How to do operetaions with columns based on date and column values?

Question

I have this pandas Dataframe:

My goal is to perform some addictions and substractions based on culumns value conditions, and store the results inside a new column "pl",

This is the Dataframe I want to have:

The first non-NaN value will be necessarly in the "entry" column,

First scenario: I want that, if the next non-NaN value (after a non-NaN inside "entry" and then a non-NaN inside "tp1") is contained inside "tp2" column, then do this operation: (tp1 - entry) + (tp2 - entry)

Second scenario: I want that, if the next non-NaN value (after entry) is contained inside the column "sl1" then do this operation: sl1 - entry.

Third scenario: I want that, if the next non-NaN value (after entry) is contained inside the column "tp1" and there's a non-NaN value inside the column "sl2" then do this operation: tp1 - entry.

This is my code:

import pandas as pd

tbl = {"date" :["2022-02-27", "2022-02-27", "2022-02-27", "2022-02-27", "2022-02-27", 
                    "2022-02-28", "2022-02-28","2022-02-28", "2022-02-28", "2022-02-01", 
                   "2022-02-01", "2022-02-01", "2022-02-01"],
       "entry" : ["NaN", "NaN", 1.2, "NaN", "NaN","NaN", 1.3, "NaN", "NaN", "NaN", 1.2, "NaN", 
                  "NaN",],
       "tp1" : ["NaN", "NaN", "NaN", 1.4, "NaN", "NaN", "NaN", "NaN", "NaN", "NaN", "NaN", 
                1.3, "NaN"],
       "sl1" : ["NaN", "NaN", "NaN", "NaN", "NaN", "NaN", "NaN", "NaN", 1.15, "NaN", "NaN", 
                "NaN", "NaN"],
       "tp2" : ["NaN", "NaN", "NaN", "NaN", 1.5, "NaN","NaN", "NaN", "NaN", "NaN", "NaN", 
                "NaN", "NaN"],
       "sl2" : ["NaN", "NaN", "NaN", "NaN", "NaN", "NaN","NaN", "NaN", "NaN", "NaN", "NaN", 
               "NaN", 1.2]}


df = pd.DataFrame(tbl)

df = df.replace('NaN', float('nan'))

############## This is the way i'm trying to achive what i want:#########

#this code will only make tp1 - entry, or sl1 - entry, but it's wrong 
#bacause it's made based on a dataframe without "sl2,tp2" consideration

group = df['date'] 

s1 = df['tp1'].fillna(df['sl1']).groupby(group).bfill()
s2 = df['entry'].groupby(group).bfill()

df.loc[~group.duplicated(), 'pl'] = s1-s2

I'm blocked here, I don't understand how to code the other conditions, Any ideas?

Edit The first value inside pl column is wrong, it should be 0.5. Not 0.20