我有一个数据框df,它包含140万行数据,每行代表2018年至2020年BTC开盘、高价、低价和收盘价的1分钟。我想将MACD(常用交易指标)添加到我的df中,但不是只计算1分钟时间范围内的MACD,如下图所示:
ShortEMA = df.Close.ewm(span=12, adjust=False).mean()
LongEMA = df.Close.ewm(span=26, adjust=False).mean()
MACD = ShortEMA - LongEMA
signal = MACD.ewm(span=9, adjust=False).mean()
df["MACD"] = MACD
df["Signal Line"] = signal
我想计算每个时间段的MACD,1分钟,15分钟,30分钟,1小时,等等
我使用以下代码(花费了很长时间):
MySet = [1, 5, 15, 30, 60, 240, 360, 720, 1440, 10080]
ShortEMA1 = df.Close.ewm(span=12 * MySet[0], adjust=False).mean()
LongEMA1 = df.Close.ewm(span=26 * MySet[0], adjust=False).mean()
MACD1 = ShortEMA1 - LongEMA1
signal1 = MACD.ewm(span=9 * MySet[0], adjust=False).mean()
ShortEMA5 = df.Close.ewm(span=12 * MySet[1], adjust=False).mean()
LongEMA5 = df.Close.ewm(span=26 * MySet[1], adjust=False).mean()
MACD5 = ShortEMA5 - LongEMA5
signal5 = MACD.ewm(span=9 * MySet[1], adjust=False).mean()
ShortEMA15 = df.Close.ewm(span=12 * MySet[2], adjust=False).mean()
LongEMA15 = df.Close.ewm(span=26 * MySet[2], adjust=False).mean()
MACD15 = ShortEMA15 - LongEMA15
signal15 = MACD.ewm(span=9 * MySet[2], adjust=False).mean()
ShortEMA30 = df.Close.ewm(span=12 * MySet[3], adjust=False).mean()
LongEMA30 = df.Close.ewm(span=26 * MySet[3], adjust=False).mean()
MACD30 = ShortEMA30 - LongEMA30
signal30 = MACD.ewm(span=9 * MySet[3], adjust=False).mean()
ShortEMA60 = df.Close.ewm(span=12 * MySet[4], adjust=False).mean()
LongEMA60 = df.Close.ewm(span=26 * MySet[4], adjust=False).mean()
MACD60 = ShortEMA60 - LongEMA60
signal60 = MACD.ewm(span=9 * MySet[4], adjust=False).mean()
ShortEMA240 = df.Close.ewm(span=12 * MySet[5], adjust=False).mean()
LongEMA240 = df.Close.ewm(span=26 * MySet[5], adjust=False).mean()
MACD240 = ShortEMA240 - LongEMA240
signal240 = MACD.ewm(span=9 * MySet[5], adjust=False).mean()
ShortEMA360 = df.Close.ewm(span=12 * MySet[6], adjust=False).mean()
LongEMA360 = df.Close.ewm(span=26 * MySet[6], adjust=False).mean()
MACD360 = ShortEMA360 - LongEMA360
signal360 = MACD.ewm(span=9 * MySet[6], adjust=False).mean()
ShortEMA720 = df.Close.ewm(span=12 * MySet[7], adjust=False).mean()
LongEMA720 = df.Close.ewm(span=26 * MySet[7], adjust=False).mean()
MACD720 = ShortEMA720 - LongEMA720
signal720 = MACD.ewm(span=9 * MySet[7], adjust=False).mean()
ShortEMA1440 = df.Close.ewm(span=12 * MySet[8], adjust=False).mean()
LongEMA1440 = df.Close.ewm(span=26 * MySet[8], adjust=False).mean()
MACD1440 = ShortEMA1440 - LongEMA1440
signal1440 = MACD.ewm(span=9 * MySet[8], adjust=False).mean()
ShortEMA10080 = df.Close.ewm(span=12 * MySet[9], adjust=False).mean()
LongEMA10080 = df.Close.ewm(span=26 * MySet[9], adjust=False).mean()
MACD10080 = ShortEMA10080 - LongEMA10080
signal10080 = MACD.ewm(span=9 * MySet[9], adjust=False).mean()
df["MACD1"] = MACD1
df["Signal Line1"] = signal1
df["MACD5"] = MACD1
df["Signal Line5"] = signal5
df["MACD15"] = MACD1
df["Signal Line15"] = signal15
df["MACD30"] = MACD1
df["Signal Line30"] = signal30
df["MACD60"] = MACD60
df["Signal Line60"] = signal60
df["MACD240"] = MACD240
df["Signal Line240"] = signal240
df["MACD360"] = MACD360
df["Signal Line360"] = signal360
df["MACD720"] = MACD720
df["Signal Line720"] = signal720
df["MACD1440"] = MACD1440
df["Signal Line1440"] = signal1440
df["MACD10080"] = MACD10080
df["Signal Line10080"] = signal10080
如何简化整个过程
如果唯一的持久性输出是存储在
DataFrame
和中间Series
中的值,以后不使用或重复使用,则最好在每次迭代中简单地更新数据帧并为列分配f-string
:如果以后需要访问值,可以通过
DataFrame
访问这些值与其让}字典,让
Short1
和Short5
成为单独的变量,不如让^{有一个{1
、5
等成为键。因此:相关问题 更多 >
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