如何解决如何将使用不同参数运行相同计算的代码简化为不同的输出变量?
我有一个数据框 df,其中包含 140 万行数据,其中每一行代表 1 分钟 BTC 从 2018 年到 2020 年的开盘价、最高价、最低价和收盘价。我想添加 MACD(流行交易指标) ) 到我的 df,而不是像这样只计算 1 分钟时间范围内的 macd:
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
我想计算每个时间范围 1 分钟、15 分钟、30 分钟、1 小时等的 MACD...
我使用以下代码(花费了很长时间)做到了这一点:
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?
解决方法
不是让 Short1
和 Short5
是单独的变量,而是有 one Shorts
字典,并有 1
,{{ 1}} 等是键。因此:
5
,
如果唯一的持久输出是存储在 DataFrame
中的值,而中间的 Series
不会被使用或稍后重新使用,那么简单地更新 DataFrame 并分配列可能会更好在每次迭代中使用 f-string
:
MySet = [1,5,15,30,60,240,360,720,1440,10080]
for val in MySet:
ShortEMA = df.Close.ewm(span=12 * val,adjust=False).mean()
LongEMA = df.Close.ewm(span=26 * val,adjust=False).mean()
df[f"MACD{val}"] = ShortEMA - LongEMA
df[f"Signal Line{val}"] = df[f"MACD{val}"].ewm(span=9 * val,adjust=False).mean()
如果以后需要访问值,可以通过 DataFrame
访问它们。
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