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Ind.py

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+# Ind.py
+import pandas as pd
+import numpy as np
+
+
+def MA(data, N1, N2, N3):
+    # 计算不同周期的移动平均
+    MAN1 = data['close'].rolling(window=N1).mean()
+    MAN2 = data['close'].rolling(window=N2).mean()
+    MAN3 = data['close'].rolling(window=N3).mean()
+
+    return (MAN1, MAN2, MAN3)
+
+
+def MACD(data):
+    # 计算指数平滑移动平均线EMA
+    EMA12 = data['close'].ewm(span=12, adjust=False).mean()
+    EMA26 = data['close'].ewm(span=26, adjust=False).mean()
+    DIF = EMA12 - EMA26
+    DEA = DIF.ewm(span=9, adjust=False).mean()
+    MACD = (DIF - DEA) * 2
+
+    return MACD
+
+
+def KDJ(data, N):
+    # 计算随机指标KDJ
+    if len(data) < N:
+        print(f"Insufficient data length for KDJ calculation. Expected at least {N} days, got {len(data)} days.")
+        return None, None, None
+
+    Lmin = data['low'].rolling(window=N).min()
+    Lmax = data['high'].rolling(window=N).max()
+    RSV = (data['close'] - Lmin) / (Lmax - Lmin) * 100
+
+    # 确保 RSV 序列的索引与 data 的索引一致
+    RSV = RSV.reindex(data.index)
+
+    K = np.zeros(len(RSV))
+    D = np.zeros(len(RSV))
+    J = np.zeros(len(RSV))
+
+    for t in range(N, len(data)):
+        if t == N:
+            K[t] = RSV[t]
+            D[t] = RSV[t]
+        else:
+            K[t] = (2 / 3) * K[t - 1] + (1 / 3) * RSV[t]
+            D[t] = (2 / 3) * D[t - 1] + (1 / 3) * K[t]
+        J[t] = 3 * D[t] - 2 * K[t]
+
+    return (pd.Series(K, index=data.index), pd.Series(D, index=data.index), pd.Series(J, index=data.index))
+
+
+def RSI(data, N):
+    # 计算相对强弱指标RSI
+    delta = data['close'].diff(1)
+    gain = (delta.where(delta > 0, 0)).rolling(window=N).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=N).mean()
+    RS = gain / loss
+    RSI = 100 - (100 / (1 + RS))
+
+    return RSI
+
+
+def BIAS(data, N):
+    # 计算乖离率BIAS
+    MA = data['close'].rolling(window=N).mean()
+    BIAS = (data['close'] - MA) / MA * 100
+
+    return BIAS
+
+
+def OBV(data):
+    # 计算能量潮OBV
+    obv = (data['vol'] * data['close'].diff().apply(lambda x: 1 if x > 0 else -1 if x < 0 else 0)).cumsum()
+
+    return obv
+
+
+def cla(data):
+    # 计算涨跌趋势
+    y = data['close'].diff().apply(lambda x: 1 if x > 0 else -1 if x < 0 else 0)
+
+    return y