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ResultsandGraphs.py
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ResultsandGraphs.py
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from distutils.log import error
from turtle import color
import pandas as pd
import numpy as np
import os
import tensorflow as tf
import pickle
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt
from py_vollib.black_scholes.implied_volatility import implied_volatility as implVola
from get_data import get_spot
from get_data import get_maturities
from get_data import get_strikes
from get_data import get_spot
from get_data import get_bids
from get_data import get_asks
#Name of Data file
real_data = 'Cali_6mat20k'
#Name of Cali file
calibration_name = 'SP500_calibeta1'
#Get the current parent path
parent_dir = os.path.dirname(os.path.abspath(__file__))
#File path of cali pickle
pickle_lsv = parent_dir + '\\PastCalibrations\\' + calibration_name + '\\finsurf.pkl'
#File path for finsurf instance
finsurf_path = parent_dir + '\\PastCalibrations\\' + calibration_name + '\\histData.npz'
#File path for finsurf instance
bidask_path = parent_dir + '\\PastCalibrations\\' + calibration_name + '\\bidaskCaliData.npz'
#READ AND PRINT NPZ FILE
data = np.load(finsurf_path)
data_bidask = np.load(bidask_path)
#DATA FOR GRAPHS BELOW
#PRICES_DATA
prices_data = data['p_data']
#PRICES_DATA
prices_model = data['p_model']
#IV_DATA
IV_data = data['iV_data']
#IV_MODEL
IV_model = data['iV_model']
#MATURITIES
Mat = get_maturities(real_data)
#STRIKES
K = get_strikes(real_data)
#SPOT
S0 = get_spot(real_data)
#Get Bids Data
Bids = get_bids(real_data)
#Get Asks
Asks = get_asks(real_data)
#Bids after Cali
minmaxvar_bids = data_bidask['minmaxvar_bidsmodel_MK']
wang_bids = data_bidask['Wang_bidsmodel_MK']
#Asks after Cali
minmaxvar_asks = data_bidask['minmaxvar_asksmodel_MK']
wang_asks = data_bidask['Wang_asksmodel_MK']
#Implied liquidity after cali
IL_minmaxvar = data_bidask['minmaxvar_impliedliquiditysurface_MK']
IL_wang = data_bidask['Wang_impliedliquiditysurface_MK']
#Bids after Cali for new distorion functions
t_minmaxWang_bidsmodel = data_bidask['t_minmaxWang_bidsmodel_MK']
comp_Wangminmax_bidsmodel = data_bidask['comp_Wangminmax_bidsmodel_MK']
comp_minmaxWang_bidsmodel = data_bidask['comp_minmaxWang_bidsmodel_MK']
#Asks after Cali for new distortion functions
t_minmaxWang_asksmodel = data_bidask['t_minmaxWang_asksmodel_MK']
comp_Wangminmax_asksmodel = data_bidask['comp_Wangminmax_asksmodel_MK']
comp_minmaxWang_asksmodel = data_bidask['comp_minmaxWang_asksmodel_MK']
#t-parameters for new hybrid distortion functions
t_parameter_minmaxWang = data_bidask['t_minmaxWang_MK']
#Implied liquidity obtained by new distortion functions
t_minmaxWang_impliedliquiditysurface = data_bidask['t_minmaxWang_impliedliquiditysurface_MK']
comp_Wangminmaxvar_impliedliquiditysurface = data_bidask['comp_Wangminmaxvar_impliedliquiditysurface_MK']
comp_minmaxWang_impliedliquiditysurface = data_bidask['comp_minmaxWang_impliedliquiditysurface_MK']
#Sensitivity parametrs
#bids
sensitivity_minmaxvar_bidsmodel_MK = data_bidask['sensitivity_minmaxvar_bidsmodel_MK']
sensitivity_Wang_bidsmodel_MK = data_bidask['sensitivity_Wang_bidsmodel_MK']
sensitivity_t_minmaxWang_bidsmodel_MK = data_bidask['sensitivity_t_minmaxWang_bidsmodel_MK']
sensitivity_comp_Wangminmax_bidsmodel_MK = data_bidask['sensitivity_comp_Wangminmax_bidsmodel_MK']
sensitivity_comp_minmaxWang_bidsmodel_MK = data_bidask['sensitivity_comp_minmaxWang_bidsmodel_MK']
#asks
sensitivity_minmaxvar_asksmodel_MK = data_bidask['sensitivity_minmaxvar_asksmodel_MK']
sensitivity_Wang_asksmodel_MK = data_bidask['sensitivity_Wang_asksmodel_MK']
sensitivity_t_minmaxWang_asksmodel_MK = data_bidask['sensitivity_t_minmaxWang_asksmodel_MK']
sensitivity_comp_Wangminmax_asksmodel_MK = data_bidask['sensitivity_comp_Wangminmax_asksmodel_MK']
sensitivity_comp_minmaxWang_asksmodel_MK = data_bidask['sensitivity_comp_minmaxWang_asksmodel_MK']
# np.savez(parent_dir + '\\PastCalibrations\\' + calibration_name + '\\'+'bidaskCaliData', mean_payoffs_MK = mean_payoffs_MK,
# minmaxvar_impliedliquiditysurface_MK = minmaxvar_impliedliquiditysurface_MK, minmaxvar_bidsmodel_MK = minmaxvar_bidsmodel_MK,
# sensitivity_minmaxvar_bidsmodel_MK = sensitivity_minmaxvar_bidsmodel_MK, minmaxvar_asksmodel_MK = minmaxvar_asksmodel_MK, sensitivity_minmaxvar_asksmodel_MK = sensitivity_minmaxvar_asksmodel_MK, Wang_impliedliquiditysurface_MK = Wang_impliedliquiditysurface_MK,
# Wang_bidsmodel_MK = Wang_bidsmodel_MK, sensitivity_Wang_bidsmodel_MK = sensitivity_Wang_bidsmodel_MK, Wang_asksmodel_MK = Wang_asksmodel_MK, sensitivity_Wang_asksmodel_MK = sensitivity_Wang_asksmodel_MK, t_minmaxWang_impliedliquiditysurface_MK = t_minmaxWang_impliedliquiditysurface_MK,
# t_minmaxWang_bidsmodel_MK = t_minmaxWang_bidsmodel_MK , sensitivity_t_minmaxWang_bidsmodel_MK=sensitivity_t_minmaxWang_bidsmodel_MK, t_minmaxWang_asksmodel_MK = t_minmaxWang_asksmodel_MK,
# sensitivity_t_minmaxWang_asksmodel_MK = sensitivity_t_minmaxWang_asksmodel_MK, t_minmaxWang_MK = t_minmaxWang_MK, comp_Wangminmaxvar_impliedliquiditysurface_MK = comp_Wangminmaxvar_impliedliquiditysurface_MK,
# comp_Wangminmax_bidsmodel_MK = comp_Wangminmax_bidsmodel_MK, sensitivity_comp_Wangminmax_bidsmodel_MK = sensitivity_comp_Wangminmax_bidsmodel_MK,
# comp_Wangminmax_asksmodel_MK=comp_Wangminmax_asksmodel_MK, sensitivity_comp_Wangminmax_asksmodel_MK=sensitivity_comp_Wangminmax_asksmodel_MK, comp_minmaxWang_impliedliquiditysurface_MK = comp_minmaxWang_impliedliquiditysurface_MK,
# comp_minmaxWang_bidsmodel_MK = comp_minmaxWang_bidsmodel_MK, sensitivity_comp_minmaxWang_bidsmodel_MK=sensitivity_comp_minmaxWang_bidsmodel_MK,
# comp_minmaxWang_asksmodel_MK = comp_minmaxWang_asksmodel_MK, sensitivity_comp_minmaxWang_asksmodel_MK = sensitivity_comp_minmaxWang_asksmodel_MK)
#Plot the implied volatility
moneyness = np.array(get_strikes(real_data))/get_spot(real_data)
# defining surface and axes
y = np.array(get_maturities(real_data))
x = moneyness[0]
x, y = np.meshgrid(x, y)
z1 = data['iV_data']
fig = plt.figure(figsize=(7, 5))
# syntax for 3-D plotting
ax = plt.axes(projection ='3d')
# syntax for plotting
ax.plot_surface(x, y, z1, cmap ='viridis', edgecolor ='green')
ax.set_title('Implied volatility surface')
ax.set_ylabel('Time to maturity (years)')
ax.set_xlabel('Moneyness')
# plt.show()
name_fig = 'PastCalibrations\\' + calibration_name + "\\implied_volatility_surface.eps"
plt.savefig(name_fig, dpi=1200, format = 'eps')
plt.close()
#Plot the implied volatility squared error after Calibration
moneyness = np.array(get_strikes(real_data))/get_spot(real_data)
# defining surface and axes
y = np.array(get_maturities(real_data))
x = moneyness[0]
x, y = np.meshgrid(x, y)
z1 = data['iV_data']
z2 = data['iV_model']
z = (z1 - z2)**2
fig = plt.figure(figsize=(7, 5))
# syntax for 3-D plotting
ax = plt.axes(projection ='3d')
# syntax for plotting
ax.plot_surface(x, y, z, cmap ='viridis', edgecolor ='yellow')
ax.set_title('Implied volatility squared error')
ax.set_ylabel('Time to maturity (years)')
ax.set_xlabel('Moneyness')
name_fig = 'PastCalibrations\\' + calibration_name + "\\implied_volatility_squared_error_surface.eps"
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
plt.close()
#Plot implied volatility model vs data
for i in range(len(Mat)):
x_points = moneyness[i]
y1_points = IV_data[i]
y2_points = IV_model[i]
error_iv = y1_points - y2_points
# print('The error average absolute error for maturity {}'.format(Mat[i]), np.mean(np.abs(error_iv)))
# print('MAX ERROR', np.max(np.abs(error_iv)))
# print('INDEX MAX ERROR', list(np.abs(error_iv)).index(np.max(np.abs(error_iv))))
#Figure of Smiles
plt.figure(figsize=(7, 5))
plt.plot(x_points, y1_points, label = 'Data implied vol')
plt.plot(x_points, y2_points, label = 'LSV-SABR fit implied vol')
plt.ylabel('Implied Volatility')
plt.xlabel('Moneyness')
plt.title('Volatility smile (T = {})'.format(Mat[i]))
plt.legend()
#print('\n\n Getting bids and ask for maturity {} and strike {}\n\n'.format(i,k))
name_fig = 'PastCalibrations\\' + calibration_name + "\\LSV_SABR_implied_vol_smile_mat{}.eps"
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
plt.close()
#Fig Error
plt.figure(figsize=(7, 5))
plt.plot(x_points, error_iv)
plt.ylabel('Error')
plt.xlabel('Moneyness')
plt.title('Implied volatility error (T = {})'.format(Mat[i]))
#print('\n\n Getting bids and ask for maturity {} and strike {}\n\n'.format(i,k))
name_fig = 'PastCalibrations\\' + calibration_name + "\\implied_volatility_error_mat{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
plt.close()
#Plot Bid Prices for ATM options
#MINMAXVAR
#X axis is the same
x_points = Mat
y1_points = [i[12] for i in prices_data]
y2_points = [i[12] for i in Bids]
y3_points = [i[12] for i in minmaxvar_bids]
plt.figure(figsize=(7, 5))
plt.plot(x_points, y1_points, '.', label = "Mid-price", color = 'red')
plt.plot(x_points, y2_points, '1', label = "Market bids", color = 'orange')
plt.plot(x_points, y3_points, '1', label = "Calibrated bids", color = 'blue', alpha = 0.5)
plt.title('Minmax distortion bid prices (ATM)')
plt.xlabel('Maturity')
plt.ylabel('Price')
plt.legend()
name_fig = 'PastCalibrations\\' + calibration_name + "\\bid_atm_cali_prices_minmax.eps"
plt.savefig(name_fig, dpi=1200, format = 'eps')
plt.close()
#WANG
#X axis is the same
x_points = Mat
y1_points = [i[12] for i in prices_data]
y2_points = [i[12] for i in Bids]
y3_points = [i[12] for i in wang_bids]
plt.figure(figsize=(7, 5))
plt.plot(x_points, y1_points, '.', label = "Mid-price", color = 'red')
plt.plot(x_points, y2_points, '1', label = "Market bids", color = 'orange')
plt.plot(x_points, y3_points, '1', label = "Calibrated bids", color = 'blue', alpha = 0.5)
plt.title('Wang distortion bid prices (ATM)')
plt.xlabel('Maturity')
plt.ylabel('Price')
plt.legend()
name_fig = 'PastCalibrations\\' + calibration_name + "\\bid_atm_cali_prices_wang.eps"
plt.savefig(name_fig, dpi=1200, format = 'eps')
plt.close()
#Plot Ask Prices for ATM options
#MINMXVAR
#X axis is the same
x_points = Mat
y1_points = [i[12] for i in prices_data]
y2_points = [i[12] for i in Asks]
y3_points = [i[12] for i in minmaxvar_asks]
plt.figure(figsize=(7, 5))
plt.plot(x_points, y1_points, '.', label = "Mid-price", color = 'red')
plt.plot(x_points, y2_points, '2', label = "Market asks", color = 'orange')
plt.plot(x_points, y3_points, '2', label = "Calibrated asks", color = 'blue', alpha = 0.5)
plt.title('Minmax distortion ask prices (ATM)')
plt.xlabel('Maturity')
plt.ylabel('Price')
plt.legend()
name_fig = 'PastCalibrations\\' + calibration_name + "\\ask_atm_cali_prices_minmax.eps"
plt.savefig(name_fig, dpi=1200, format = 'eps')
plt.close()
#WANG
#X axis is the same
x_points = Mat
y1_points = [i[12] for i in prices_data]
y2_points = [i[12] for i in Asks]
y3_points = [i[12] for i in wang_asks]
plt.figure(figsize=(7, 5))
plt.plot(x_points, y1_points, '.', label = "Mid-price", color = 'red')
plt.plot(x_points, y2_points, '2', label = "Market asks", color = 'orange')
plt.plot(x_points, y3_points, '2', label = "Calibrated asks", color = 'blue', alpha = 0.5)
plt.title('Wang distortion ask prices (ATM)')
plt.xlabel('Maturity')
plt.ylabel('Price')
plt.legend()
name_fig = 'PastCalibrations\\' + calibration_name + "\\ask_atm_cali_prices_wang.eps"
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
plt.close()
#Plot Bid and Ask IV
#BID
for i in range(len(Mat)):
#X axis is the same
x_points = moneyness[i]
#Real Mid-prices
mid_prices = prices_data
mid_implvola = IV_data[i]
#Real bids IV
y1_points = Bids[i]
y1_implvola = []
for x in range(len(y1_points)):
y = implVola(y1_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y1_implvola.append(y)
#Minmax bid IV
y3_points = minmaxvar_bids[i]
y3_implvola = []
for x in range(len(y3_points)):
y = implVola(y3_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y3_implvola.append(y)
#Wang bid IV
y5_points = wang_bids[i]
y5_implvola = []
for x in range(len(y5_points)):
y = implVola(y5_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y5_implvola.append(y)
plt.subplots(figsize=(7, 5))
plt.plot(x_points, mid_implvola, '.', label = "Mid-price IV", color = 'red')
plt.plot(x_points, y1_implvola, '1', label = "Market bids IV", color = 'orange')
plt.plot(x_points, y3_implvola, '1', label = "Minmax bid IV", color = 'blue', alpha = 0.5)
plt.plot(x_points, y5_implvola, '1', label = "Wang bid IV", color = 'green', alpha = 0.5)
plt.title('Calibrated bids IV (T = {})'.format(Mat[i]), fontsize = 12)
plt.xlabel('Moneyness')
plt.ylabel('IV')
plt.legend()
name_fig = 'PastCalibrations\\' + calibration_name + "\\bid_cali_pureIV_mat{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
plt.close()
#ASK
for i in range(len(Mat)):
#X axis is the same
x_points = moneyness[i]
#Real Mid-prices
mid_prices = prices_data
mid_implvola = IV_data[i]
#Real bids IV
y1_points = Asks[i]
y1_implvola = []
for x in range(len(y1_points)):
y = implVola(y1_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y1_implvola.append(y)
#Minmax bid IV
y3_points = minmaxvar_asks[i]
y3_implvola = []
for x in range(len(y3_points)):
y = implVola(y3_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y3_implvola.append(y)
#Wang bid IV
y5_points = wang_asks[i]
y5_implvola = []
for x in range(len(y5_points)):
y = implVola(y5_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y5_implvola.append(y)
plt.subplots(figsize=(7, 5))
plt.plot(x_points, mid_implvola, '.', label = "Mid-price IV", color = 'red')
plt.plot(x_points, y1_implvola, '1', label = "Market asks IV", color = 'orange')
plt.plot(x_points, y3_implvola, '1', label = "Minmax asks IV", color = 'blue', alpha = 0.5)
plt.plot(x_points, y5_implvola, '1', label = "Wang asks IV", color = 'green', alpha = 0.5)
plt.title('Calibrated asks IV (T = {})'.format(Mat[i]), fontsize = 12)
plt.xlabel('Moneyness')
plt.ylabel('IV')
plt.legend()
name_fig = 'PastCalibrations\\' + calibration_name + "\\asks_cali_pureIV_mat{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
plt.close()
sum_minmax_bid_error = []
sum_wang_bid_error = []
#Plot implied vola error Bids
for i in range(0,len(Mat)):
#X axis is the same
x_points = moneyness[i]
#Real bids IV
y1_points = Bids[i]
y1_implvola = []
for x in range(len(y1_points)):
y = implVola(y1_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y1_implvola.append(y)
#Minmax bid IV
y3_points = minmaxvar_bids[i]
y3_implvola = []
for x in range(len(y3_points)):
y = implVola(y3_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y3_implvola.append(y)
#Wang bid
y5_points = wang_bids[i]
y5_implvola = []
for x in range(len(y5_points)):
y = implVola(y5_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y5_implvola.append(y)
#Create Errror list
error_bid_IV_minmaxvar = np.array(y1_implvola) - np.array(y3_implvola)
error_bid_IV_wang = np.array(y1_implvola) - np.array(y5_implvola)
# print('error_bid_IV_minmaxvar')
##ASK ERROR
sum_minmax_bid_error.append(np.sum(np.abs(error_bid_IV_minmaxvar)))
sum_wang_bid_error.append(np.sum(np.abs(error_bid_IV_wang )))
plt.subplots(figsize=(14, 5))
# using subplot function and creating
# plot one
plt.subplot(1, 2, 1)
plt.plot(x_points, error_bid_IV_minmaxvar, label = "IV error", color = 'darkblue')
plt.title('Minmax distortion')
plt.xlabel('Moneyness')
plt.ylabel('Error')
plt.legend()
# using subplot function and creating plot two
plt.subplot(1, 2, 2)
plt.plot(x_points, error_bid_IV_wang, label = "IV error", color = 'darkblue')
plt.title('Wang distortion')
plt.xlabel('Moneyness')
plt.ylabel('Error')
plt.legend()
# space between the plots
# plt.tight_layout()
# show plot
plt.suptitle('Bid calibration IV error(T = {})'.format(Mat[i]), fontsize = 12)
name_fig = 'PastCalibrations\\' + calibration_name + "\\bid_cali_IVerror_mat{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
plt.close()
print("The Average bid error of minmaxvar function ", np.sum(sum_minmax_bid_error)/120)
print("The Average bid error of wang function", np.sum(sum_wang_bid_error)/120)
sum_minmax_ask_error = []
sum_wang_ask_error = []
#Plot implied vola error ask
for i in range(0,len(Mat)):
#X axis is the same
x_points = moneyness[i]
#Real bids IV
y2_points = Asks[i]
y2_implvola = []
for x in range(len(y2_points)):
y = implVola(y2_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y2_implvola.append(y)
#Minmax bid IV
y4_points = minmaxvar_asks[i]
y4_implvola = []
for x in range(len(y4_points)):
y = implVola(y4_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y4_implvola.append(y)
#Wang bid-ask
y6_points = wang_asks[i]
y6_implvola = []
for x in range(len(y6_points)):
y = implVola(y6_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y6_implvola.append(y)
#Create Errror list
error_ask_IV_minmaxvar = np.array(y2_implvola) - np.array(y4_implvola)
error_ask_IV_wang = np.array(y2_implvola) - np.array(y6_implvola)
##ASK ERROR
sum_minmax_ask_error.append(np.sum(np.abs(error_ask_IV_minmaxvar)))
sum_wang_ask_error.append(np.sum(np.abs(error_ask_IV_wang )))
#
plt.subplots(figsize=(14, 5))
# using subplot function and creating
# plot one
plt.subplot(1, 2, 1)
plt.plot(x_points, error_ask_IV_minmaxvar, label = "IV error", color = 'darkblue')
plt.title('Minmax distortion')
plt.xlabel('Moneyness')
plt.ylabel('IV error')
plt.legend()
# using subplot function and creating plot two
plt.subplot(1, 2, 2)
plt.plot(x_points, error_ask_IV_wang, label = "IV error", color = 'darkblue')
plt.title('Wang distortion')
plt.xlabel('Moneyness')
plt.ylabel('IV error')
plt.legend()
# space between the plots
# plt.tight_layout()
# show plot
plt.suptitle('Ask calibration IV error(T = {})'.format(Mat[i]), fontsize = 12)
name_fig = 'PastCalibrations\\' + calibration_name + "\\ask_cali_IVerror_mat{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
plt.close()
print("The Average ask error of minmaxvar function ", np.sum(sum_minmax_ask_error)/120)
print("The Average ask error of wang function", np.sum(sum_wang_ask_error)/120)
############## T-Minmax-Wang ##################
sum_t_bid_error = []
sum_t_ask_error = []
for i in range(len(Mat)):
x_points = moneyness[i]
###Load Real Bids and Asks (Caculate their respective implied volatilies)
#Real bids IV
y1_points = Bids[i]
y1_implvola = []
for x in range(len(y1_points)):
y = implVola(y1_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y1_implvola.append(y)
#Real asks IV
y2_points = Asks[i]
y2_implvola = []
for x in range(len(y2_points)):
y = implVola(y2_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y2_implvola.append(y)
#Load Model Bid and Asks (t)
#Bid
y3_points = t_minmaxWang_bidsmodel[i]
y3_implvola = []
for x in range(len(y3_points)):
y = implVola(y3_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y3_implvola.append(y)
#Ask
y4_points = t_minmaxWang_asksmodel[i]
y4_implvola = []
for x in range(len(y4_points)):
y = implVola(y4_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y4_implvola.append(y)
#Calculate Errors
bid_error_iv_t_minmaxwang = np.array(y1_implvola) - np.array(y3_implvola)
ask_error_iv_t_minmaxwang = np.array(y2_implvola) - np.array(y4_implvola)
sum_t_bid_error.append(np.sum(np.abs(bid_error_iv_t_minmaxwang)))
sum_t_ask_error.append(np.sum(np.abs(ask_error_iv_t_minmaxwang)))
#Load t-parameter
t_parameter_minmaxWang_mat = t_parameter_minmaxWang[i]
#Fig Error Bid
plt.figure(figsize=(7, 5))
plt.plot(x_points, bid_error_iv_t_minmaxwang, label = "IV error", color = 'darkblue')
plt.ylabel('Error')
plt.xlabel('Moneyness')
plt.title('Bid Implied volatility error (T = {})'.format(Mat[i]))
#print('\n\n Getting bids and ask for maturity {} and strike {}\n\n'.format(i,k))
name_fig = 'PastCalibrations\\' + calibration_name + "\\bid_error_iv_t_minmaxwang_mat{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
plt.close()
#Fig Error Ask
plt.figure(figsize=(7, 5))
plt.plot(x_points, ask_error_iv_t_minmaxwang, label = "IV error", color = 'darkblue')
plt.ylabel('Error')
plt.xlabel('Moneyness')
plt.title('Ask Implied volatility error (T = {})'.format(Mat[i]))
#print('\n\n Getting bids and ask for maturity {} and strike {}\n\n'.format(i,k))
name_fig = 'PastCalibrations\\' + calibration_name + "\\ask_error_iv_t_minmaxwang_mat{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
plt.close()
#Fig T-parameter
plt.figure(figsize=(7, 5))
plt.ylim(-0.05, 1.05)
plt.scatter(x_points, t_parameter_minmaxWang_mat)
plt.ylabel('Error')
plt.xlabel('Moneyness')
plt.title('t-parameter for maturity (T = {})'.format(Mat[i]))
#print('\n\n Getting bids and ask for maturity {} and strike {}\n\n'.format(i,k))
name_fig = 'PastCalibrations\\' + calibration_name + "\\t_parameter_minmaxWang_mat{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
plt.close()
# #Asks after Cali for new distortion functions
# t_minmaxWang_asksmodel = data_bidask['t_minmaxWang_asksmodel_MK']
# comp_Wangminmax_asksmodel = data_bidask['comp_Wangminmax_asksmodel_MK']
# comp_minmaxWang_asksmodel = data_bidask['comp_minmaxWang_asksmodel_MK']
# #t-parameters for new hybrid distortion functions
# t_parameter_minmaxWang = data_bidask['t_minmaxWang_MK']
print("The Average bid error of new T-minmaxvar function", np.sum(sum_t_bid_error)/120)
print("The Average ask error of new T-minmaxvar function", np.sum(sum_t_ask_error)/120)
sum_t_bid_error = []
sum_t_ask_error = []
####################Composition of two function###########
sum_bid_error_minmaxwang = []
sum_bid_error_wangminmax = []
#Plot implied vola error bid
for i in range(len(Mat)):
#X axis is the same
x_points = moneyness[i]
#Real bids IV
y1_points = Bids[i]
y1_implvola = []
for x in range(len(y1_points)):
y = implVola(y1_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y1_implvola.append(y)
#Minmax bid IV
y3_points = comp_Wangminmax_bidsmodel[i]
y3_implvola = []
for x in range(len(y3_points)):
y = implVola(y3_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y3_implvola.append(y)
#Wang bid-ask
y5_points = comp_minmaxWang_bidsmodel[i]
y5_implvola = []
for x in range(len(y5_points)):
y = implVola(y5_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y5_implvola.append(y)
#Create Errror list
# print("Minmax-wang IV", y3_implvola)
# print("Wang-Minmax IV", y5_implvola)
#Error Minmax-Wang
error_ask_IV_Minmaxvar_Wang = np.array(y1_implvola) - np.array(y3_implvola)
#Error Wang-Minmaxvar
error_ask_IV_Wang_Minmaxvar = np.array(y1_implvola) - np.array(y5_implvola)
#
sum_bid_error_minmaxwang.append(np.sum(np.abs(error_ask_IV_Minmaxvar_Wang )))
sum_bid_error_wangminmax.append(np.sum(np.abs(error_ask_IV_Wang_Minmaxvar)))
plt.subplots(figsize=(14, 5))
# using subplot function and creating
# plot one
plt.subplot(1, 2, 1)
plt.plot(x_points, error_ask_IV_Minmaxvar_Wang, label = "IV error", color = 'darkblue')
plt.title('The Minmaxvar-Wang distortion')
plt.xlabel('Moneyness')
plt.ylabel('IV error')
plt.legend()
# using subplot function and creating plot two
plt.subplot(1, 2, 2)
plt.plot(x_points, error_ask_IV_Wang_Minmaxvar, label = "IV error", color = 'darkblue')
plt.title('The Wang-Minmaxvar distortion')
plt.xlabel('Moneyness')
plt.ylabel('IV error')
plt.legend()
# space between the plots
# plt.tight_layout()
# show plot
plt.suptitle('Bid calibration IV error(T = {})'.format(Mat[i]), fontsize = 12)
name_fig = 'PastCalibrations\\' + calibration_name + "\\bid_cali_comp_IVerror_mat{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
plt.close()
print("The Average bid error of new Minmaxvar-Wang function", np.sum(sum_bid_error_minmaxwang)/120)
print("The Average bid error of new Wang-Minmaxvar function", np.sum(sum_bid_error_wangminmax)/120)
sum_ask_error_minmaxwang = []
sum_ask_error_wangminmax = []
#Plot implied vola error ask for composition of functions
for i in range(len(Mat)):
#X axis is the same
x_points = moneyness[i]
#Real asks IV
y2_points = Asks[i]
y2_implvola = []
for x in range(len(y2_points)):
y = implVola(y2_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y2_implvola.append(y)
#Minmax-Wang ask IV
y4_points = comp_Wangminmax_asksmodel[i]
y4_implvola = []
for x in range(len(y4_points)):
y = implVola(y4_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y4_implvola.append(y)
#Wang-Minmaxvar ask IV
y6_points = comp_minmaxWang_asksmodel[i]
y6_implvola = []
for x in range(len(y6_points)):
y = implVola(y6_points[x], S0, K[i][x], Mat[i], 0.0, 'c')
y6_implvola.append(y)
#Create Error list
#Error Minmax-Wang
error_ask_IV_Minmaxvar_Wang = np.array(y2_implvola) - np.array(y4_implvola)
#Error Wang-Minmaxvar
error_ask_IV_Wang_Minmaxvar = np.array(y2_implvola) - np.array(y6_implvola)
###
sum_ask_error_minmaxwang.append(np.sum(np.abs(error_ask_IV_Minmaxvar_Wang )))
sum_ask_error_wangminmax.append(np.sum(np.abs(error_ask_IV_Wang_Minmaxvar)))
#
plt.subplots(figsize=(14, 5))
# using subplot function and creating
# plot one
plt.subplot(1, 2, 1)
plt.plot(x_points, error_ask_IV_Minmaxvar_Wang, label = "IV error", color = 'darkblue')
plt.title('The Minmaxvar-Wang distortion')
plt.xlabel('Moneyness')
plt.ylabel('IV error')
plt.legend()
# using subplot function and creating plot two
plt.subplot(1, 2, 2)
plt.plot(x_points, error_ask_IV_Wang_Minmaxvar, label = "IV error", color = 'darkblue')
plt.title('The Wang-Minmaxvar distortion')
plt.xlabel('Moneyness')
plt.ylabel('IV error')
plt.legend()
# space between the plots
# plt.tight_layout()
# show plot
plt.suptitle('Ask calibration IV error(T = {})'.format(Mat[i]), fontsize = 12)
name_fig = 'PastCalibrations\\' + calibration_name + "\\ask_cali_comp_IVerror_mat{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
plt.show()
# plt.close()
print("The Average ask error of new Minmaxvar-Wang function", np.sum(sum_ask_error_minmaxwang)/120)
print("The Average ask error of new Wang-Minmaxvar function", np.sum(sum_ask_error_wangminmax)/120)
#####PLOT THE IMPLIED LIQUIDITY
#Plot Implied liquidity Minmax
moneyness = np.array(get_strikes(real_data))/get_spot(real_data)
# defining surface and axes
y = np.array(get_maturities(real_data))
x = moneyness[0]
x, y = np.meshgrid(x, y)
z1 = IL_minmaxvar
fig = plt.figure(figsize=(7, 5))
# syntax for 3-D plotting
ax = plt.axes(projection ='3d')
# syntax for plotting
ax.plot_surface(x, y, z1, cmap ='viridis', edgecolor ='blue')
ax.set_title('Implied liquidity surface (minmaxvar distortion)')
ax.set_ylabel('Time to maturity (years)')
ax.set_xlabel('Moneyness')
# plt.show()
name_fig = 'PastCalibrations\\' + calibration_name + "\\implied_liquidity_surface_minmaxvar.eps"
plt.savefig(name_fig, dpi=1200, format = 'eps')
#Plot Implied liquidity Wang transform
moneyness = np.array(get_strikes(real_data))/get_spot(real_data)
# defining surface and axes
y = np.array(get_maturities(real_data))
x = moneyness[0]
x, y = np.meshgrid(x, y)
z1 = IL_wang
fig = plt.figure(figsize=(7, 5))
# syntax for 3-D plotting
ax = plt.axes(projection ='3d')
# syntax for plotting
ax.plot_surface(x, y, z1, cmap ='viridis', edgecolor ='blue')
ax.set_title('Implied liquidity surface (wang distortion)')
ax.set_ylabel('Time to maturity (years)')
ax.set_xlabel('Moneyness')
# plt.show()
name_fig = 'PastCalibrations\\' + calibration_name + "\\implied_liquidity_surface_wang.eps"
plt.savefig(name_fig, dpi=1200, format = 'eps')
#Plot the sensitivity parameters
##Get implied Volatility
# implVola(model_price[iter], self.spot, K, mat, 0.0, 'c')
#Sensitivity parametrs
#bids
sensitivity_minmaxvar_bidsmodel_MK = data_bidask['sensitivity_minmaxvar_bidsmodel_MK']
sensitivity_Wang_bidsmodel_MK = data_bidask['sensitivity_Wang_bidsmodel_MK']
sensitivity_t_minmaxWang_bidsmodel_MK = data_bidask['sensitivity_t_minmaxWang_bidsmodel_MK']
#asks
sensitivity_minmaxvar_asksmodel_MK = data_bidask['sensitivity_minmaxvar_asksmodel_MK']
sensitivity_Wang_asksmodel_MK = data_bidask['sensitivity_Wang_asksmodel_MK']
sensitivity_t_minmaxWang_asksmodel_MK = data_bidask['sensitivity_t_minmaxWang_asksmodel_MK']
############## Sensitivity Parameters ##################
for i in range(len(Mat)):
#X points
x_points = moneyness[i]
#Bid Sensitivities
s_bid_minmax = sensitivity_minmaxvar_bidsmodel_MK[i]
s_bid_wang = sensitivity_Wang_bidsmodel_MK[i]
s_bid_tminmaxwang = sensitivity_t_minmaxWang_bidsmodel_MK[i]
#Ask Sensitivites
s_ask_minmax = sensitivity_minmaxvar_asksmodel_MK[i]
s_ask_wang =sensitivity_Wang_asksmodel_MK[i]
s_ask_tminmaxwang = sensitivity_t_minmaxWang_asksmodel_MK[i]
#Fig Error Bid
plt.figure(figsize=(7, 5))
plt.plot(x_points, s_bid_minmax, label = "Minmaxvar", color = 'blue')
plt.plot(x_points, s_bid_wang, label = "Wang", color = 'orange')
plt.plot(x_points, s_bid_tminmaxwang, label = "t-Minmax-Wang", color = 'green')
plt.ylabel('Sensitivity')
plt.xlabel('Moneyness')
plt.title('Sensitivity Parameters bid (T = {})'.format(Mat[i]))
plt.legend()
#print('\n\n Getting bids and ask for maturity {} and strike {}\n\n'.format(i,k))
name_fig = 'PastCalibrations\\' + calibration_name + "\\bid_error_sensitivities{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
#Fig Error Ask
plt.figure(figsize=(7, 5))
plt.plot(x_points, s_ask_minmax, label = "Minmaxvar", color = 'blue')
plt.plot(x_points, s_ask_wang, label = "Wang", color = 'orange')
plt.plot(x_points, s_ask_tminmaxwang, label = "t-Minmax-Wang", color = 'green')
plt.ylabel('Sensitivity')
plt.xlabel('Moneyness')
plt.title('Sensitivity Parameters ask (T = {})'.format(Mat[i]))
plt.legend()
#print('\n\n Getting bids and ask for maturity {} and strike {}\n\n'.format(i,k))
name_fig = 'PastCalibrations\\' + calibration_name + "\\ask_error_sensitivities{}.eps".format(Mat[i])
plt.savefig(name_fig, dpi=1200, format = 'eps')
# plt.show()
#Fig T-parameter
# plt.figure(figsize=(7, 5))
# plt.ylim(-0.05, 1.05)
# plt.scatter(x_points, t_parameter_minmaxWang_mat)
# plt.ylabel('Error')
# plt.xlabel('Moneyness')
# plt.title('t-parameter for maturity (T = {})'.format(Mat[i]))
# #print('\n\n Getting bids and ask for maturity {} and strike {}\n\n'.format(i,k))
# name_fig = 'PastCalibrations\\' + calibration_name + "\\t_parameter_minmaxWang_mat{}.eps".format(Mat[i])
# plt.savefig(name_fig, dpi=1200, format = 'eps')
# # plt.show()
# plt.close()