Subtraction between 'dict_values' and 'float' - python-3.x

Question

I am getting the error "TypeError: unsupported operand type(s) for -: 'dict_values' and 'float'" from line 173 in the sample code. I have copied from a book that does not yet seem to be updated to Python 3 and other forum topics don't seem to cover this problem.
It is trying to calculate the error in an optimsation for the difference in market values and model values, but the data storage type is different across the two.
Thanks
import numpy as np
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
import calendar
# frame
from get_year_deltas import get_year_deltas
from constant_short_rate import constant_short_rate
from market_environment import market_environment
from plot_option_stats import plot_option_stats
# simulation
from sn_random_numbers import sn_random_numbers
from simulation_class import simulation_class
from geometric_brownian_motion import geometric_brownian_motion
from jump_diffusion import jump_diffusion
from square_root_diffusion import square_root_diffusion
# valuation
from valuation_class import valuation_class
from valuation_mcs_european import valuation_mcs_european
from valuation_mcs_american import valuation_mcs_american
from derivatives_position import derivatives_position
from derivatives_portfolio import derivatives_portfolio
#import os
#path = os.getcwd()
url = 'http://www.stoxx.com/download/historical_values/h_vstoxx.txt'
vstoxx_index = pd.read_csv(url, index_col=0, header=2,parse_dates=True, dayfirst=True)
vstoxx_index = vstoxx_index[('2013/12/31' < vstoxx_index.index) & (vstoxx_index.index < '2014/4/1')]
vstoxx_futures = pd.read_excel('./vstoxx_march_2014.xlsx', 'vstoxx_futures')
del vstoxx_futures['A_SETTLEMENT_PRICE_SCALED']
del vstoxx_futures['A_CALL_PUT_FLAG']
del vstoxx_futures['A_EXERCISE_PRICE']
del vstoxx_futures['A_PRODUCT_ID']
columns = ['DATE', 'EXP_YEAR', 'EXP_MONTH', 'PRICE']
vstoxx_futures.columns = columns
def third_friday(date):
day = 21 - (calendar.weekday(date.year, date.month, 1) + 2) % 7
return dt.datetime(date.year, date.month, day)
set(vstoxx_futures['EXP_MONTH'])
third_fridays = {}
for month in set(vstoxx_futures['EXP_MONTH']):
third_fridays[month] = third_friday(dt.datetime(2014, month, 1))
#third_fridays
tf = lambda x: third_fridays[x]
vstoxx_futures['MATURITY'] = vstoxx_futures['EXP_MONTH'].apply(tf)
#vstoxx_futures.tail()
vstoxx_options = pd.read_excel('./vstoxx_march_2014.xlsx', 'vstoxx_options')
#vstoxx_options.info()
del vstoxx_options['A_SETTLEMENT_PRICE_SCALED']
del vstoxx_options['A_PRODUCT_ID']
columns = ['DATE', 'EXP_YEAR', 'EXP_MONTH', 'TYPE', 'STRIKE', 'PRICE']
vstoxx_options.columns = columns
vstoxx_options['MATURITY'] = vstoxx_options['EXP_MONTH'].apply(tf)
#vstoxx_options.head()
vstoxx_options['STRIKE'] = vstoxx_options['STRIKE'] / 100.0
save = False
if save is True:
import warnings
warnings.simplefilter('ignore')
h5 = pd.HDFStore('./vstoxx_march_2014.h5', complevel=9, complib='blosc')
h5['vstoxx_index'] = vstoxx_index
h5['vstoxx_futures'] = vstoxx_futures
h5['vstoxx_options'] = vstoxx_options
h5.close()
pricing_date = dt.datetime(2014, 3, 31)
# last trading day in March 2014
maturity = third_fridays[10]
# October maturity
initial_value = vstoxx_index['V2TX'][pricing_date]
# VSTOXX on pricing_date
forward = vstoxx_futures[(vstoxx_futures.DATE == pricing_date) & (vstoxx_futures.MATURITY == maturity)]['PRICE'].values[0]
tol = 0.20
option_selection = vstoxx_options[(vstoxx_options.DATE == pricing_date)
& (vstoxx_options.MATURITY == maturity)
& (vstoxx_options.TYPE == 'C')
& (vstoxx_options.STRIKE > (1 - tol) * forward)
& (vstoxx_options.STRIKE < (1 + tol) * forward)]
me_vstoxx = market_environment('me_vstoxx', pricing_date)
me_vstoxx.add_constant('initial_value', initial_value)
me_vstoxx.add_constant('final_date', maturity)
me_vstoxx.add_constant('currency', 'EUR')
me_vstoxx.add_constant('frequency', 'B')
me_vstoxx.add_constant('paths', 10000)
csr = constant_short_rate('csr', 0.01)
# somewhat arbitrarily chosen here
me_vstoxx.add_curve('discount_curve', csr)
# parameters to be calibrated later
me_vstoxx.add_constant('kappa', 1.0)
me_vstoxx.add_constant('theta', 1.2 * initial_value)
vol_est = vstoxx_index['V2TX'].std() * np.sqrt(len(vstoxx_index['V2TX']) / 252.0)
me_vstoxx.add_constant('volatility', vol_est)
# vol_est
vstoxx_model = square_root_diffusion('vstoxx_model', me_vstoxx)
me_vstoxx.add_constant('strike', forward)
me_vstoxx.add_constant('maturity', maturity)
payoff_func = 'np.maximum(maturity_value - strike, 0)'
vstoxx_eur_call = valuation_mcs_european('vstoxx_eur_call',vstoxx_model, me_vstoxx, payoff_func)
option_models = {}
for option in option_selection.index:
strike = option_selection['STRIKE'].ix[option]
me_vstoxx.add_constant('strike', strike)
option_models[option] = valuation_mcs_european( 'eur_call_%d' % strike, vstoxx_model, me_vstoxx, payoff_func )
def calculate_model_values(p0):
'''
Returns all relevant option values.
Parameters
p0 : tuple/list, tuple of kappa, theta, volatility
Returns
model_values : dict, dictionary with model values
'''
kappa, theta, volatility = p0
vstoxx_model.update(kappa=kappa,
theta=theta,
volatility=volatility)
model_values = {}
for option in option_models:
model_values[option] = option_models[option].present_value(fixed_seed=True)
return model_values
# calculate_model_values((0.5, 27.5, vol_est))
i = 0
def mean_squared_error(p0):
'''
Returns the mean-squared error given the model and market values.
Parameters
p0 : tuple/list, tuple of kappa, theta, volatility
Returns
MSE : float, mean-squared error
'''
global i
model_values = np.array(calculate_model_values(p0).values())
market_values = option_selection['PRICE'].values
option_diffs = model_values - market_values
MSE = np.sum(option_diffs ** 2) / len(option_diffs)
# vectorized MSE calculation
if i % 20 == 0:
if i == 0:
print( '%4s' % i, '%6s' % "kappa", '%6s' % "theta", '%6s —>' % "vola", '%6s' % "MSE")
print( '%4d' % i, '%6.3f' % p0[0], '%6.3f' % p0[1], '%6.3f —>' % p0[2], '%6.3f' % MSE )
i += 1
return MSE
mean_squared_error((0.5, 27.5, vol_est))