You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
86 lines
2.3 KiB
86 lines
2.3 KiB
# 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
|