用ny进行MACD、KDJ、RSI的代码实现

以下是使用Python和ny进行MACD、KDJ、RSI指标计算的代码实现：

1. MACD指标计算代码：

import numpy as np
import pandas as pd

def MACD(df, n_fast=12, n_slow=26, n_signal=9):
    """
    计算MACD指标
    """
    EMAfast = df['close'].ewm(span=n_fast, min_periods=n_fast - 1).mean()
    EMAslow = df['close'].ewm(span=n_slow, min_periods=n_slow - 1).mean()
    MACD = EMAfast - EMAslow
    signal = MACD.ewm(span=n_signal, min_periods=n_signal - 1).mean()
    histogram = MACD - signal
    return MACD, signal, histogram

2. KDJ指标计算代码：

import numpy as np
import pandas as pd

def KDJ(df, n=9):
    """
    计算KDJ指标
    """
    low_list = df['low'].rolling(window=n, min_periods=n).min()
    high_list = df['high'].rolling(window=n, min_periods=n).max()
    rsv = (df['close'] - low_list) / (high_list - low_list) * 100
    K = rsv.ewm(span=3, adjust=False).mean()
    D = K.ewm(span=3, adjust=False).mean()
    J = 3 * K - 2 * D
    return K, D, J

3. RSI指标计算代码：

import numpy as np
import pandas as pd

def RSI(df, n=14):
    """
    计算RSI指标
    """
    delta = df['close'].diff()
    up, down = delta.copy(), delta.copy()
    up[up < 0] = 0
    down[down > 0] = 0
    gain = up.rolling(window=n, min_periods=n).mean()
    loss = abs(down.rolling(window=n, min_periods=n).mean())
    RS = gain / loss
    RSI = 100.0 - (100.0 / (1.0 + RS))
    return RSI

以上代码实现了MACD、KDJ、RSI三个指标的计算，使用时需要传入包含股票价格信息的DataFrame，例如：

df = pd.read_csv('stock.csv')
MACD(df)
KDJ(df)
RSI(df)

其中'stock.csv'是包含股票价格信息的CSV文件，包含'open'、'high'、'low'、'close'等列。