问题描述
我有两个数据框
df1=pd.DataFrame({"a":pd.date_range("2021-01-01","2021-01-10"),"b" :[12,13,16,15,12,14,17,19,20,21]})
df2=pd.DataFrame({"a":pd.date_range("2021-01-01","b":[np.nan,np.nan,13.7,14.3,16.7,18.8,20]})
whenevr df1[b] > df2[b] 我想买，whenevr df1[b]
注意：
所需输出
#dataframe
stat=pd.DataFrame({"stock":["b","b"],"Entry_date":["2021-01-03","2021-01-07"],"Entry_price":[16,17],"Exit_date":["2021-01-05","2021-01-10"],"Exit_price":[12,21]})
解决方法
更新（多只股票）
给定多只股票 b 和 c：
df1 = pd.DataFrame({'a':{0:pd.Timestamp('2021-01-01 00:00:00'),1:pd.Timestamp('2021-01-02 00:00:00'),2:pd.Timestamp('2021-01-03 00:00:00'),3:pd.Timestamp('2021-01-04 00:00:00'),4:pd.Timestamp('2021-01-05 00:00:00'),5:pd.Timestamp('2021-01-06 00:00:00'),6:pd.Timestamp('2021-01-07 00:00:00'),7:pd.Timestamp('2021-01-08 00:00:00'),8:pd.Timestamp('2021-01-09 00:00:00'),9:pd.Timestamp('2021-01-10 00:00:00')},'b':{0:12,1:13,2:16,3:15,4:12,5:14,6:17,7:19,8:20,9:21},'c':{0:13,1:16,2:15,3:12,4:14,5:17,6:19,7:20,8:21,9:12}})
df2 = pd.DataFrame({'a':{0:pd.Timestamp('2021-01-01 00:00:00'),'b':{0:np.nan,1:np.nan,2:13.7,3:14.3,4:14.0,5:15.0,6:14.3,7:16.7,8:18.8,9:20.0},'c':{0:np.nan,1:13.7,2:14.3,3:14.0,4:15.0,5:14.3,6:16.7,7:30.0,8:30.0,9:30.0}})
# -------- df1 -------- ---------- df2 ----------
# a b c a b c
# 0 2021-01-01 12 13 0 2021-01-01 NaN NaN
# 1 2021-01-02 13 16 1 2021-01-02 NaN 13.7
# 2 2021-01-03 16 15 2 2021-01-03 13.7 14.3
# 3 2021-01-04 15 12 3 2021-01-04 14.3 14.0
# 4 2021-01-05 12 14 4 2021-01-05 14.0 15.0
# 5 2021-01-06 14 17 5 2021-01-06 15.0 14.3
# 6 2021-01-07 17 19 6 2021-01-07 14.3 16.7
# 7 2021-01-08 19 20 7 2021-01-08 16.7 30.0
# 8 2021-01-09 20 21 8 2021-01-09 18.8 30.0
# 9 2021-01-10 21 12 9 2021-01-10 20.0 30.0
首先将日期设置为索引并创建初始 df1 > df2 掩码：
df1 = df1.set_index('a')
df2 = df2.set_index('a')
mask = df1 > df2
要处理最后缺少的退出条件（如库存 b），请在 df1 和 mask 后附加一行：
# duplicate the mask's last row but with False values
# - if the last row originally was True,this closes the exit condition
# - if the last row originally was False,this has no effect
mask = mask.append(mask.tail(1))
mask.iloc[-1] = False
# duplicate df1's last row to match the mask
df1 = df1.append(df1.tail(1))
用entry索引mask & ~mask.shift()点，用exit索引~mask & mask.shift()点：
entry = (df1[mask & ~mask.shift().bfill()]
.stack().reset_index(name='entry_price')
.rename(columns={'a': 'entry_date','level_1': 'stock'}))
exit = (df1[~mask & mask.shift().bfill()]
.stack().reset_index(name='exit_price')
.rename(columns={'a': 'exit_date','level_1': 'stock'}))
然后 concat() 帧：
pd.concat([
entry[['stock','entry_date','entry_price']],exit[['exit_date','exit_price']],],axis=1).sort_values(by='stock')
# stock entry_date entry_price exit_date exit_price
# 1 b 2021-01-03 16.0 2021-01-05 12.0
# 3 b 2021-01-07 17.0 2021-01-10 21.0
# 0 c 2021-01-02 16.0 2021-01-04 12.0
# 2 c 2021-01-06 17.0 2021-01-08 20.0
原答案（一只股票）
创建一个布尔值 mask 并用 entry 索引 mask & ~mask.shift() 点：
mask = df1.b > df2.b
entry = df1[mask & ~mask.shift().bfill()].rename(columns={'a': 'entry_date','b': 'entry_price'})
# entry_date entry_price
# 0 2021-01-03 16
# 1 2021-01-07 17
并用 exit 索引 ~mask & mask.shift() 点：
exit = df1[~mask & mask.shift().bfill()]
exit = pd.concat([exit,df1.tail(1)]).rename(columns={'a': 'exit_date','b': 'exit_price'})
# exit_date exit_price
# 0 2021-01-05 12
# 1 2021-01-10 21
然后 concat() 帧：
stat = pd.concat([
entry.reset_index(drop=True),exit.reset_index(drop=True),axis=1).assign(stock='b').dropna()
# entry_date entry_price exit_date exit_price stock
# 0 2021-01-03 16 2021-01-05 12 b
# 1 2021-01-07 17 2021-01-10 21 b