1 files changed, 104 insertions, 0 deletions
diff --git a/services/strategy-engine/strategies/indicators/trend.py b/services/strategy-engine/strategies/indicators/trend.py
new file mode 100644
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+++ b/services/strategy-engine/strategies/indicators/trend.py
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+"""Trend indicators: EMA, SMA, MACD, ADX."""
+import pandas as pd
+import numpy as np
+
+
+def sma(series: pd.Series, period: int) -> pd.Series:
+    """Simple Moving Average."""
+    return series.rolling(window=period).mean()
+
+
+def ema(series: pd.Series, period: int) -> pd.Series:
+    """Exponential Moving Average."""
+    return series.ewm(span=period, adjust=False).mean()
+
+
+def macd(
+    closes: pd.Series,
+    fast: int = 12,
+    slow: int = 26,
+    signal: int = 9,
+) -> tuple[pd.Series, pd.Series, pd.Series]:
+    """MACD indicator.
+
+    Returns: (macd_line, signal_line, histogram)
+    """
+    fast_ema = ema(closes, fast)
+    slow_ema = ema(closes, slow)
+    macd_line = fast_ema - slow_ema
+    signal_line = ema(macd_line, signal)
+    histogram = macd_line - signal_line
+    return macd_line, signal_line, histogram
+
+
+def adx(
+    highs: pd.Series,
+    lows: pd.Series,
+    closes: pd.Series,
+    period: int = 14,
+) -> pd.Series:
+    """Average Directional Index (ADX).
+
+    Returns ADX series. Values > 25 indicate strong trend, < 20 indicate ranging.
+    """
+    high = highs.values
+    low = lows.values
+    close = closes.values
+    n = len(close)
+
+    if n < period + 1:
+        return pd.Series([np.nan] * n)
+
+    # True Range
+    tr = np.zeros(n)
+    tr[0] = high[0] - low[0]
+    for i in range(1, n):
+        tr[i] = max(
+            high[i] - low[i],
+            abs(high[i] - close[i - 1]),
+            abs(low[i] - close[i - 1]),
+        )
+
+    # Directional Movement
+    plus_dm = np.zeros(n)
+    minus_dm = np.zeros(n)
+    for i in range(1, n):
+        up = high[i] - high[i - 1]
+        down = low[i - 1] - low[i]
+        plus_dm[i] = up if up > down and up > 0 else 0.0
+        minus_dm[i] = down if down > up and down > 0 else 0.0
+
+    # Smoothed with Wilder's method
+    atr_vals = np.zeros(n)
+    plus_di = np.zeros(n)
+    minus_di = np.zeros(n)
+
+    atr_vals[period] = np.mean(tr[1 : period + 1])
+    plus_smooth = np.mean(plus_dm[1 : period + 1])
+    minus_smooth = np.mean(minus_dm[1 : period + 1])
+
+    if atr_vals[period] > 0:
+        plus_di[period] = 100 * plus_smooth / atr_vals[period]
+        minus_di[period] = 100 * minus_smooth / atr_vals[period]
+
+    for i in range(period + 1, n):
+        atr_vals[i] = (atr_vals[i - 1] * (period - 1) + tr[i]) / period
+        plus_smooth = (plus_smooth * (period - 1) + plus_dm[i]) / period
+        minus_smooth = (minus_smooth * (period - 1) + minus_dm[i]) / period
+        if atr_vals[i] > 0:
+            plus_di[i] = 100 * plus_smooth / atr_vals[i]
+            minus_di[i] = 100 * minus_smooth / atr_vals[i]
+
+    # DX and ADX
+    dx = np.zeros(n)
+    for i in range(period, n):
+        di_sum = plus_di[i] + minus_di[i]
+        dx[i] = 100 * abs(plus_di[i] - minus_di[i]) / di_sum if di_sum > 0 else 0.0
+
+    adx_vals = np.full(n, np.nan)
+    if 2 * period < n:
+        adx_vals[2 * period] = np.mean(dx[period : 2 * period + 1])
+        for i in range(2 * period + 1, n):
+            adx_vals[i] = (adx_vals[i - 1] * (period - 1) + dx[i]) / period
+
+    return pd.Series(adx_vals, index=closes.index if hasattr(closes, 'index') else None)