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hedging_real_data.py
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hedging_real_data.py
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import xlrd
from delta_hedging_mc import EuropeanCallOption, EuropeanPutOption
import pandas as pd
'''
The closing price of HC2110 in every 30 minutes from July 2, 2021 to July 29, 2021
Expiration time： 29 July 2021
'''
# import the real data (this part may be varied in the different cases)
workbook = xlrd.open_workbook('HC2110.xls')
table = workbook.sheets()[0]
col_stock = table.col_values(3)
col_time = table.col_values(5)
rows_number = table.nrows
l_strike = []
for z in range(0, rows_number):
l_strike.append(6011)
# delta s = (s(a) - s(a-1)) / s(a-1)
l_0 = [] # list of s(a-1)
l_1 = [] # list of s(a)
l_ds = [0] # list of delta s
for x in range(0, rows_number - 1):
s_0_value = col_stock[x]
l_0.append(s_0_value)
for j in range(1, rows_number):
s_1_value = col_stock[j]
l_1.append(s_1_value)
if len(l_0) == len(l_1):
for z in range(0, rows_number - 1):
d_s = (l_1[z] - l_0[z]) / l_0[z]
l_ds.append(d_s)
class Hedging1:
def __init__(self, name, r, sig, towards, number):
self.total_delta_1 = self.total_delta(name, r, sig)
total_delta_2 = self.total_delta(name, r, sig)
self.underlying_position_1 = self.underlying_position(total_delta_2, towards, number)
underlying_position_2 = self.underlying_position(total_delta_2, towards, number)
self.pol_1 = self.pol(underlying_position_2)
self.totaling_value_1 = self.totaling_value(name, r, sig)
def total_delta(self, name, r, sig):
# s_l = stock list
# name: "c"=call, "p"=put
delta_number = []
for i in range(0, rows_number):
current_s = col_stock[i]
s0 = current_s
dt = col_time[i] / (360 * 24) # time unit = hour
k = l_strike[i]
if name == "c":
total = EuropeanCallOption(s0, k, r, sig, dt)
delta_number.append(total.delta_1)
else:
total = EuropeanPutOption(s0, k, r, sig, dt)
delta_number.append(total.delta_1)
return delta_number
def underlying_position(self, total_delta, towards, number):
# towards: buy=1, sell=-1
total_position = []
for i in range(0, rows_number):
b = total_delta[i]
position = -round(b * number, 0) * towards
total_position.append(position)
return total_position
def pol(self, underlying_position):
pol_in_stock = [0]
for i in range(1, rows_number):
pol_in_stock.append(
(col_stock[i] - col_stock[i - 1]) * underlying_position[i - 1])
return pol_in_stock
def totaling_value(self, name, r, sig):
# name: "c"=call, "p"=put
total_value = []
for i in range(0, rows_number):
s0 = col_stock[i] # s0 = current s
dt = col_time[i] / (360 * 24)
k = l_strike[i]
if name == "c":
total = EuropeanCallOption(s0, k, r, sig, dt)
else:
total = EuropeanPutOption(s0, k, r, sig, dt)
total_value.append(total.value_1)
return total_value
def hedging_in_fixed_gap(name, r, sig, towards, number):
# step 1: making a DataFrame
one_path = Hedging1(name, r, sig, towards, number)
list_stock = col_stock
list_strike = l_strike
list_ds = l_ds
list_value = one_path.totaling_value_1
list_delta = one_path.total_delta_1
list_position = one_path.underlying_position_1
list_time = col_time
my_dict = {'s': list_stock, 'k': list_strike, 'ds': list_ds, 'value': list_value,
'delta': list_delta,
'underlying_position': list_position, 'time': list_time}
total_list = pd.DataFrame(my_dict)
# step 2: choosing rows which satisfy the gap requirements (gap = sig / 16)
choose_dict = total_list[(total_list["ds"] > (sig / 16)) | (total_list["ds"] < -(sig / 16))]
# step 3: adding the initial row (when s = k)
a = list_stock[0]
b = list_strike[0]
c = list_ds[0]
d = list_value[0]
e = list_delta[0]
f = list_position[0]
g = list_time[0]
top_row = pd.DataFrame({'s': [a], 'k': [b], 'ds': [c], 'value': [d],
'delta': [e],
'underlying_position': [f], 'time': [g]})
choose_dict = pd.concat([top_row, choose_dict]).reset_index(drop=True)
# step 4: adding prior value of stock and underlying position for calculating PoL in stock
s_to_list = choose_dict["s"].tolist()
u_p_to_list = choose_dict["underlying_position"].tolist()
long = len(choose_dict)
s_for_cal = [0]
position_for_cal = [0]
for i in range(1, long):
s_cal = s_to_list[i - 1]
s_for_cal.append(s_cal)
position_cal = u_p_to_list[i - 1]
position_for_cal.append(position_cal)
choose_dict.insert(1, 's (prior)', s_for_cal, allow_duplicates=False)
choose_dict.insert(7, 'position (prior)', position_for_cal, allow_duplicates=False)
# step 5: calculating PoL in stock
choose_dict["PoL"] = (choose_dict["s"] - choose_dict["s (prior)"]) * choose_dict["position (prior)"]
# step 6: printing the DataFrame
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print(choose_dict)
# step 7: showing the result and profit
profit_in_options = (choose_dict.iloc[-1].iat[4] - choose_dict.iloc[0].iat[4]) * number
profit_in_stock = choose_dict["PoL"].sum()
benefit = profit_in_options + profit_in_stock
print("profit in options market is %d \n profit in stock market is %d \n the final profit is %d " % (
profit_in_options, profit_in_stock, benefit))
hedging_in_fixed_gap('c', 0.03, 0.21, 1, 100)