Merge pull request 'feat: Daily CB P&L 기준을 당일 시작 평가금액으로 변경 (#207)' (#227) from feature/issue-207-daily-cb-pnl into main

Reviewed-on: #227

CLAUDE.md

@@ -94,6 +94,7 @@ Smart Scanner runs in `TRADE_MODE=realtime` only. Daily mode uses static watchli
 - **[Testing](docs/testing.md)** — Test structure, coverage requirements, writing tests
 - **[Agent Policies](docs/agents.md)** — Prime directives, constraints, prohibited actions
 - **[Requirements Log](docs/requirements-log.md)** — User requirements and feedback tracking
+- **[Live Trading Checklist](docs/live-trading-checklist.md)** — 모의→실전 전환 체크리스트
 
 ## Core Principles
 

docs/live-trading-checklist.md

@@ -0,0 +1,131 @@
+# 실전 전환 체크리스트
+
+모의 거래(paper)에서 실전(live)으로 전환하기 전에 아래 항목을 **순서대로** 모두 확인하세요.
+
+---
+
+## 1. 사전 조건
+
+### 1-1. KIS OpenAPI 실전 계좌 준비
+- [ ] 한국투자증권 계좌 개설 완료 (일반 위탁 계좌)
+- [ ] OpenAPI 실전 사용 신청 (KIS 홈페이지 → Open API → 서비스 신청)
+- [ ] 실전용 APP_KEY / APP_SECRET 발급 완료
+- [ ] KIS_ACCOUNT_NO 형식 확인: `XXXXXXXX-XX` (8자리-2자리)
+
+### 1-2. 리스크 파라미터 검토
+- [ ] `CIRCUIT_BREAKER_PCT` 확인: 기본값 -3.0% (더 엄격하게 조정 권장)
+- [ ] `FAT_FINGER_PCT` 확인: 기본값 30.0% (1회 주문 최대 잔고 대비 %)
+- [ ] `CONFIDENCE_THRESHOLD` 확인: BEARISH ≥ 90, NEUTRAL ≥ 80, BULLISH ≥ 75
+- [ ] 초기 투자금 결정 및 해외 주식 운용 한도 설정
+
+### 1-3. 시스템 요건
+- [ ] 커버리지 80% 이상 유지 확인: `pytest --cov=src`
+- [ ] 타입 체크 통과: `mypy src/ --strict`
+- [ ] Lint 통과: `ruff check src/ tests/`
+
+---
+
+## 2. 환경 설정
+
+### 2-1. `.env` 파일 수정
+
+```bash
+# 1. KIS 실전 URL로 변경 (모의: openapivts 포트 29443)
+KIS_BASE_URL=https://openapi.koreainvestment.com:9443
+
+# 2. 실전 APP_KEY / APP_SECRET으로 교체
+KIS_APP_KEY=<실전_APP_KEY>
+KIS_APP_SECRET=<실전_APP_SECRET>
+KIS_ACCOUNT_NO=<실전_계좌번호>
+
+# 3. 모드를 live로 변경
+MODE=live
+
+# 4. PAPER_OVERSEAS_CASH 비활성화 (live 모드에선 무시되지만 명시적으로 0 설정)
+PAPER_OVERSEAS_CASH=0
+```
+
+> ⚠️ `KIS_BASE_URL` 포트 주의:
+> - **모의(VTS)**: `https://openapivts.koreainvestment.com:29443`
+> - **실전**: `https://openapi.koreainvestment.com:9443`
+
+### 2-2. TR_ID 자동 분기 확인
+
+아래 TR_ID는 `MODE` 값에 따라 코드에서 **자동으로 선택**됩니다.
+별도 설정 불필요하나, 문제 발생 시 아래 표를 참조하세요.
+
+| 구분 | 모의 TR_ID | 실전 TR_ID |
+|------|-----------|-----------|
+| 국내 잔고 조회 | `VTTC8434R` | `TTTC8434R` |
+| 국내 현금 매수 | `VTTC0012U` | `TTTC0012U` |
+| 국내 현금 매도 | `VTTC0011U` | `TTTC0011U` |
+| 해외 잔고 조회 | `VTTS3012R` | `TTTS3012R` |
+| 해외 매수 | `VTTT1002U` | `TTTT1002U` |
+| 해외 매도 | `VTTT1001U` | `TTTT1006U` |
+
+> **출처**: `docs/한국투자증권_오픈API_전체문서_20260221_030000.xlsx` (공식 문서 기준)
+
+---
+
+## 3. 최종 확인
+
+### 3-1. 실전 시작 전 점검
+- [ ] DB 백업 완료: `data/trade_logs.db` → `data/backups/`
+- [ ] Telegram 알림 설정 확인 (실전에서는 알림이 더욱 중요)
+- [ ] 소액으로 첫 거래 진행 후 TR_ID/계좌 정상 동작 확인
+
+### 3-2. 실행 명령
+
+```bash
+# 실전 모드로 실행
+python -m src.main --mode=live
+
+# 대시보드 함께 실행 (별도 터미널에서 모니터링)
+python -m src.main --mode=live --dashboard
+```
+
+### 3-3. 실전 시작 직후 확인 사항
+- [ ] 로그에 `MODE=live` 출력 확인
+- [ ] 첫 잔고 조회 성공 (ConnectionError 없음)
+- [ ] Telegram 알림 수신 확인 ("System started")
+- [ ] 첫 주문 후 KIS 앱에서 체결 내역 확인
+
+---
+
+## 4. 비상 정지 방법
+
+### 즉각 정지
+```bash
+# 터미널에서 Ctrl+C (정상 종료 트리거)
+# 또는 Telegram 봇 명령:
+/stop
+```
+
+### Circuit Breaker 발동 시
+- CB가 발동되면 자동으로 거래 중단 및 Telegram 알림 전송
+- CB 임계값: `CIRCUIT_BREAKER_PCT` (기본 -3.0%)
+- **임계값은 엄격하게만 조정 가능** (더 낮은 음수 값으로만 변경)
+
+---
+
+## 5. 롤백 절차
+
+실전 전환 후 문제 발생 시:
+
+```bash
+# 1. 즉시 .env에서 MODE=paper로 복원
+# 2. 재시작
+python -m src.main --mode=paper
+
+# 3. DB에서 최근 거래 확인
+sqlite3 data/trade_logs.db "SELECT * FROM trades ORDER BY id DESC LIMIT 20;"
+```
+
+---
+
+## 관련 문서
+
+- [시스템 아키텍처](architecture.md)
+- [워크플로우 가이드](workflow.md)
+- [재해 복구](disaster_recovery.md)
+- [Agent 제약 조건](agents.md)

src/config.py

@@ -17,7 +17,7 @@ class Settings(BaseSettings):
 
     # Google Gemini
     GEMINI_API_KEY: str
-    GEMINI_MODEL: str = "gemini-pro"
+    GEMINI_MODEL: str = "gemini-2.0-flash"
 
     # External Data APIs (optional — for data-driven decisions)
     NEWS_API_KEY: str | None = None

src/main.py

@@ -88,6 +88,47 @@ DAILY_TRADE_SESSIONS = 4  # Number of trading sessions per day
 TRADE_SESSION_INTERVAL_HOURS = 6  # Hours between sessions
 
 
+async def _retry_connection(coro_factory: Any, *args: Any, label: str = "", **kwargs: Any) -> Any:
+    """Call an async function retrying on ConnectionError with exponential backoff.
+
+    Retries up to MAX_CONNECTION_RETRIES times (exclusive of the first attempt),
+    sleeping 2^attempt seconds between attempts.  Use only for idempotent read
+    operations — never for order submission.
+
+    Args:
+        coro_factory: Async callable (method or function) to invoke.
+        *args: Positional arguments forwarded to coro_factory.
+        label: Human-readable label for log messages.
+        **kwargs: Keyword arguments forwarded to coro_factory.
+
+    Raises:
+        ConnectionError: If all retries are exhausted.
+    """
+    for attempt in range(1, MAX_CONNECTION_RETRIES + 1):
+        try:
+            return await coro_factory(*args, **kwargs)
+        except ConnectionError as exc:
+            if attempt < MAX_CONNECTION_RETRIES:
+                wait_secs = 2 ** attempt
+                logger.warning(
+                    "Connection error %s (attempt %d/%d), retrying in %ds: %s",
+                    label,
+                    attempt,
+                    MAX_CONNECTION_RETRIES,
+                    wait_secs,
+                    exc,
+                )
+                await asyncio.sleep(wait_secs)
+            else:
+                logger.error(
+                    "Connection error %s — all %d retries exhausted: %s",
+                    label,
+                    MAX_CONNECTION_RETRIES,
+                    exc,
+                )
+                raise
+
+
 def _extract_symbol_from_holding(item: dict[str, Any]) -> str:
     """Extract symbol from overseas holding payload variants."""
     for key in (
@@ -867,18 +908,30 @@ async def run_daily_session(
     telegram: TelegramClient,
     settings: Settings,
     smart_scanner: SmartVolatilityScanner | None = None,
-) -> None:
+    daily_start_eval: float = 0.0,
+) -> float:
     """Execute one daily trading session.
 
     V2 proactive strategy: 1 Gemini call for playbook generation,
     then local scenario evaluation per stock (0 API calls).
+
+    Args:
+        daily_start_eval: Portfolio evaluation at the start of the trading day.
+            Used to compute intra-day P&L for the Circuit Breaker.
+            Pass 0.0 on the first session of each day; the function will set
+            it from the first balance query and return it for subsequent
+            sessions.
+
+    Returns:
+        The daily_start_eval value that should be forwarded to the next
+        session of the same trading day.
     """
     # Get currently open markets
     open_markets = get_open_markets(settings.enabled_market_list)
 
     if not open_markets:
         logger.info("No markets open for this session")
-        return
+        return daily_start_eval
 
     logger.info("Starting daily trading session for %d markets", len(open_markets))
 
@@ -964,11 +1017,18 @@ async def run_daily_session(
             try:
                 if market.is_domestic:
                     current_price, price_change_pct, foreigner_net = (
-                        await broker.get_current_price(stock_code)
+                        await _retry_connection(
+                            broker.get_current_price,
+                            stock_code,
+                            label=stock_code,
+                        )
                     )
                 else:
-                    price_data = await overseas_broker.get_overseas_price(
-                        market.exchange_code, stock_code
+                    price_data = await _retry_connection(
+                        overseas_broker.get_overseas_price,
+                        market.exchange_code,
+                        stock_code,
+                        label=f"{stock_code}@{market.exchange_code}",
                     )
                     current_price = safe_float(
                         price_data.get("output", {}).get("last", "0")
@@ -1019,9 +1079,27 @@ async def run_daily_session(
             logger.warning("No valid stock data for market %s", market.code)
             continue
 
-        # Get balance data once for the market
+        # Get balance data once for the market (read-only — safe to retry)
+        try:
+            if market.is_domestic:
+                balance_data = await _retry_connection(
+                    broker.get_balance, label=f"balance:{market.code}"
+                )
+            else:
+                balance_data = await _retry_connection(
+                    overseas_broker.get_overseas_balance,
+                    market.exchange_code,
+                    label=f"overseas_balance:{market.exchange_code}",
+                )
+        except ConnectionError as exc:
+            logger.error(
+                "Balance fetch failed for market %s after all retries — skipping market: %s",
+                market.code,
+                exc,
+            )
+            continue
+
         if market.is_domestic:
-            balance_data = await broker.get_balance()
             output2 = balance_data.get("output2", [{}])
             total_eval = safe_float(
                 output2[0].get("tot_evlu_amt", "0")
@@ -1033,7 +1111,6 @@ async def run_daily_session(
                 output2[0].get("pchs_amt_smtl_amt", "0")
             ) if output2 else 0
         else:
-            balance_data = await overseas_broker.get_overseas_balance(market.exchange_code)
             output2 = balance_data.get("output2", [{}])
             if isinstance(output2, list) and output2:
                 balance_info = output2[0]
@@ -1056,12 +1133,27 @@ async def run_daily_session(
             ):
                 total_cash = settings.PAPER_OVERSEAS_CASH
 
-        # Calculate daily P&L %
-        pnl_pct = (
-            ((total_eval - purchase_total) / purchase_total * 100)
-            if purchase_total > 0
-            else 0.0
-        )
+        # Capture the day's opening portfolio value on the first market processed
+        # in this session.  Used to compute intra-day P&L for the CB instead of
+        # the cumulative purchase_total which spans the entire account history.
+        if daily_start_eval <= 0 and total_eval > 0:
+            daily_start_eval = total_eval
+            logger.info(
+                "Daily CB baseline set: total_eval=%.2f (first balance of the day)",
+                daily_start_eval,
+            )
+
+        # Daily P&L: compare current eval vs start-of-day eval.
+        # Falls back to purchase_total if daily_start_eval is unavailable (e.g. paper
+        # mode where balance API returns 0 for all values).
+        if daily_start_eval > 0:
+            pnl_pct = (total_eval - daily_start_eval) / daily_start_eval * 100
+        else:
+            pnl_pct = (
+                ((total_eval - purchase_total) / purchase_total * 100)
+                if purchase_total > 0
+                else 0.0
+            )
         portfolio_data = {
             "portfolio_pnl_pct": pnl_pct,
             "total_cash": total_cash,
@@ -1330,6 +1422,7 @@ async def run_daily_session(
             )
 
     logger.info("Daily trading session completed")
+    return daily_start_eval
 
 
 async def _handle_market_close(
@@ -1965,13 +2058,26 @@ async def run(settings: Settings) -> None:
 
             session_interval = settings.SESSION_INTERVAL_HOURS * 3600  # Convert to seconds
 
+            # daily_start_eval: portfolio eval captured at the first session of each
+            # trading day.  Reset on calendar-date change so the CB measures only
+            # today's drawdown, not cumulative account history.
+            _cb_daily_start_eval: float = 0.0
+            _cb_last_date: str = ""
+
             while not shutdown.is_set():
                 # Wait for trading to be unpaused
                 await pause_trading.wait()
                 _run_context_scheduler(context_scheduler, now=datetime.now(UTC))
 
+                # Reset intra-day CB baseline on a new calendar date
+                today_str = datetime.now(UTC).date().isoformat()
+                if today_str != _cb_last_date:
+                    _cb_last_date = today_str
+                    _cb_daily_start_eval = 0.0
+                    logger.info("New trading day %s — daily CB baseline reset", today_str)
+
                 try:
-                    await run_daily_session(
+                    _cb_daily_start_eval = await run_daily_session(
                         broker,
                         overseas_broker,
                         scenario_engine,
@@ -1985,6 +2091,7 @@ async def run(settings: Settings) -> None:
                         telegram,
                         settings,
                         smart_scanner=smart_scanner,
+                        daily_start_eval=_cb_daily_start_eval,
                     )
                 except CircuitBreakerTripped:
                     logger.critical("Circuit breaker tripped — shutting down")

src/strategies/v20260220_210124_evolved.py

@@ -0,0 +1,114 @@
+"""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

@@ -0,0 +1,97 @@
+"""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

@@ -0,0 +1,88 @@
+"""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_main.py

@@ -18,9 +18,11 @@ from src.main import (
     _extract_held_codes_from_balance,
     _extract_held_qty_from_balance,
     _handle_market_close,
+    _retry_connection,
     _run_context_scheduler,
     _run_evolution_loop,
     _start_dashboard_server,
+    run_daily_session,
     safe_float,
     trading_cycle,
 )
@@ -3183,3 +3185,330 @@ class TestOverseasBrokerIntegration:
 
         # DB도 브로커도 보유 없음 → BUY 주문이 실행되어야 함 (회귀 테스트)
         overseas_broker.send_overseas_order.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# _retry_connection — unit tests (issue #209)
+# ---------------------------------------------------------------------------
+
+
+class TestRetryConnection:
+    """Unit tests for the _retry_connection helper (issue #209)."""
+
+    @pytest.mark.asyncio
+    async def test_success_on_first_attempt(self) -> None:
+        """Returns the result immediately when the first call succeeds."""
+        async def ok() -> str:
+            return "data"
+
+        result = await _retry_connection(ok, label="test")
+        assert result == "data"
+
+    @pytest.mark.asyncio
+    async def test_succeeds_after_one_connection_error(self) -> None:
+        """Retries once on ConnectionError and returns result on 2nd attempt."""
+        call_count = 0
+
+        async def flaky() -> str:
+            nonlocal call_count
+            call_count += 1
+            if call_count < 2:
+                raise ConnectionError("timeout")
+            return "ok"
+
+        with patch("src.main.asyncio.sleep") as mock_sleep:
+            mock_sleep.return_value = None
+            result = await _retry_connection(flaky, label="flaky")
+
+        assert result == "ok"
+        assert call_count == 2
+        mock_sleep.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_raises_after_all_retries_exhausted(self) -> None:
+        """Raises ConnectionError after MAX_CONNECTION_RETRIES attempts."""
+        from src.main import MAX_CONNECTION_RETRIES
+
+        call_count = 0
+
+        async def always_fail() -> None:
+            nonlocal call_count
+            call_count += 1
+            raise ConnectionError("unreachable")
+
+        with patch("src.main.asyncio.sleep") as mock_sleep:
+            mock_sleep.return_value = None
+            with pytest.raises(ConnectionError, match="unreachable"):
+                await _retry_connection(always_fail, label="always_fail")
+
+        assert call_count == MAX_CONNECTION_RETRIES
+
+    @pytest.mark.asyncio
+    async def test_passes_args_and_kwargs_to_factory(self) -> None:
+        """Forwards positional and keyword arguments to the callable."""
+        received: dict = {}
+
+        async def capture(a: int, b: int, *, key: str) -> str:
+            received["a"] = a
+            received["b"] = b
+            received["key"] = key
+            return "captured"
+
+        result = await _retry_connection(capture, 1, 2, key="val", label="test")
+        assert result == "captured"
+        assert received == {"a": 1, "b": 2, "key": "val"}
+
+    @pytest.mark.asyncio
+    async def test_non_connection_error_not_retried(self) -> None:
+        """Non-ConnectionError exceptions propagate immediately without retry."""
+        call_count = 0
+
+        async def bad_input() -> None:
+            nonlocal call_count
+            call_count += 1
+            raise ValueError("bad data")
+
+        with pytest.raises(ValueError, match="bad data"):
+            await _retry_connection(bad_input, label="bad")
+
+        assert call_count == 1  # No retry for non-ConnectionError
+
+
+# ---------------------------------------------------------------------------
+# run_daily_session — daily CB baseline (daily_start_eval) tests (issue #207)
+# ---------------------------------------------------------------------------
+
+
+class TestDailyCBBaseline:
+    """Tests for run_daily_session's daily_start_eval (CB baseline) behaviour.
+
+    Issue #207: CB P&L should be computed relative to the portfolio value at
+    the start of each trading day, not the cumulative purchase_total.
+    """
+
+    def _make_settings(self) -> Settings:
+        return Settings(
+            KIS_APP_KEY="test-key",
+            KIS_APP_SECRET="test-secret",
+            KIS_ACCOUNT_NO="12345678-01",
+            GEMINI_API_KEY="test-gemini",
+            MODE="paper",
+            PAPER_OVERSEAS_CASH=0,
+        )
+
+    def _make_domestic_balance(
+        self, tot_evlu_amt: float = 0.0, dnca_tot_amt: float = 50000.0
+    ) -> dict:
+        return {
+            "output1": [],
+            "output2": [
+                {
+                    "tot_evlu_amt": str(tot_evlu_amt),
+                    "dnca_tot_amt": str(dnca_tot_amt),
+                    "pchs_amt_smtl_amt": "40000.0",
+                }
+            ],
+        }
+
+    @pytest.mark.asyncio
+    async def test_returns_daily_start_eval_when_no_markets_open(self) -> None:
+        """run_daily_session returns the unchanged daily_start_eval when no markets are open."""
+        with patch("src.main.get_open_markets", return_value=[]):
+            result = await run_daily_session(
+                broker=MagicMock(),
+                overseas_broker=MagicMock(),
+                scenario_engine=MagicMock(),
+                playbook_store=MagicMock(),
+                pre_market_planner=MagicMock(),
+                risk=MagicMock(),
+                db_conn=init_db(":memory:"),
+                decision_logger=MagicMock(),
+                context_store=MagicMock(),
+                criticality_assessor=MagicMock(),
+                telegram=MagicMock(),
+                settings=self._make_settings(),
+                smart_scanner=None,
+                daily_start_eval=12345.0,
+            )
+        assert result == 12345.0
+
+    @pytest.mark.asyncio
+    async def test_returns_zero_when_no_markets_and_no_baseline(self) -> None:
+        """run_daily_session returns 0.0 when no markets are open and daily_start_eval=0."""
+        with patch("src.main.get_open_markets", return_value=[]):
+            result = await run_daily_session(
+                broker=MagicMock(),
+                overseas_broker=MagicMock(),
+                scenario_engine=MagicMock(),
+                playbook_store=MagicMock(),
+                pre_market_planner=MagicMock(),
+                risk=MagicMock(),
+                db_conn=init_db(":memory:"),
+                decision_logger=MagicMock(),
+                context_store=MagicMock(),
+                criticality_assessor=MagicMock(),
+                telegram=MagicMock(),
+                settings=self._make_settings(),
+                smart_scanner=None,
+                daily_start_eval=0.0,
+            )
+        assert result == 0.0
+
+    @pytest.mark.asyncio
+    async def test_captures_total_eval_as_baseline_on_first_session(self) -> None:
+        """When daily_start_eval=0 and balance returns a positive total_eval, the returned
+        value equals total_eval (the captured baseline for the day)."""
+        from src.analysis.smart_scanner import ScanCandidate
+
+        settings = self._make_settings()
+        broker = MagicMock()
+        # Domestic balance: tot_evlu_amt=55000
+        broker.get_balance = AsyncMock(
+            return_value=self._make_domestic_balance(tot_evlu_amt=55000.0)
+        )
+        # Price data for the stock
+        broker.get_current_price = AsyncMock(
+            return_value=(100.0, 1.5, 100.0)
+        )
+
+        market = MagicMock()
+        market.name = "KR"
+        market.code = "KR"
+        market.exchange_code = "KRX"
+        market.is_domestic = True
+        market.timezone = __import__("zoneinfo").ZoneInfo("Asia/Seoul")
+
+        smart_scanner = MagicMock()
+        smart_scanner.scan = AsyncMock(
+            return_value=[
+                ScanCandidate(
+                    stock_code="005930",
+                    name="Samsung",
+                    price=100.0,
+                    volume=1_000_000.0,
+                    volume_ratio=2.5,
+                    rsi=45.0,
+                    signal="momentum",
+                    score=80.0,
+                )
+            ]
+        )
+
+        playbook_store = MagicMock()
+        playbook_store.load = MagicMock(return_value=_make_playbook("KR"))
+
+        scenario_engine = MagicMock(spec=ScenarioEngine)
+        scenario_engine.evaluate = MagicMock(return_value=_make_hold_match("005930"))
+
+        risk = MagicMock()
+        risk.check_circuit_breaker = MagicMock()
+        risk.check_fat_finger = MagicMock()
+
+        telegram = MagicMock()
+        telegram.notify_trade_execution = AsyncMock()
+        telegram.notify_scenario_matched = AsyncMock()
+
+        decision_logger = MagicMock()
+        decision_logger.log_decision = MagicMock(return_value="d1")
+
+        async def _passthrough(fn, *a, label: str = "", **kw):  # type: ignore[override]
+            return await fn(*a, **kw)
+
+        with patch("src.main.get_open_markets", return_value=[market]), \
+             patch("src.main._retry_connection", new=_passthrough):
+            result = await run_daily_session(
+                broker=broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=scenario_engine,
+                playbook_store=playbook_store,
+                pre_market_planner=MagicMock(),
+                risk=risk,
+                db_conn=init_db(":memory:"),
+                decision_logger=decision_logger,
+                context_store=MagicMock(),
+                criticality_assessor=MagicMock(),
+                telegram=telegram,
+                settings=settings,
+                smart_scanner=smart_scanner,
+                daily_start_eval=0.0,
+            )
+
+        assert result == 55000.0  # captured from tot_evlu_amt
+
+    @pytest.mark.asyncio
+    async def test_does_not_overwrite_existing_baseline(self) -> None:
+        """When daily_start_eval > 0, it must not be overwritten even if balance returns
+        a different value (baseline is fixed at the start of each trading day)."""
+        from src.analysis.smart_scanner import ScanCandidate
+
+        settings = self._make_settings()
+        broker = MagicMock()
+        # Balance reports a different eval value (market moved during the day)
+        broker.get_balance = AsyncMock(
+            return_value=self._make_domestic_balance(tot_evlu_amt=58000.0)
+        )
+        broker.get_current_price = AsyncMock(return_value=(100.0, 1.5, 100.0))
+
+        market = MagicMock()
+        market.name = "KR"
+        market.code = "KR"
+        market.exchange_code = "KRX"
+        market.is_domestic = True
+        market.timezone = __import__("zoneinfo").ZoneInfo("Asia/Seoul")
+
+        smart_scanner = MagicMock()
+        smart_scanner.scan = AsyncMock(
+            return_value=[
+                ScanCandidate(
+                    stock_code="005930",
+                    name="Samsung",
+                    price=100.0,
+                    volume=1_000_000.0,
+                    volume_ratio=2.5,
+                    rsi=45.0,
+                    signal="momentum",
+                    score=80.0,
+                )
+            ]
+        )
+
+        playbook_store = MagicMock()
+        playbook_store.load = MagicMock(return_value=_make_playbook("KR"))
+
+        scenario_engine = MagicMock(spec=ScenarioEngine)
+        scenario_engine.evaluate = MagicMock(return_value=_make_hold_match("005930"))
+
+        risk = MagicMock()
+        risk.check_circuit_breaker = MagicMock()
+
+        telegram = MagicMock()
+        telegram.notify_trade_execution = AsyncMock()
+        telegram.notify_scenario_matched = AsyncMock()
+
+        decision_logger = MagicMock()
+        decision_logger.log_decision = MagicMock(return_value="d1")
+
+        async def _passthrough(fn, *a, label: str = "", **kw):  # type: ignore[override]
+            return await fn(*a, **kw)
+
+        with patch("src.main.get_open_markets", return_value=[market]), \
+             patch("src.main._retry_connection", new=_passthrough):
+            result = await run_daily_session(
+                broker=broker,
+                overseas_broker=MagicMock(),
+                scenario_engine=scenario_engine,
+                playbook_store=playbook_store,
+                pre_market_planner=MagicMock(),
+                risk=risk,
+                db_conn=init_db(":memory:"),
+                decision_logger=decision_logger,
+                context_store=MagicMock(),
+                criticality_assessor=MagicMock(),
+                telegram=telegram,
+                settings=settings,
+                smart_scanner=smart_scanner,
+                daily_start_eval=55000.0,  # existing baseline
+            )
+
+        # Must return the original baseline, NOT the new total_eval (58000)
+        assert result == 55000.0