关于成交量绿色s的问题，我们总结了以下几点，给你解答：
成交量绿色是买还是卖
表示买和卖的总成交量，如果当收阳线，成交量柱用红色，收阴线用绿色表示。用红色或绿色是根据当日收阴收阳决定的。（其实会有人误解为红色表示买的量，绿色表示卖的量，这是错误的理解）
成交量绿色s
# 均线
ma5 = talib.MA(close, timeperiod=5, matype=0)
ma10 = talib.MA(close, timeperiod=10, matype=0)
ma20 = talib.MA(close, timeperiod=20, matype=0)
ma30 = talib.MA(close, timeperiod=30, matype=0)
ma60 = talib.MA(close, timeperiod=60, matype=0)
# 动量
mtm6 = talib.MOM(close, timeperiod=6)
mtm12 = talib.MOM(close, timeperiod=12)
# 威廉指标
willr = talib.WILLR(high, low, close, timeperiod=14)
# 随机指标
rsi6 = talib.RSI(close, timeperiod=6)
rsi12 = talib.RSI(close, timeperiod=12)
# 平均真实波动率
atr = talib.ATR(high, low, close, timeperiod=14)
# 平均趋向指标
adx = talib.ADX(high, low, close, timeperiod=14)
# 布林带
upperband, middleband, lowerband = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
# 技术指标
macd, macdsignal, macdhist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
# 将技术指标合并到DataFrame中
df['ma5'] = ma5
df['ma10'] = ma10
df['ma20'] = ma20
df['ma30'] = ma30
df['ma60'] = ma60
df['mtm6'] = mtm6
df['mtm12'] = mtm12
df['willr'] = willr
df['rsi6'] = rsi6
df['rsi12'] = rsi12
df['atr'] = atr
df['adx'] = adx
df['upperband'] = upperband
df['middleband'] = middleband
df['lowerband'] = lowerband
df['macd'] = macd
df['macdsignal'] = macdsignal
df['macdhist'] = macdhist
# 将技术指标转换为百分比
df = df.apply(lambda x: x / x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - 1) * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨跌
df = df.apply(lambda x: x - x[0])
# 将技术指标转换为涨跌幅
df = df.apply(lambda x: (x - x[0]) / x[0] * 100)
# 将技术指标转换为涨
成交量绿色数字代表什么
展开1全部 绿色的代表主动卖出，红色的代表主动买入。柱体的长度代表成交量大小，越长则量越大，反之则越校主动买入就是平常说的资金流毫均探认口叶建入，是指主动性买盘的成交金额，俗称外盘。主动卖出就是平常说的资金流出，是指主动性卖盘的成交金额，俗称内盘。 成交.松注亮..
