python – 需要根据数据框中的行号应用不同的公式
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我正在努力在数据框架中找到某种移动平均线.公式将根据计算的行数进行更改.实际情况是我需要计算列Z的位置.
编辑-2：
以下是我正在使用的实际数据
Date Open High Low Close
0 01-01-2018 1763.95 1763.95 1725.00 1731.35
1 02-01-2018 1736.20 1745.80 1725.00 1743.20
2 03-01-2018 1741.10 1780.00 1740.10 1774.60
3 04-01-2018 1779.95 1808.00 1770.00 1801.35
4 05-01-2018 1801.10 1820.40 1795.60 1809.95
5 08-01-2018 1816.00 1827.95 1800.00 1825.00
6 09-01-2018 1823.00 1835.00 1793.90 1812.05
7 10-01-2018 1812.05 1823.00 1801.40 1816.55
8 11-01-2018 1825.00 1825.05 1798.55 1802.10
9 12-01-2018 1805.00 1820.00 1794.00 1804.95
10 15-01-2018 1809.90 1834.45 1792.45 1830.00
11 16-01-2018 1835.00 1857.45 1826.10 1850.25
12 17-01-2018 1850.00 1852.45 1826.20 1840.50
13 18-01-2018 1840.50 1852.00 1823.50 1839.00
14 19-01-2018 1828.25 1836.35 1811.00 1829.50
15 22-01-2018 1816.50 1832.55 1805.50 1827.20
16 23-01-2018 1825.00 1825.00 1782.25 1790.15
17 24-01-2018 1787.80 1792.70 1732.15 1737.50
18 25-01-2018 1739.90 1753.40 1720.00 1726.40
19 29-01-2018 1735.15 1754.95 1729.80 1738.70
我使用的代码片段如下：
from datetime import date
from nsepy import get_history
import csv
import pandas as pd
import numpy as np
import requests
from datetime import timedelta
import datetime as dt
import pandas_datareader.data as web
import io
df = pd.read_csv('ACC.CSV')
idx = df.reset_index().index
df['Change'] = df['Close'].diff()
df['Advance'] = np.where(df.Change > 0, df.Change,0)
df['Decline'] = np.where(df.Change < 0, df.Change*-1, 0)
conditions = [idx < 14, idx == 14, idx > 14]
values = [0, (df.Advance.rolling(14).sum())/14, (df.Avg_Gain.shift(1) * 13 + df.Advance)/14]
df['Avg_Gain'] = np.select(conditions, values)
df['Avg_Loss'] = (df.Decline.rolling(14).sum())/14
df['RS'] = df.Avg_Gain / df.Avg_Loss
df['RSI'] = np.where(df['Avg_Loss'] == 0, 100, 100-(100/(1+df.RS)))
df.drop(['Change', 'Advance', 'Decline', 'Avg_Gain', 'Avg_Loss', 'RS'], axis=1)
print(df.head(20))
以下是我得到的错误：
Traceback (most recent call last):
File "C:/Users/Lenovo/Desktop/Python/0.Chart Patterns/Z.Sample Code.py", line 20, in <module>
values = [0, (df.Advance.rolling(14).sum())/14, (df.Avg_Gain.shift(1) * 13 + df.Advance)/14]
File "C:\Users\Lenovo\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\generic.py", line 3614, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'Avg_Gain'
编辑3：下面是预期的输出,然后我也会写下公式.原始DF由列Date Open High Low&关.
Advance and Decline –
? If the difference between current & previous is +ve then Advance = difference and Decline = 0
? If the difference between current & previous is –ve then Advance = 0 and Decline = -1 * difference
Avg_Gain:
? If index is < 13 then Avg_Gain = 0
? If index = 13 then Avg_Gain = Average of 14 periods
? If index > 13, then Avg_Gain = (Avg_Gain(previous-row) * 13 + Advance(current-row) )/14
Avg_Loss:
? If index is < 13 then Avg_Loss = 0
? If index = 13 then Avg_Loss = Average of Advance of 14 periods
? If index > 13, then Avg_Loss = (Avg_Loss(previous-row) * 13 + Decline(current-row) )/14
RS:
? If index < 13 then RS = 0
? If Index >= 13 then RS = Avg_Gain/Avg_Loss
RSI = 100-(100/(1 + RS))
我希望这有帮助.
解决方法:
您的代码中有错误,因为您在创建df.Avg_Gain时使用了df.Avg_Gain. ÿ
values = [0, (df.Advance.rolling(14).sum())/14, (df.Avg_Gain.shift(1) * 13 + df.Advance)/14]
df['Avg_Gain'] = np.select(conditions, values)
我将代码的这一部分更改为以下内容：
up = df.Advance.rolling(14).sum()/14
values = [0, up, (up.shift(1) * 13 + df.Advance)/14]
输出(idx> = 14)：
Date Open High Low Close RSI
14 2018-01-19 1828.25 1836.35 1811.00 1829.50 75.237850
15 2018-01-22 1816.50 1832.55 1805.50 1827.20 72.920021
16 2018-01-23 1825.00 1825.00 1782.25 1790.15 58.793750
17 2018-01-24 1787.80 1792.70 1732.15 1737.50 40.573938
18 2018-01-25 1739.90 1753.40 1720.00 1726.40 31.900045
19 2018-01-29 1735.15 1754.95 1729.80 1738.70 33.197678
应该有更好的方法来做到这一点.如果我找到一个,我会用更好的解决方案更新它.如果这些数据是正确的,请告诉我.
更新：
您还需要更正’Avg_loss` ::的计算
down = df.Decline.rolling(14).sum()/14
down_values = [0, down, (down.shift(1) * 13 + df.Decline)/14]
df['Avg_Loss'] = np.select(conditions, down_values)
更新2：提供预期数据后.
所以我能做到这一点的唯一方法就是循环 – 不确定是否有可能做到这一点 – 也许是我不知道的一些熊猫功能.
所以首先设置Avg_Gain和Avg_Loss与之前一样：
你只需稍微改变一下这些值：
conditions = [idx<13, idx==13, idx>13]
up = df.Advance.rolling(14).sum()/14
values = [0, up, 0]
df['Avg_Gain'] = np.select(conditions, values)
down = df.Decline.rolling(14).sum()/14
down_values = [0, down, 0]
df['Avg_Loss'] = np.select(conditions, d_values)
我已经将你的条件改为在指数13上拆分 – 因为这是我根据预期的输出看到的.
运行此代码后,您将使用Agv_Gain和Avg_Loss的先前值从索引14填充Avg_Gain和Avg_Loss的值.
p=14
for i in range(p, len(df)):
df.at[i, 'Avg_Gain'] = ((df.loc[i-1, 'Avg_Gain'] * (p-1)) + df.loc[i, 'Advance']) / p
df.at[i, 'Avg_Loss'] = ((df.loc[i-1, 'Avg_Loss'] * (p-1)) + df.loc[i, 'Decline']) / p
输出：
df[13:][['Date','Avg_Gain', 'Avg_Loss', 'RS', 'RSI']]
Date Avg_Gain Avg_Loss RS RSI
13 2018-01-18 10.450000 2.760714 3.785252 79.102460
14 2018-01-19 9.703571 3.242092 2.992997 74.956155
15 2018-01-22 9.010459 3.174800 2.838119 73.945571
16 2018-01-23 8.366855 5.594457 1.495562 59.928860
17 2018-01-24 7.769222 8.955567 0.867530 46.453335
18 2018-01-25 7.214278 9.108741 0.792017 44.196960
19 2018-01-29 7.577544 8.458116 0.895890 47.254330
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