks = [
+            {
+                "stock_code": "005930",  # candidate
+                "scenarios": [
+                    {
+                        "condition": {"rsi_below": 30},
+                        "action": "BUY",
+                        "confidence": 85,
+                        "rationale": "oversold",
+                    }
+                ],
+            },
+            {
+                "stock_code": holding_code,  # holding only
+                "scenarios": [
+                    {
+                        "condition": {"price_change_pct_below": -2.0},
+                        "action": "SELL",
+                        "confidence": 90,
+                        "rationale": "stop-loss",
+                    }
+                ],
+            },
+        ]
+        planner = _make_planner(gemini_response=_gemini_response_json(stocks=stocks))
+        candidates = [_candidate()]  # only 005930
+        holdings = [
+            {
+                "stock_code": holding_code,
+                "name": "SK Hynix",
+                "qty": 5,
+                "entry_price": 180000.0,
+                "unrealized_pnl_pct": -1.5,
+                "holding_days": 7,
+            }
+        ]
+
+        pb = await planner.generate_playbook(
+            "KR",
+            candidates,
+            today=date(2026, 2, 8),
+            current_holdings=holdings,
+        )
+
+        codes = [sp.stock_code for sp in pb.stock_playbooks]
+        assert "005930" in codes
+        assert holding_code in codes

tests/test_scenario_engine.py

@@ -440,3 +440,135 @@ class TestEvaluate:
         assert result.action == ScenarioAction.BUY
         assert result.match_details["rsi"] == 25.0
         assert isinstance(result.match_details["rsi"], float)
+
+
+# ---------------------------------------------------------------------------
+# Position-aware condition tests (#171)
+# ---------------------------------------------------------------------------
+
+
+class TestPositionAwareConditions:
+    """Tests for unrealized_pnl_pct and holding_days condition fields."""
+
+    def test_evaluate_condition_unrealized_pnl_above_matches(
+        self, engine: ScenarioEngine
+    ) -> None:
+        """unrealized_pnl_pct_above should match when P&L exceeds threshold."""
+        condition = StockCondition(unrealized_pnl_pct_above=3.0)
+        assert engine.evaluate_condition(condition, {"unrealized_pnl_pct": 5.0}) is True
+
+    def test_evaluate_condition_unrealized_pnl_above_no_match(
+        self, engine: ScenarioEngine
+    ) -> None:
+        """unrealized_pnl_pct_above should NOT match when P&L is below threshold."""
+        condition = StockCondition(unrealized_pnl_pct_above=3.0)
+        assert engine.evaluate_condition(condition, {"unrealized_pnl_pct": 2.0}) is False
+
+    def test_evaluate_condition_unrealized_pnl_below_matches(
+        self, engine: ScenarioEngine
+    ) -> None:
+        """unrealized_pnl_pct_below should match when P&L is under threshold."""
+        condition = StockCondition(unrealized_pnl_pct_below=-2.0)
+        assert engine.evaluate_condition(condition, {"unrealized_pnl_pct": -3.5}) is True
+
+    def test_evaluate_condition_unrealized_pnl_below_no_match(
+        self, engine: ScenarioEngine
+    ) -> None:
+        """unrealized_pnl_pct_below should NOT match when P&L is above threshold."""
+        condition = StockCondition(unrealized_pnl_pct_below=-2.0)
+        assert engine.evaluate_condition(condition, {"unrealized_pnl_pct": -1.0}) is False
+
+    def test_evaluate_condition_holding_days_above_matches(
+        self, engine: ScenarioEngine
+    ) -> None:
+        """holding_days_above should match when position held longer than threshold."""
+        condition = StockCondition(holding_days_above=5)
+        assert engine.evaluate_condition(condition, {"holding_days": 7}) is True
+
+    def test_evaluate_condition_holding_days_above_no_match(
+        self, engine: ScenarioEngine
+    ) -> None:
+        """holding_days_above should NOT match when position held shorter."""
+        condition = StockCondition(holding_days_above=5)
+        assert engine.evaluate_condition(condition, {"holding_days": 3}) is False
+
+    def test_evaluate_condition_holding_days_below_matches(
+        self, engine: ScenarioEngine
+    ) -> None:
+        """holding_days_below should match when position held fewer days."""
+        condition = StockCondition(holding_days_below=3)
+        assert engine.evaluate_condition(condition, {"holding_days": 1}) is True
+
+    def test_evaluate_condition_holding_days_below_no_match(
+        self, engine: ScenarioEngine
+    ) -> None:
+        """holding_days_below should NOT match when held more days."""
+        condition = StockCondition(holding_days_below=3)
+        assert engine.evaluate_condition(condition, {"holding_days": 5}) is False
+
+    def test_combined_pnl_and_holding_days(self, engine: ScenarioEngine) -> None:
+        """Combined position-aware conditions should AND-evaluate correctly."""
+        condition = StockCondition(
+            unrealized_pnl_pct_above=3.0,
+            holding_days_above=5,
+        )
+        # Both met → match
+        assert engine.evaluate_condition(
+            condition,
+            {"unrealized_pnl_pct": 4.5, "holding_days": 7},
+        ) is True
+        # Only pnl met → no match
+        assert engine.evaluate_condition(
+            condition,
+            {"unrealized_pnl_pct": 4.5, "holding_days": 3},
+        ) is False
+
+    def test_missing_unrealized_pnl_does_not_match(
+        self, engine: ScenarioEngine
+    ) -> None:
+        """Missing unrealized_pnl_pct key should not match the condition."""
+        condition = StockCondition(unrealized_pnl_pct_above=3.0)
+        assert engine.evaluate_condition(condition, {}) is False
+
+    def test_missing_holding_days_does_not_match(
+        self, engine: ScenarioEngine
+    ) -> None:
+        """Missing holding_days key should not match the condition."""
+        condition = StockCondition(holding_days_above=5)
+        assert engine.evaluate_condition(condition, {}) is False
+
+    def test_match_details_includes_position_fields(
+        self, engine: ScenarioEngine
+    ) -> None:
+        """match_details should include position fields when condition specifies them."""
+        pb = _playbook(
+            scenarios=[
+                StockScenario(
+                    condition=StockCondition(unrealized_pnl_pct_above=3.0),
+                    action=ScenarioAction.SELL,
+                    confidence=90,
+                    rationale="Take profit",
+                )
+            ]
+        )
+        result = engine.evaluate(
+            pb,
+            "005930",
+            {"unrealized_pnl_pct": 5.0},
+            {},
+        )
+        assert result.action == ScenarioAction.SELL
+        assert "unrealized_pnl_pct" in result.match_details
+        assert result.match_details["unrealized_pnl_pct"] == 5.0
+
+    def test_position_conditions_parse_from_planner(self) -> None:
+        """StockCondition should accept and store new fields from JSON parsing."""
+        condition = StockCondition(
+            unrealized_pnl_pct_above=3.0,
+            unrealized_pnl_pct_below=None,
+            holding_days_above=5,
+            holding_days_below=None,
+        )
+        assert condition.unrealized_pnl_pct_above == 3.0
+        assert condition.holding_days_above == 5
+        assert condition.has_any_condition() is True