关于wr指标与kdj的问题，我们总结了以下几点，给你解答：
wr指标与kdj指标的经典结合
1. 虽然三者都是以超买超卖为核心指标，但KDJ与RSI指标更能直观展示行情的走势（即rsi与kdj两指标走高代表现在会有上涨，走低会下跌）
2. WR指标在低位即买入信号，代表要上涨，WR指标在高位即卖出信号，代表下跌
3. 2者从不同的角度表达而已，不是你所说的背离
建议你就结合KDJ与boll判断区间，KDJ判断入场时间吧，你说的三个KDJ最为敏锐
如果你有兴趣，可以共同交流
理论上说kdj反应更灵敏，rsi次之。
wr指标与kdj
指标
# 参数：
# df：择时函数所需的数据
# m1：短期均线参数
# m2：长期均线参数
# m3：rsi参数
# m4：wr参数
# m5：kdj参数
# 返回：
# df：择时函数所需的数据
# 增加的指标：
# ma_short：短期均线
# ma_long：长期均线
# rsi：rsi指标
# wr：wr指标
# k：kdj指标
# d：kdj指标
# j：kdj指标
# 增加的信号：
# ma_signal：均线信号
# rsi_signal：rsi信号
# wr_signal：wr信号
# kdj_signal：kdj信号
# 增加的择时信号：
# signal：择时信号
# 增加的择时函数：
# select_time：择时函数
# 增加的择时函数参数：
# m1：短期均线参数
# m2：长期均线参数
# m3：rsi参数
# m4：wr参数
# m5：kdj参数
# 增加的择时函数返回：
# signal：择时信号
# 增加的择时函数参数：
# m1：短期均线参数
# m2：长期均线参数
# m3：rsi参数
# m4：wr参数
# m5：kdj参数
# 增加的择时函数返回：
# signal：择时信号
# 增加的择时函数参数：
# m1：短期均线参数
# m2：长期均线参数
# m3：rsi参数
# m4：wr参数
# m5：kdj参数
# 增加的择时函数返回：
# signal：择时信号
df['ma_short'] = talib.SMA(df['close'].values, m1)
df['ma_long'] = talib.SMA(df['close'].values, m2)
df['rsi'] = talib.RSI(df['close'].values, m3)
df['wr'] = talib.WILLR(df['high'].values, df['low'].values, df['close'].values, m4)
df['k'], df['d'] = talib.STOCH(df['high'].values, df['low'].values, df['close'].values, m5, m5)
df['j'] = 3 * df['k'] - 2 * df['d']
df['ma_signal'] = np.where(df['ma_short'] > df['ma_long'], 1, 0)
df['rsi_signal'] = np.where(df['rsi'] > 50, 1, 0)
df['wr_signal'] = np.where(df['wr'] < -50, 1, 0)
df['kdj_signal'] = np.where(df['k'] > df['d'], 1, 0)
df['signal'] = df['ma_signal'] + df['rsi_signal'] + df['wr_signal'] + df['kdj_signal']
df['signal'] = np.where(df['signal'] > 2, 1, 0)
df['signal'] = np.where(df['signal'] < 2, -1, df['signal'])
df['signal'] = np.where(df['signal'] == 2, 0, df['signal'])
df['signal'] = df['signal'].shift(1)
df['signal'] = np.where(df['signal'] == -1, 0, df['signal'])
df['signal'] = np.where(df['signal'] == 1, 1, df['signal'])
df['signal'] = np.where(df['signal'] == 0, 0, df['signal'])
df['signal'] = df['signal'].shift(1)
df['signal'] = np.where(df['signal'] == -1, 0, df['signal'])
df['signal'] = np.where(df['signal'] == 1, 1, df['signal'])
df['signal'] = np.where(df['signal'] == 0, 0, df['signal'])
df['signal'] = df['signal'].shift(1)
df['signal'] = np.where(df['signal'] == -1, 0, df['signal'])
df['signal'] = np.where(df['signal'] == 1, 1, df['signal'])
df['signal'] = np.where(df['signal'] == 0, 0, df['signal'])
df['signal'] = df['signal'].shift(1)
df['signal'] = np.where(df['signal'] == -1, 0, df['signal'])
df['signal'] = np.where(df['signal'] == 1, 1, df['signal'])
df['signal'] = np.where(df['signal'] == 0, 0, df['signal'])
df['signal'] = df['signal'].shift(1)
df['signal'] = np.where(df['signal'] == -1, 0, df['signal'])
df['signal'] = np.where(df['signal'] == 1, 1, df['signal'])
df['signal'] = np.where(df['signal'] == 0, 0, df['signal'])
df['signal'] = df['signal'].shift(1)
df['signal'] = np.where(df['signal'] == -1, 0, df['signal'])
df['signal'] = np.where(df['signal'] == 1, 1, df['signal'])
df['signal'] = np.where(df['signal'] == 0, 0, df['signal'])
df['signal'] = df['signal'].shift(1)
df['signal'] = np.where(df['signal'] == -1, 0, df['signal'])
df['signal'] = np.where(df['signal'] == 1, 1, df['signal'])
df['signal'] = np.where(df['signal'] == 0, 0, df['signal
wr指标与kdj指标结合
你把WR货民保均态律指标翻转（右键勾选“翻转坐标”），和KDJ对应了看，就很清楚了。
但要注意，KDJ和WR指标，2个都不是很精准的，发出的信号过多，适合短线操作
WR一般只和RSI 快鲜、TMT配合，效果展信压比较好
