performing more complex calculations on groupby objects

Question

I am currently looking to do some calculations on a large dataset of options where I want first to split the data according to the strike price and expiry, then perform a set of calculations shown below onto each subgroup. I have been able to separate the data using groupby to get the split I want, I also wrote the calculation i want to do which works when tested on a subgroup. The only problem I have is to combine the two together.

Here is the code I used to group my data:

grouped =df.groupby(['Expiry','Strike'])

I had a read online and they mentioned the use of the apply function but the examples only included simple functions such as summation or averages.

Here is the calculation that I would like to perform on each subgroup data, where x,y,z,u,R are columns that in each subset that is the same for all subgroups:

 def p(d, S, B, c):
     return d * S + B - c


 def b_t(r, b_old, S, d, d_old, t):
     return np.exp(r * t) * b_old + S * (d_old - d)


 def e_t(d_old, S, c, r, t, b_old):
     return d_old * S - c + np.exp(r * t) * b_old
 
 P_results = []
 B_results = []
 E_results = []
 
 for i,(d,S,c,t,r) in enumerate(zip(x,y,z,u,R)):
    B = b_t(r, b_old, S, d, d_old, t)
    P = p(d, S, B, c)
    E = e_t(d_old, S, c, r, t, b_old)
    print('i={},P={},B={},E={}'.format(i,P,B,E))
    B_results.append(B)
    P_results.append(P)
    E_results.append(E)
    b_old = B
    d_old = d

I thought maybe if I could save each subset as a new variable dataframe then maybe it could work but I haven't been able to do that.

I hope this is clear and I think posting some data would help but I am not sure how best to upload it here.

Much appreciate your help!

UPDATE 1: Found a solution that works

    grouped =df.groupby(['Expiry','Strike'])

    lg = list(grouped)


    P_results = []
    l_results =[]
    B_results = []
    E_results = []
    
    for l in range(len(lg)):
      df2=lg[l][1]
      d_old = df2.iloc[0, 4]
      S_old = df2.iloc[0, 8]
      c_old = df2.iloc[0, 10]
      b_old = c_old - d_old * S_old
      x = df2.iloc[1:, 4]
      y = df2.iloc[1:, 8]
      z = df2.iloc[1:, 10]
      u = df2.iloc[1:, 9]
      R = df2.iloc[1:, 7]

        for i, (d, S, c, t, r) in enumerate(zip(x, y, z, u, R)):
           B = b_t(r, b_old, S, d, d_old, t)
           P = p(d, S, B, c)
           E = e_t(d_old, S, c, r, t, b_old)
           print('i={},P={},B={},E={}'.format(i, P, B, E))
           l_results.append(l)
           B_results.append(B)
           P_results.append(P)
           E_results.append(E)
           b_old = B
           d_old = d

    BB = pd.DataFrame(np.column_stack([l_results, P_results, 
    E_results,B_results]),columns=['l','P','E','B'])

All I did was to transform grouped into a callable list and then call each of the sections out using a for loop then use another for loop to perform the calculations. It is not the prettiest output, I put l_results there to show which group the calculations were referring to but seems to be sufficient for now. If there is any better way please let me know!

performing more complex calculations on groupby objects

0 Answers