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