feat: implement Smart Volatility Scanner with RSI/volume filters (issue #76)

Add Python-first scanning pipeline that reduces Gemini API calls by filtering
stocks before AI analysis: KIS rankings API -> RSI/volume filter -> AI judgment.

## Implementation
- Add RSI calculation (Wilder's smoothing method) to VolatilityAnalyzer
- Add KIS API methods: fetch_market_rankings() and get_daily_prices()
- Create SmartVolatilityScanner with configurable thresholds
- Integrate scanner into main.py realtime mode
- Add selection_context logging to trades table for Evolution system

## Configuration
- RSI_OVERSOLD_THRESHOLD: 30 (configurable 0-50)
- RSI_MOMENTUM_THRESHOLD: 70 (configurable 50-100)
- VOL_MULTIPLIER: 2.0 (minimum volume ratio, configurable 1-10)
- SCANNER_TOP_N: 3 (max candidates per scan, configurable 1-10)

## Benefits
- Reduces Gemini API calls (process 1-3 qualified stocks vs 20-30 ranked)
- Python-based technical filtering before expensive AI judgment
- Tracks selection criteria (RSI, volume_ratio, signal, score) for strategy optimization
- Graceful fallback to static watchlist if ranking API fails

## Tests
- 13 new tests for SmartVolatilityScanner and RSI calculation
- All existing tests updated and passing
- Coverage maintained at 73%

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

src/analysis/__init__.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 from src.analysis.scanner import MarketScanner
+from src.analysis.smart_scanner import ScanCandidate, SmartVolatilityScanner
 from src.analysis.volatility import VolatilityAnalyzer
 
-__all__ = ["VolatilityAnalyzer", "MarketScanner"]
+__all__ = ["VolatilityAnalyzer", "MarketScanner", "SmartVolatilityScanner", "ScanCandidate"]

src/analysis/smart_scanner.py

@@ -0,0 +1,192 @@
+"""Smart Volatility Scanner with RSI and volume filters.
+
+Fetches market rankings from KIS API and applies technical filters
+to identify high-probability trading candidates.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from typing import Any
+
+from src.analysis.volatility import VolatilityAnalyzer
+from src.broker.kis_api import KISBroker
+from src.config import Settings
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ScanCandidate:
+    """A qualified candidate from the smart scanner."""
+
+    stock_code: str
+    name: str
+    price: float
+    volume: float
+    volume_ratio: float  # Current volume / previous day volume
+    rsi: float
+    signal: str  # "oversold" or "momentum"
+    score: float  # Composite score for ranking
+
+
+class SmartVolatilityScanner:
+    """Scans market rankings and applies RSI/volume filters.
+
+    Flow:
+    1. Fetch volume rankings from KIS API
+    2. For each ranked stock, fetch daily prices
+    3. Calculate RSI and volume ratio
+    4. Apply filters: volume > VOL_MULTIPLIER AND (RSI < 30 OR RSI > 70)
+    5. Return top N qualified candidates
+    """
+
+    def __init__(
+        self,
+        broker: KISBroker,
+        volatility_analyzer: VolatilityAnalyzer,
+        settings: Settings,
+    ) -> None:
+        """Initialize the smart scanner.
+
+        Args:
+            broker: KIS broker for API calls
+            volatility_analyzer: Analyzer for RSI calculation
+            settings: Application settings
+        """
+        self.broker = broker
+        self.analyzer = volatility_analyzer
+        self.settings = settings
+
+        # Extract scanner settings
+        self.rsi_oversold = settings.RSI_OVERSOLD_THRESHOLD
+        self.rsi_momentum = settings.RSI_MOMENTUM_THRESHOLD
+        self.vol_multiplier = settings.VOL_MULTIPLIER
+        self.top_n = settings.SCANNER_TOP_N
+
+    async def scan(
+        self,
+        fallback_stocks: list[str] | None = None,
+    ) -> list[ScanCandidate]:
+        """Execute smart scan and return qualified candidates.
+
+        Args:
+            fallback_stocks: Stock codes to use if ranking API fails
+
+        Returns:
+            List of ScanCandidate, sorted by score, up to top_n items
+        """
+        # Step 1: Fetch rankings
+        try:
+            rankings = await self.broker.fetch_market_rankings(
+                ranking_type="volume",
+                limit=30,  # Fetch more than needed for filtering
+            )
+            logger.info("Fetched %d stocks from volume rankings", len(rankings))
+        except ConnectionError as exc:
+            logger.warning("Ranking API failed, using fallback: %s", exc)
+            if fallback_stocks:
+                # Create minimal ranking data for fallback
+                rankings = [
+                    {
+                        "stock_code": code,
+                        "name": code,
+                        "price": 0,
+                        "volume": 0,
+                        "change_rate": 0,
+                        "volume_increase_rate": 0,
+                    }
+                    for code in fallback_stocks
+                ]
+            else:
+                return []
+
+        # Step 2: Analyze each stock
+        candidates: list[ScanCandidate] = []
+
+        for stock in rankings:
+            stock_code = stock["stock_code"]
+            if not stock_code:
+                continue
+
+            try:
+                # Fetch daily prices for RSI calculation
+                daily_prices = await self.broker.get_daily_prices(stock_code, days=20)
+
+                if len(daily_prices) < 15:  # Need at least 14+1 for RSI
+                    logger.debug("Insufficient price history for %s", stock_code)
+                    continue
+
+                # Calculate RSI
+                close_prices = [p["close"] for p in daily_prices]
+                rsi = self.analyzer.calculate_rsi(close_prices, period=14)
+
+                # Calculate volume ratio (today vs previous day avg)
+                if len(daily_prices) >= 2:
+                    prev_day_volume = daily_prices[-2]["volume"]
+                    current_volume = stock.get("volume", 0) or daily_prices[-1]["volume"]
+                    volume_ratio = (
+                        current_volume / prev_day_volume if prev_day_volume > 0 else 1.0
+                    )
+                else:
+                    volume_ratio = stock.get("volume_increase_rate", 0) / 100 + 1  # Fallback
+
+                # Apply filters
+                volume_qualified = volume_ratio >= self.vol_multiplier
+                rsi_oversold = rsi < self.rsi_oversold
+                rsi_momentum = rsi > self.rsi_momentum
+
+                if volume_qualified and (rsi_oversold or rsi_momentum):
+                    signal = "oversold" if rsi_oversold else "momentum"
+
+                    # Calculate composite score
+                    # Higher score for: extreme RSI + high volume
+                    rsi_extremity = abs(rsi - 50) / 50  # 0-1 scale
+                    volume_score = min(volume_ratio / 5, 1.0)  # Cap at 5x
+                    score = (rsi_extremity * 0.6 + volume_score * 0.4) * 100
+
+                    candidates.append(
+                        ScanCandidate(
+                            stock_code=stock_code,
+                            name=stock.get("name", stock_code),
+                            price=stock.get("price", daily_prices[-1]["close"]),
+                            volume=current_volume,
+                            volume_ratio=volume_ratio,
+                            rsi=rsi,
+                            signal=signal,
+                            score=score,
+                        )
+                    )
+
+                    logger.info(
+                        "Qualified: %s (%s) RSI=%.1f vol=%.1fx signal=%s score=%.1f",
+                        stock_code,
+                        stock.get("name", ""),
+                        rsi,
+                        volume_ratio,
+                        signal,
+                        score,
+                    )
+
+            except ConnectionError as exc:
+                logger.warning("Failed to analyze %s: %s", stock_code, exc)
+                continue
+            except Exception as exc:
+                logger.error("Unexpected error analyzing %s: %s", stock_code, exc)
+                continue
+
+        # Sort by score and return top N
+        candidates.sort(key=lambda c: c.score, reverse=True)
+        return candidates[: self.top_n]
+
+    def get_stock_codes(self, candidates: list[ScanCandidate]) -> list[str]:
+        """Extract stock codes from candidates for watchlist update.
+
+        Args:
+            candidates: List of scan candidates
+
+        Returns:
+            List of stock codes
+        """
+        return [c.stock_code for c in candidates]

src/analysis/volatility.py

@@ -124,6 +124,54 @@ class VolatilityAnalyzer:
             return 1.0
         return current_volume / avg_volume
 
+    def calculate_rsi(
+        self,
+        close_prices: list[float],
+        period: int = 14,
+    ) -> float:
+        """Calculate Relative Strength Index (RSI) using Wilder's smoothing.
+
+        Args:
+            close_prices: List of closing prices (oldest to newest, minimum period+1 values)
+            period: RSI period (default 14)
+
+        Returns:
+            RSI value between 0 and 100, or 50.0 (neutral) if insufficient data
+
+        Examples:
+            >>> analyzer = VolatilityAnalyzer()
+            >>> prices = [100 - i * 0.5 for i in range(20)]  # Downtrend
+            >>> rsi = analyzer.calculate_rsi(prices)
+            >>> assert rsi < 50  # Oversold territory
+        """
+        if len(close_prices) < period + 1:
+            return 50.0  # Neutral RSI if insufficient data
+
+        # Calculate price changes
+        changes = [close_prices[i] - close_prices[i - 1] for i in range(1, len(close_prices))]
+
+        # Separate gains and losses
+        gains = [max(0.0, change) for change in changes]
+        losses = [max(0.0, -change) for change in changes]
+
+        # Calculate initial average gain/loss (simple average for first period)
+        avg_gain = sum(gains[:period]) / period
+        avg_loss = sum(losses[:period]) / period
+
+        # Apply Wilder's smoothing for remaining periods
+        for i in range(period, len(changes)):
+            avg_gain = (avg_gain * (period - 1) + gains[i]) / period
+            avg_loss = (avg_loss * (period - 1) + losses[i]) / period
+
+        # Calculate RS and RSI
+        if avg_loss == 0:
+            return 100.0  # All gains, maximum RSI
+
+        rs = avg_gain / avg_loss
+        rsi = 100 - (100 / (1 + rs))
+
+        return rsi
+
     def calculate_pv_divergence(
         self,
         price_change: float,