refactor(phase2): Add DayTradingStrategy example and indicators module

backend/app/strategy/indicators.py

@@ -0,0 +1,158 @@
+"""
+Technical Indicators
+
+보조지표 계산 함수들
+"""
+from typing import List, Optional, Tuple
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def sma(prices: List[float], period: int) -> Optional[float]:
+    """단순 이동평균 (Simple Moving Average)"""
+    if len(prices) < period:
+        return None
+    return sum(prices[-period:]) / period
+
+
+def ema(prices: List[float], period: int, prev_ema: Optional[float] = None) -> Optional[float]:
+    """지수 이동평균 (Exponential Moving Average)"""
+    if len(prices) < period:
+        return None
+    
+    k = 2 / (period + 1)
+    if prev_ema is None:
+        # 초기값은 SMA
+        prev_ema = sum(prices[-period:]) / period
+    
+    return prices[-1] * k + prev_ema * (1 - k)
+
+
+def rsi(prices: List[float], period: int = 14) -> Optional[float]:
+    """
+RSI (Relative Strength Index)
+    
+    Args:
+        prices: 종가 리스트
+        period: 기간 (기본 14)
+    
+    Returns:
+        0~100 사이 RSI 값
+    """
+    if len(prices) < period + 1:
+        return None
+    
+    gains, losses = [], []
+    for i in range(-period, 0):
+        delta = prices[i] - prices[i - 1]
+        gains.append(max(delta, 0))
+        losses.append(max(-delta, 0))
+    
+    avg_gain = sum(gains) / period
+    avg_loss = sum(losses) / period
+    
+    if avg_loss == 0:
+        return 100.0
+    
+    rs = avg_gain / avg_loss
+    return 100 - (100 / (1 + rs))
+
+
+def bollinger_bands(prices: List[float], period: int = 20, std_mult: float = 2.0) -> Optional[Tuple[float, float, float]]:
+    """
+    볼린저 밴드
+    
+    Returns:
+        (upper, middle, lower) 또는 None
+    """
+    if len(prices) < period:
+        return None
+    
+    window = prices[-period:]
+    middle = sum(window) / period
+    variance = sum((p - middle) ** 2 for p in window) / period
+    std = variance ** 0.5
+    
+    upper = middle + std_mult * std
+    lower = middle - std_mult * std
+    
+    return (upper, middle, lower)
+
+
+def macd(prices: List[float], fast: int = 12, slow: int = 26, signal: int = 9) -> Optional[Tuple[float, float, float]]:
+    """
+    MACD (Moving Average Convergence Divergence)
+    
+    Returns:
+        (macd_line, signal_line, histogram) 또는 None
+    """
+    if len(prices) < slow:
+        return None
+    
+    # EMA 계산 (재귀적으로 계산)
+    def calc_ema(data: List[float], period: int) -> float:
+        k = 2 / (period + 1)
+        ema_val = sum(data[:period]) / period
+        for p in data[period:]:
+            ema_val = p * k + ema_val * (1 - k)
+        return ema_val
+    
+    ema_fast = calc_ema(prices, fast)
+    ema_slow = calc_ema(prices, slow)
+    macd_line = ema_fast - ema_slow
+    
+    # Signal line = MACD의 EMA(9)
+    # 실제로는 과거 MACD 값들 필요 (간략화)
+    signal_line = macd_line  # TODO: 정확한 구현 필요
+    histogram = macd_line - signal_line
+    
+    return (macd_line, signal_line, histogram)
+
+
+def atr(highs: List[float], lows: List[float], closes: List[float], period: int = 14) -> Optional[float]:
+    """
+    ATR (Average True Range)
+    
+    Args:
+        highs: 고가 리스트
+        lows: 저가 리스트
+        closes: 종가 리스트
+        period: 기간
+    
+    Returns:
+        ATR 값
+    """
+    if len(highs) < period + 1 or len(lows) < period + 1 or len(closes) < period + 1:
+        return None
+    
+    trs = []
+    for i in range(1, len(highs)):
+        h = highs[i]
+        l = lows[i]
+        cp = closes[i - 1]
+        tr = max(h - l, abs(h - cp), abs(l - cp))
+        trs.append(tr)
+    
+    if len(trs) < period:
+        return None
+    
+    return sum(trs[-period:]) / period
+
+
+def vwap(prices: List[float], volumes: List[float]) -> Optional[float]:
+    """
+    VWAP (Volume Weighted Average Price)
+    
+    Returns:
+        VWAP 값
+    """
+    if len(prices) != len(volumes) or not prices:
+        return None
+    
+    total_volume = sum(volumes)
+    if total_volume == 0:
+        return None
+    
+    total_pv = sum(p * v for p, v in zip(prices, volumes))
+    return total_pv / total_volume