fix: market_data에 unrealized_pnl_pct/holding_days 추가하여 SELL 시나리오 정상화 (#259)

trading_cycle()의 market_data에 보유 포지션 정보가 없어
Condition requires 'unrealized_pnl_pct' but key missing from market_data 경고 발생.
보유 종목(NVDA 등)의 take-profit/stop-loss 시나리오가 평가 불가하여 HOLD(confidence=0) 고착.

- get_open_position()에 timestamp 컬럼 추가
- market_data 구성 시 open_position 조회 후 아래 키 추가:
  - unrealized_pnl_pct: (current_price - entry_price) / entry_price * 100
  - holding_days: 매수일로부터 경과 일수

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

scripts/run_overnight.sh

@@ -23,7 +23,7 @@ if [ -z "${APP_CMD:-}" ]; then
 
     dashboard_port="${DASHBOARD_PORT:-8080}"
 
-    APP_CMD="DASHBOARD_PORT=$dashboard_port $PYTHON_BIN -m src.main --mode=paper --dashboard"
+    APP_CMD="DASHBOARD_PORT=$dashboard_port $PYTHON_BIN -m src.main --mode=live --dashboard"
 fi
 
 mkdir -p "$LOG_DIR"

src/broker/overseas.py

@@ -121,6 +121,7 @@ class OverseasBroker:
             tr_id = self._broker._settings.OVERSEAS_RANKING_VOLUME_TR_ID
             path = self._broker._settings.OVERSEAS_RANKING_VOLUME_PATH
             params: dict[str, str] = {
+                "KEYB": "",  # NEXT KEY BUFF — Required, 공백
                 "AUTH": "",
                 "EXCD": ranking_excd,
                 "MIXN": "0",
@@ -130,10 +131,11 @@ class OverseasBroker:
             tr_id = self._broker._settings.OVERSEAS_RANKING_FLUCT_TR_ID
             path = self._broker._settings.OVERSEAS_RANKING_FLUCT_PATH
             params = {
+                "KEYB": "",  # NEXT KEY BUFF — Required, 공백
                 "AUTH": "",
                 "EXCD": ranking_excd,
                 "NDAY": "0",
-                "GUBN": "0",  # 0=전체(상승+하락), 1=상승만 — 변동성 스캐너는 전체 필요
+                "GUBN": "1",  # 0=하락율, 1=상승율 — 변동성 스캐너는 급등 종목 우선
                 "VOL_RANG": "0",
             }
 

src/db.py

@@ -254,7 +254,7 @@ def get_open_position(
     """Return open position if latest trade is BUY, else None."""
     cursor = conn.execute(
         """
-        SELECT action, decision_id, price, quantity
+        SELECT action, decision_id, price, quantity, timestamp
         FROM trades
         WHERE stock_code = ?
           AND market = ?
@@ -266,7 +266,7 @@ def get_open_position(
     row = cursor.fetchone()
     if not row or row[0] != "BUY":
         return None
-    return {"decision_id": row[1], "price": row[2], "quantity": row[3]}
+    return {"decision_id": row[1], "price": row[2], "quantity": row[3], "timestamp": row[4]}
 
 
 def get_recent_symbols(

src/main.py

@@ -576,6 +576,22 @@ async def trading_cycle(
         market_data["rsi"] = candidate.rsi
         market_data["volume_ratio"] = candidate.volume_ratio
 
+    # Enrich market_data with holding info for SELL/HOLD scenario conditions
+    open_pos = get_open_position(db_conn, stock_code, market.code)
+    if open_pos and current_price > 0:
+        entry_price = safe_float(open_pos.get("price"), 0.0)
+        if entry_price > 0:
+            market_data["unrealized_pnl_pct"] = (
+                (current_price - entry_price) / entry_price * 100
+            )
+        entry_ts = open_pos.get("timestamp")
+        if entry_ts:
+            try:
+                entry_date = datetime.fromisoformat(entry_ts).date()
+                market_data["holding_days"] = (datetime.now(UTC).date() - entry_date).days
+            except (ValueError, TypeError):
+                pass
+
     # 1.3. Record L7 real-time context (market-scoped keys)
     timeframe = datetime.now(UTC).isoformat()
     context_store.set_context(

src/strategies/v20260220_210124_evolved.py

@@ -1,114 +0,0 @@
-"""Auto-generated strategy: v20260220_210124
-
-Generated at: 2026-02-20T21:01:24.706847+00:00
-Rationale: Auto-evolved from 6 failures. Primary failure markets: ['US_AMEX', 'US_NYSE', 'US_NASDAQ']. Average loss: -194.69
-"""
-
-from __future__ import annotations
-from typing import Any
-from src.strategies.base import BaseStrategy
-
-
-class Strategy_v20260220_210124(BaseStrategy):
-    """Strategy: v20260220_210124"""
-
-    def evaluate(self, market_data: dict[str, Any]) -> dict[str, Any]:
-        import datetime
-
-        # --- Strategy Constants ---
-        # Minimum price for a stock to be considered for trading (avoids penny stocks)
-        MIN_PRICE = 5.0
-
-        # Momentum signal thresholds (stricter than previous failures)
-        MOMENTUM_PRICE_CHANGE_THRESHOLD = 7.0  # % price change
-        MOMENTUM_VOLUME_RATIO_THRESHOLD = 4.0  # X times average volume
-
-        # Oversold signal thresholds (more conservative)
-        OVERSOLD_RSI_THRESHOLD = 25.0  # RSI value (lower means more oversold)
-
-        # Confidence levels
-        CONFIDENCE_HOLD = 30
-        CONFIDENCE_BUY_OVERSOLD = 65
-        CONFIDENCE_BUY_MOMENTUM = 85
-        CONFIDENCE_BUY_STRONG_MOMENTUM = 90  # For higher-priced stocks with strong momentum
-
-        # Market hours in UTC (9:30 AM ET to 4:00 PM ET)
-        MARKET_OPEN_UTC = datetime.time(14, 30)
-        MARKET_CLOSE_UTC = datetime.time(21, 0)
-
-        # Volatile periods within market hours (UTC) to avoid
-        # First hour after open (14:30 UTC - 15:30 UTC)
-        VOLATILE_OPEN_END_UTC = datetime.time(15, 30)
-        # Last 30 minutes before close (20:30 UTC - 21:00 UTC)
-        VOLATILE_CLOSE_START_UTC = datetime.time(20, 30)
-
-        current_price = market_data.get('current_price')
-        price_change_pct = market_data.get('price_change_pct')
-        volume_ratio = market_data.get('volume_ratio')  # Assumed pre-computed indicator
-        rsi = market_data.get('rsi')                    # Assumed pre-computed indicator
-        timestamp_str = market_data.get('timestamp')
-
-        action = "HOLD"
-        confidence = CONFIDENCE_HOLD
-        rationale = "Initial HOLD: No clear signal or conditions not met."
-
-        # --- 1. Basic Data Validation ---
-        if current_price is None or price_change_pct is None:
-            return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
-                    "rationale": "Insufficient core data (price or price change) to evaluate."}
-
-        # --- 2. Price Filter: Avoid low-priced/penny stocks ---
-        if current_price < MIN_PRICE:
-            return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
-                    "rationale": f"Avoiding low-priced stock (${current_price:.2f} < ${MIN_PRICE:.2f})."}
-
-        # --- 3. Time Filter: Only trade during core market hours ---
-        if timestamp_str:
-            try:
-                dt_object = datetime.datetime.fromisoformat(timestamp_str)
-                current_time_utc = dt_object.time()
-
-                if not (MARKET_OPEN_UTC <= current_time_utc < MARKET_CLOSE_UTC):
-                    return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
-                            "rationale": f"Avoiding trade outside core market hours ({current_time_utc} UTC)."}
-
-                if (MARKET_OPEN_UTC <= current_time_utc < VOLATILE_OPEN_END_UTC) or \
-                   (VOLATILE_CLOSE_START_UTC <= current_time_utc < MARKET_CLOSE_UTC):
-                    return {"action": "HOLD", "confidence": CONFIDENCE_HOLD,
-                            "rationale": f"Avoiding trade during volatile market open/close periods ({current_time_utc} UTC)."}
-
-            except ValueError:
-                rationale += " (Warning: Malformed timestamp, time filters skipped)"
-
-        # --- Initialize signal states ---
-        has_momentum_buy_signal = False
-        has_oversold_buy_signal = False
-
-        # --- 4. Evaluate Enhanced Buy Signals ---
-
-        # Momentum Buy Signal
-        if volume_ratio is not None and \
-           price_change_pct > MOMENTUM_PRICE_CHANGE_THRESHOLD and \
-           volume_ratio > MOMENTUM_VOLUME_RATIO_THRESHOLD:
-            has_momentum_buy_signal = True
-            rationale = f"Momentum BUY: Price change {price_change_pct:.2f}%, Volume {volume_ratio:.2f}x."
-            confidence = CONFIDENCE_BUY_MOMENTUM
-            if current_price >= 10.0:
-                confidence = CONFIDENCE_BUY_STRONG_MOMENTUM
-
-        # Oversold Buy Signal
-        if rsi is not None and rsi < OVERSOLD_RSI_THRESHOLD:
-            has_oversold_buy_signal = True
-            if not has_momentum_buy_signal:
-                rationale = f"Oversold BUY: RSI {rsi:.2f}."
-                confidence = CONFIDENCE_BUY_OVERSOLD
-                if current_price >= 10.0:
-                    confidence = min(CONFIDENCE_BUY_OVERSOLD + 5, 80)
-
-        # --- 5. Decision Logic ---
-        if has_momentum_buy_signal:
-            action = "BUY"
-        elif has_oversold_buy_signal:
-            action = "BUY"
-
-        return {"action": action, "confidence": confidence, "rationale": rationale}

src/strategies/v20260220_210159_evolved.py

@@ -1,97 +0,0 @@
-"""Auto-generated strategy: v20260220_210159
-
-Generated at: 2026-02-20T21:01:59.391523+00:00
-Rationale: Auto-evolved from 6 failures. Primary failure markets: ['US_AMEX', 'US_NYSE', 'US_NASDAQ']. Average loss: -194.69
-"""
-
-from __future__ import annotations
-from typing import Any
-from src.strategies.base import BaseStrategy
-
-
-class Strategy_v20260220_210159(BaseStrategy):
-    """Strategy: v20260220_210159"""
-
-    def evaluate(self, market_data: dict[str, Any]) -> dict[str, Any]:
-        import datetime
-
-        current_price = market_data.get('current_price')
-        price_change_pct = market_data.get('price_change_pct')
-        volume_ratio = market_data.get('volume_ratio')
-        rsi = market_data.get('rsi')
-        timestamp_str = market_data.get('timestamp')
-        market_name = market_data.get('market')
-
-        # Default action
-        action = "HOLD"
-        confidence = 0
-        rationale = "No strong signal or conditions not met."
-
-        # --- FAILURE PATTERN AVOIDANCE ---
-
-        # 1. Avoid low-priced/penny stocks
-        MIN_PRICE_THRESHOLD = 5.0  # USD
-        if current_price is not None and current_price < MIN_PRICE_THRESHOLD:
-            rationale = (
-                f"HOLD: Stock price (${current_price:.2f}) is below minimum threshold "
-                f"(${MIN_PRICE_THRESHOLD:.2f}). Past failures consistently involved low-priced stocks."
-            )
-            return {"action": action, "confidence": confidence, "rationale": rationale}
-
-        # 2. Avoid early market hour volatility
-        if timestamp_str:
-            try:
-                dt_obj = datetime.datetime.fromisoformat(timestamp_str)
-                utc_hour = dt_obj.hour
-                utc_minute = dt_obj.minute
-
-                if (utc_hour == 14 and utc_minute < 45) or (utc_hour == 13 and utc_minute >= 30):
-                    rationale = (
-                        f"HOLD: Trading during early market hours (UTC {utc_hour}:{utc_minute}), "
-                        f"a period identified with past failures due to high volatility."
-                    )
-                    return {"action": action, "confidence": confidence, "rationale": rationale}
-            except ValueError:
-                pass
-
-        # --- IMPROVED BUY STRATEGY ---
-
-        # Momentum BUY signal
-        if volume_ratio is not None and price_change_pct is not None:
-            if price_change_pct > 7.0 and volume_ratio > 3.0:
-                action = "BUY"
-                confidence = 70
-                rationale = "Improved BUY: Momentum signal with high volume and above price threshold."
-
-                if market_name == 'US_AMEX':
-                    confidence = max(55, confidence - 5)
-                    rationale += " (Adjusted lower for AMEX market's higher risk profile)."
-                elif market_name == 'US_NASDAQ' and price_change_pct > 20:
-                    confidence = max(50, confidence - 10)
-                    rationale += " (Adjusted lower for aggressive NASDAQ momentum volatility)."
-
-                if price_change_pct > 15.0:
-                    confidence = max(50, confidence - 5)
-                    rationale += " (Caution: Very high daily price change, potential for reversal)."
-
-                return {"action": action, "confidence": confidence, "rationale": rationale}
-
-        # Oversold BUY signal
-        if rsi is not None and price_change_pct is not None:
-            if rsi < 30 and price_change_pct < -3.0:
-                action = "BUY"
-                confidence = 65
-                rationale = "Improved BUY: Oversold signal with recent decline and above price threshold."
-
-                if market_name == 'US_AMEX':
-                    confidence = max(50, confidence - 5)
-                    rationale += " (Adjusted lower for AMEX market's higher risk on oversold assets)."
-
-                if price_change_pct < -10.0:
-                    confidence = max(45, confidence - 10)
-                    rationale += " (Caution: Very steep decline, potential falling knife)."
-
-                return {"action": action, "confidence": confidence, "rationale": rationale}
-
-        # If no specific BUY signal, default to HOLD
-        return {"action": action, "confidence": confidence, "rationale": rationale}

src/strategies/v20260220_210244_evolved.py

@@ -1,88 +0,0 @@
-"""Auto-generated strategy: v20260220_210244
-
-Generated at: 2026-02-20T21:02:44.387355+00:00
-Rationale: Auto-evolved from 6 failures. Primary failure markets: ['US_AMEX', 'US_NYSE', 'US_NASDAQ']. Average loss: -194.69
-"""
-
-from __future__ import annotations
-from typing import Any
-from src.strategies.base import BaseStrategy
-
-
-class Strategy_v20260220_210244(BaseStrategy):
-    """Strategy: v20260220_210244"""
-
-    def evaluate(self, market_data: dict[str, Any]) -> dict[str, Any]:
-        from datetime import datetime
-
-        # Extract required data points safely
-        current_price = market_data.get("current_price")
-        price_change_pct = market_data.get("price_change_pct")
-        volume_ratio = market_data.get("volume_ratio")
-        rsi = market_data.get("rsi")
-        timestamp_str = market_data.get("timestamp")
-        market_name = market_data.get("market")
-        stock_code = market_data.get("stock_code", "UNKNOWN")
-
-        # Default action is HOLD with conservative confidence and rationale
-        action = "HOLD"
-        confidence = 50
-        rationale = f"No strong BUY signal for {stock_code} or awaiting more favorable conditions after avoiding known failure patterns."
-
-        # --- 1. Failure Pattern Avoidance Filters ---
-
-        # A. Avoid low-priced (penny) stocks
-        if current_price is not None and current_price < 5.0:
-            return {
-                "action": "HOLD",
-                "confidence": 50,
-                "rationale": f"AVOID {stock_code}: Stock price (${current_price:.2f}) is below minimum threshold ($5.00) for BUY action. Identified past failures on highly volatile, low-priced stocks."
-            }
-
-        # B. Avoid initiating BUY trades during identified high-volatility hours
-        if timestamp_str:
-            try:
-                trade_hour = datetime.fromisoformat(timestamp_str).hour
-                if trade_hour in [14, 20]:
-                    return {
-                        "action": "HOLD",
-                        "confidence": 50,
-                        "rationale": f"AVOID {stock_code}: Trading during historically volatile hour ({trade_hour} UTC) where previous BUYs resulted in losses. Prefer to observe market stability."
-                    }
-            except ValueError:
-                pass
-
-        # C. Be cautious with extreme momentum spikes
-        if volume_ratio is not None and price_change_pct is not None:
-            if volume_ratio >= 9.0 and price_change_pct >= 15.0:
-                return {
-                    "action": "HOLD",
-                    "confidence": 50,
-                    "rationale": f"AVOID {stock_code}: Extreme short-term momentum detected (price change: +{price_change_pct:.2f}%, volume ratio: {volume_ratio:.1f}x). Historical failures indicate buying into such rapid spikes often leads to reversals."
-                }
-
-        # D. Be cautious with "oversold" signals without further confirmation
-        if rsi is not None and rsi < 30:
-            return {
-                "action": "HOLD",
-                "confidence": 50,
-                "rationale": f"AVOID {stock_code}: Oversold signal (RSI={rsi:.1f}) detected. While often a BUY signal, historical failures on similar 'oversold' trades suggest waiting for stronger confirmation."
-            }
-
-        # --- 2. Improved BUY Signal Generation ---
-        if volume_ratio is not None and 2.0 <= volume_ratio < 9.0 and \
-           price_change_pct is not None and 2.0 <= price_change_pct < 15.0:
-
-            action = "BUY"
-            confidence = 70
-            rationale = f"BUY {stock_code}: Moderate momentum detected (price change: +{price_change_pct:.2f}%, volume ratio: {volume_ratio:.1f}x). Passed filters for price and extreme momentum, avoiding past failure patterns."
-
-            if market_name in ["US_AMEX", "US_NASDAQ"]:
-                confidence = max(60, confidence - 5)
-                rationale += f" Adjusted confidence for {market_name} market characteristics."
-            elif market_name == "US_NYSE":
-                confidence = max(65, confidence)
-
-            confidence = max(50, min(85, confidence))
-
-        return {"action": action, "confidence": confidence, "rationale": rationale}

tests/test_overseas_broker.py

@@ -28,6 +28,7 @@ def mock_settings() -> Settings:
         KIS_APP_SECRET="test_secret",
         KIS_ACCOUNT_NO="12345678-01",
         GEMINI_API_KEY="test_gemini_key",
+        MODE="paper",  # Explicitly set to avoid .env MODE=live override
     )
 
 
@@ -122,9 +123,10 @@ class TestFetchOverseasRankings:
         params = call_args[1]["params"]
 
         assert "/uapi/overseas-stock/v1/ranking/updown-rate" in url
+        assert params["KEYB"] == ""  # Required by KIS API spec
         assert params["EXCD"] == "NAS"
         assert params["NDAY"] == "0"
-        assert params["GUBN"] == "0"  # 0=전체(상승+하락), 변동성 스캐너에 필요
+        assert params["GUBN"] == "1"  # 1=상승율 — 변동성 스캐너는 급등 종목 우선
         assert params["VOL_RANG"] == "0"
 
         overseas_broker._broker._auth_headers.assert_called_with("HHDFS76290000")
@@ -157,6 +159,7 @@ class TestFetchOverseasRankings:
         params = call_args[1]["params"]
 
         assert "/uapi/overseas-stock/v1/ranking/volume-surge" in url
+        assert params["KEYB"] == ""  # Required by KIS API spec
         assert params["EXCD"] == "NYS"
         assert params["MIXN"] == "0"
         assert params["VOL_RANG"] == "0"