如何解决缩短长 Python 指标
这是我编写的代码，用于帮助我定义策略。它运行良好，但我觉得代码太长，我想知道是否有一种有效的方法可以对 for 循环执行相同的操作。 该代码搜索一组烛台，其实体包含在另一个先前的柱内。用我笨拙的方法，我只能设计最少 2 根蜡烛和最多 7 根蜡烛。是否可以使用 for 循环来获取尽可能多的满足此条件的蜡烛？ 提前致谢。
def eATR(df1,n=14):
"""This calculates the Average True Range of of a dataframe of the open,high,low,and close data of an instrument"""
df = df1[['Open','High','Low','Close',]]
# True Range
df['TR'] = 0
for i in range(len(df)):
try:
df.iloc[i,4] = max(df.iat[i,1] - df.iat[i,2],abs(df.iat[i,1] - df.iat[i-1,3]),2] - df.iat[i-1,3]))
except ValueError:
pass
# eATR
df['eATR']= df['TR'].ewm(span=n,adjust=False).mean()
df.drop(['TR'],axis = 1,inplace=True)
return df['eATR']
def contraction(data):
df = data.copy()
e_ATR = eATR(df)
bodyHi = df[['Open','Close']].apply(max,axis=1)
bodyLo = df[['Open','Close']].apply(min,axis=1)
topWick = abs(df['High'] - bodyHi)
botWick = abs(bodyLo - df['Low'])
bodyLen = abs(bodyHi - bodyLo)
canLen = df['High'] - df['Low']
per = 1.5
contraction = np.where((df['High'].shift(7) >= bodyHi.shift(6))
& (df['High'].shift(7) >= bodyHi.shift(5))
& (df['High'].shift(7) >= bodyHi.shift(4))
& (df['High'].shift(7) >= bodyHi.shift(3))
& (df['High'].shift(7) >= bodyHi.shift(2))
& (df['High'].shift(7) >= bodyHi.shift(1))
& (df['High'].shift(7) >= bodyHi.shift(0))
& (df['Low'].shift(7) <= bodyLo.shift(6))
& (df['Low'].shift(7) <= bodyLo.shift(5))
& (df['Low'].shift(7) <= bodyLo.shift(4))
& (df['Low'].shift(7) <= bodyLo.shift(3))
& (df['Low'].shift(7) <= bodyLo.shift(2))
& (df['Low'].shift(7) <= bodyLo.shift(1))
& (df['Low'].shift(7) <= bodyLo.shift(0))
& (canLen.shift(0) <= canLen.shift(7) * per)
& (canLen.shift(1) <= canLen.shift(7) * per)
& (canLen.shift(2) <= canLen.shift(7) * per)
& (canLen.shift(3) <= canLen.shift(7) * per)
& (canLen.shift(4) <= canLen.shift(7) * per)
& (canLen.shift(5) <= canLen.shift(7) * per)
& (canLen.shift(6) <= canLen.shift(7) * per)
| (df['High'].shift(6) >= bodyHi.shift(5))
& (df['High'].shift(6) >= bodyHi.shift(4))
& (df['High'].shift(6) >= bodyHi.shift(3))
& (df['High'].shift(6) >= bodyHi.shift(2))
& (df['High'].shift(6) >= bodyHi.shift(1))
& (df['High'].shift(6) >= bodyHi.shift(0))
& (df['Low'].shift(6) <= bodyLo.shift(5))
& (df['Low'].shift(6) <= bodyLo.shift(4))
& (df['Low'].shift(6) <= bodyLo.shift(3))
& (df['Low'].shift(6) <= bodyLo.shift(2))
& (df['Low'].shift(6) <= bodyLo.shift(1))
& (df['Low'].shift(6) <= bodyLo.shift(0))
& (canLen.shift(0) <= canLen.shift(6) * per)
& (canLen.shift(1) <= canLen.shift(6) * per)
& (canLen.shift(2) <= canLen.shift(6) * per)
& (canLen.shift(3) <= canLen.shift(6) * per)
& (canLen.shift(4) <= canLen.shift(6) * per)
& (canLen.shift(5) <= canLen.shift(6) * per)
| (df['High'].shift(5) >= bodyHi.shift(4))
& (df['High'].shift(5) >= bodyHi.shift(3))
& (df['High'].shift(5) >= bodyHi.shift(2))
& (df['High'].shift(5) >= bodyHi.shift(1))
& (df['High'].shift(5) >= bodyHi.shift(0))
& (df['Low'].shift(5) <= bodyLo.shift(4))
& (df['Low'].shift(5) <= bodyLo.shift(3))
& (df['Low'].shift(5) <= bodyLo.shift(2))
& (df['Low'].shift(5) <= bodyLo.shift(1))
& (df['Low'].shift(5) <= bodyLo.shift(0))
& (canLen.shift(0) <= canLen.shift(5) * per)
& (canLen.shift(1) <= canLen.shift(5) * per)
& (canLen.shift(2) <= canLen.shift(5) * per)
& (canLen.shift(3) <= canLen.shift(5) * per)
& (canLen.shift(4) <= canLen.shift(5) * per)
| (df['High'].shift(4) >= bodyHi.shift(3))
& (df['High'].shift(4) >= bodyHi.shift(2))
& (df['High'].shift(4) >= bodyHi.shift(1))
& (df['High'].shift(4) >= bodyHi.shift(0))
& (df['Low'].shift(4) <= bodyLo.shift(3))
& (df['Low'].shift(4) <= bodyLo.shift(2))
& (df['Low'].shift(4) <= bodyLo.shift(1))
& (df['Low'].shift(4) <= bodyLo.shift(0))
& (canLen.shift(0) <= canLen.shift(4) * per)
& (canLen.shift(1) <= canLen.shift(4) * per)
& (canLen.shift(2) <= canLen.shift(4) * per)
& (canLen.shift(3) <= canLen.shift(4) * per)
| (df['High'].shift(3) >= bodyHi.shift(2))
& (df['High'].shift(3) >= bodyHi.shift(1))
& (df['High'].shift(3) >= bodyHi.shift(0))
& (df['Low'].shift(3) <= bodyLo.shift(2))
& (df['Low'].shift(3) <= bodyLo.shift(1))
& (df['Low'].shift(3) <= bodyLo.shift(0))
& (canLen.shift(0) <= canLen.shift(3) * per)
& (canLen.shift(1) <= canLen.shift(3) * per)
& (canLen.shift(1) <= canLen.shift(3) * per)
| (df['High'].shift(2) >= bodyHi.shift(1))
& (df['High'].shift(2) >= bodyHi.shift(0))
& (df['Low'].shift(2) <= bodyLo.shift(1))
& (df['Low'].shift(2) <= bodyLo.shift(0))
& (canLen.shift(0) <= canLen.shift(2) * per)
& (canLen.shift(1) <= canLen.shift(2) * per),1,0)
return contraction
当满足上述条件时，这是我的图表上以“burlywood”着色的输出。
期待中的感谢！
解决方法
虽然我不太明白你在做什么，但这里有一些我认为你正在尝试做的 Pythonic 伪代码：
def fits_in(a,b,per=1.5):
return (a.hi <= b.hi) and (a.lo >= b.lo) and (a.size * per <= b.size)
def anyfit (candles):
for i in range(1,len(candle_list)):
for j in range(i):
if fits_in(candles[i],candles[j]):
return True
return False
candle_list = [] # <- fill this list in from somewhere
print(anyfit(candle_list))
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