Is there an alternative to long nested np.where clauses for vectorized conditional evaluation?

Question

I have a seven column data frame that contains many thousands of rows. Below is a sample. The seventh column is populated dynamically based on the values in the other six columns in the current row and the row above it.

The nested np.where statement I came up with works but it's not very flexible or easy to read. I explored using np.select but I don't have a finite set of values ex-ante that I can assign. Technically, they can be any real number.

So I'm wondering if there's a better way to perform this type of conditional vectorized operation given the constraint?

df['G'] = np.where(df['D'].eq(0) & df['D'].shift(1).eq(0) | 
             df['D'].ne(0) & df['D'].shift(1).ne(0), df['C'], 
             np.where(df['D'].eq(0) & df['D'].shift(1).eq(-1) & 
                      (df['B'] <= df['E'].shift(1)) & (df['E'].shift(1) <= df['A']), df['E'].shift(1), 
             np.where(df['D'].eq(0) & df['D'].shift(1).eq(-1) & 
                      (df['A'] >= df['F'].shift(1)) & (df['F'].shift(1) >= df['B']), df['F'].shift(1),
             np.where(df['D'].eq(0) & df['D'].shift(1).eq(1) & 
                      (df['B'] <= df['E'].shift(1)) & (df['E'].shift(1) <= df['A']), df['E'].shift(1),
             np.where((df['A'] >= df['F'].shift(1)) & (df['F'].shift(1) >= df['B']), 
                               df['F'].shift(1), df['C'])))))

df

                 A       B       C    D       E       F       G
Date                                                           
2021-05-17  126.93  125.17  125.90  0.0    0.00    0.00  125.90
2021-05-18  126.99  124.78  124.48  0.0    0.00    0.00  124.48
2021-05-19  124.92  122.86  124.32  1.0  122.86  135.15  124.32
2021-05-20  127.72  125.10  126.93  1.0  122.86  135.15  126.93
2021-05-21  128.00  125.21  125.06 -1.0  128.00  115.20  125.06
2021-05-24  127.94  125.94  126.72 -1.0  128.00  115.20  126.72
2021-05-25  128.32  126.32  126.53  0.0    0.00    0.00  128.00
2021-05-26  127.39  126.42  126.48  0.0    0.00    0.00  126.48
2021-05-27  127.64  125.08  124.91  0.0    0.00    0.00  124.91
2021-05-28  125.80  124.55  124.24 -1.0  125.80  113.22  124.24
2021-06-01  125.35  123.94  123.91 -1.0  125.80  113.22  123.91
2021-06-02  125.24  124.05  124.69  1.0  124.05  136.46  124.69
2021-06-03  124.85  123.13  123.18  0.0    0.00    0.00  124.05
2021-06-04  126.16  123.85  125.52  1.0  123.85  136.23  125.52
2021-06-07  126.32  124.83  125.53  1.0  123.85  136.23  125.53
2021-06-08  128.46  126.21  126.37  1.0  123.85  136.23  126.37