forwards_rates_from options.py

import pandas as pd
import numpy as np

option_data_frame = pd.read_csv('C:/code/python/pyql/examples/data/df_SPX_24jan2011.csv',parse_dates=[0,2])
grouped = option_data_frame.groupby('dtExpiry')


for spec, group in grouped:
    print('processing group %s' % spec)
    indx = group.index
    dtTrade = group['dtTrade'][indx[0]]
    dtExpiry = group['dtExpiry'][indx[0]]
    spot = group['Spot'][indx[0]]
    daysToExpiry = (dtExpiry-dtTrade).days
    timeToMaturity = daysToExpiry/365.0
    df_call = group[group['Type'] == 'C']
    df_put = group[group['Type'] == 'P']

    df_C = pd.DataFrame((df_call['PBid']+df_call['PAsk'])/2,columns=['PremiumC'])
    df_C.index = df_call['Strike']
    df_P = pd.DataFrame((df_put['PBid']+df_put['PAsk'])/2,columns=['PremiumP'])
    df_P.index = df_put['Strike']
    df_all = df_C.join(df_P, how='inner')
    df_all['Strike'] = df_all.index
    df_all['C-P'] = df_all['PremiumC'] - df_all['PremiumP']

    y = np.array(df_all['C-P'])
    x = np.array(df_all['Strike'])
    A = np.vstack((x, np.ones(x.shape))).T
    b = np.linalg.lstsq(A, y)[0]
    iRate = -np.log(-b[0])/timeToMaturity
    dRate = np.log(spot/b[1])/timeToMaturity
    discountFactor = np.exp(-iRate*timeToMaturity)
    Fwd = spot * np.exp((iRate-dRate)*timeToMaturity)
    print('Fwd: %f int rate: %f div yield: %f' % (Fwd, iRate, dRate))
    
    
#based off combos    
def impliedCarry(callP ,  putP , strike  , stockP):
    return callP  -  putP  +  strike -  stockP
    
def impliedBorrow (callP ,  putP , strike  , stockP, daysToExpiry):
    return impliedCarry(callP ,  putP , strike  , stockP)  /  strike *  360 / daysToExpiry