我正在尝试在数据帧上计算RSI
df = pd.DataFrame({"Close": [100,101,102,103,104,105,106,105,103,102,103,104,103,105,106,107,108,106,105,107,109]})
df["Change"] = df["Close"].diff()
df["Gain"] = np.where(df["Change"]>0,df["Change"],0)
df["Loss"] = np.where(df["Change"]<0,abs(df["Change"]),0 )
df["Index"] = [x for x in range(len(df))]
print(df)
Close Change Gain Loss Index
0 100 NaN 0.0 0.0 0
1 101 1.0 1.0 0.0 1
2 102 1.0 1.0 0.0 2
3 103 1.0 1.0 0.0 3
4 104 1.0 1.0 0.0 4
5 105 1.0 1.0 0.0 5
6 106 1.0 1.0 0.0 6
7 105 -1.0 0.0 1.0 7
8 103 -2.0 0.0 2.0 8
9 102 -1.0 0.0 1.0 9
10 103 1.0 1.0 0.0 10
11 104 1.0 1.0 0.0 11
12 103 -1.0 0.0 1.0 12
13 105 2.0 2.0 0.0 13
14 106 1.0 1.0 0.0 14
15 107 1.0 1.0 0.0 15
16 108 1.0 1.0 0.0 16
17 106 -2.0 0.0 2.0 17
18 105 -1.0 0.0 1.0 18
19 107 2.0 2.0 0.0 19
20 109 2.0 2.0 0.0 20
RSI_length = 7
现在，我被困在计算“平均增益”。此处的平均增益逻辑是对于索引6处的第一个平均增益，将是RSI_length周期的“增益”平均值。对于连续的“平均增益”，应为
（先前平均增益*（RSI_length-1）+“ Gain”）/ RSI_length
我尝试了以下操作，但无法正常工作
df["Avg Gain"] = np.nan
df["Avg Gain"] = np.where(df["Index"]==(RSI_length-1),df["Gain"].rolling(window=RSI_length).mean(),\
np.where(df["Index"]>(RSI_length-1),(df["Avg Gain"].iloc[df["Index"]-1]*(RSI_length-1)+df["Gain"]) / RSI_length,np.nan))
此代码的输出是：
print(df)
Close Change Gain Loss Index Avg Gain
0 100 NaN 0.0 0.0 0 NaN
1 101 1.0 1.0 0.0 1 NaN
2 102 1.0 1.0 0.0 2 NaN
3 103 1.0 1.0 0.0 3 NaN
4 104 1.0 1.0 0.0 4 NaN
5 105 1.0 1.0 0.0 5 NaN
6 106 1.0 1.0 0.0 6 0.857143
7 105 -1.0 0.0 1.0 7 NaN
8 103 -2.0 0.0 2.0 8 NaN
9 102 -1.0 0.0 1.0 9 NaN
10 103 1.0 1.0 0.0 10 NaN
11 104 1.0 1.0 0.0 11 NaN
12 103 -1.0 0.0 1.0 12 NaN
13 105 2.0 2.0 0.0 13 NaN
14 106 1.0 1.0 0.0 14 NaN
15 107 1.0 1.0 0.0 15 NaN
16 108 1.0 1.0 0.0 16 NaN
17 106 -2.0 0.0 2.0 17 NaN
18 105 -1.0 0.0 1.0 18 NaN
19 107 2.0 2.0 0.0 19 NaN
20 109 2.0 2.0 0.0 20 NaN
期望的是：
Close Change Gain Loss Index Avg Gain
0 100 NaN 0 0 0 NaN
1 101 1.0 1 0 1 NaN
2 102 1.0 1 0 2 NaN
3 103 1.0 1 0 3 NaN
4 104 1.0 1 0 4 NaN
5 105 1.0 1 0 5 NaN
6 106 1.0 1 0 6 0.857143
7 105 -1.0 0 1 7 0.795918
8 103 -2.0 0 2 8 0.739067
9 102 -1.0 0 1 9 0.686277
10 103 1.0 1 0 10 0.708685
11 104 1.0 1 0 11 0.729494
12 103 -1.0 0 1 12 0.677387
13 105 2.0 2 0 13 0.771859
14 106 1.0 1 0 14 0.788155
15 107 1.0 1 0 15 0.803287
16 108 1.0 1 0 16 0.817338
17 106 -2.0 0 2 17 0.758956
18 105 -1.0 0 1 18 0.704745
19 107 2.0 2 0 19 0.797263
20 109 2.0 2 0 20 0.883173
