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python - 在 Python 中计算 RSI

问题描述

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，我一直在计算“平均增益”。此处平均增益的逻辑是索引 6 处的第一个平均增益将是 RSI_length 周期的“增益”的平均值。对于连续的“平均增益”，它应该是

(Previous Avg Gain * (RSI_length - 1) + "Gain") / RSI_length

我尝试了以下但没有按预期工作

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))

这段代码的输出是：

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

期望的输出是：

    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  0.734694
8     103     -2.0     0     2      8  0.629738
9     102     -1.0     0     1      9  0.539775
10    103      1.0     1     0     10  0.605522
11    104      1.0     1     0     11  0.661876
12    103     -1.0     0     1     12  0.567322
13    105      2.0     2     0     13  0.771990
14    106      1.0     1     0     14  0.804563
15    107      1.0     1     0     15  0.832483
16    108      1.0     1     0     16  0.856414
17    106     -2.0     0     2     17  0.734069
18    105     -1.0     0     1     18  0.629202
19    107      2.0     2     0     19  0.825030
20    109      2.0     2     0     20  0.992883

​</p>

标签： pythonnumpydataframetrading

解决方案

（已编辑）

这是您的公式的实现。

RSI_LENGTH = 7

rolling_gain = df["Gain"].rolling(RSI_LENGTH).mean()
df.loc[RSI_LENGTH-1, "RSI"] = rolling_gain[RSI_LENGTH-1]

for inx in range(RSI_LENGTH, len(df)):
    df.loc[inx, "RSI"] = (df.loc[inx-1, "RSI"] * (RSI_LENGTH -1) + df.loc[inx, "Gain"]) / RSI_LENGTH

结果是：

    Close  Change  Gain  Loss  Index       RSI
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  0.734694
8     103    -2.0   0.0   2.0      8  0.629738
9     102    -1.0   0.0   1.0      9  0.539775
10    103     1.0   1.0   0.0     10  0.605522
11    104     1.0   1.0   0.0     11  0.661876
12    103    -1.0   0.0   1.0     12  0.567322
13    105     2.0   2.0   0.0     13  0.771990
14    106     1.0   1.0   0.0     14  0.804563
15    107     1.0   1.0   0.0     15  0.832483
16    108     1.0   1.0   0.0     16  0.856414
17    106    -2.0   0.0   2.0     17  0.734069
18    105    -1.0   0.0   1.0     18  0.629202
19    107     2.0   2.0   0.0     19  0.825030
20    109     2.0   2.0   0.0     20  0.992883

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