def estimate_last(self, args):
# initialize
N = len(self.z) # use close market's value
t_init = 0
y_init = self.z[t_init]
# BSM model
model = bsm.BSM(N, args.mu, args.sigma, t_init, y_init, self.seed)
y = model.predict()
#print(float(y[-1]),float(self.z[-1]),(float(y[-1])-float(self.z[-1])))
error = (float(y[-1]) - float(self.z[-1]))**2
return error
def estimate(self):
# initialize
N = len(self.z) # use close market's value
t_init = 0
y_init = self.z[t_init]
# BSM model
model = bsm.BSM(N, self.params["mu"], self.params["sgm"], t_init,
y_init, self.seed)
y = model.predict()
loss = model.calc_loss(np.array(self.z, dtype=float))
return loss, y
def import_iv():
# set option param
spot = 2.483
today = dt.date(2015, 1, 27)
risk_free = 0.05
qList = []
# 读取行情
with open('option_20150127.csv') as f:
f_csv = csv.reader(f)
headers = next(f_csv)
for row in f_csv:
price = float(row[5])
if (row[10] == "PO"):
mytype = -1
elif (row[10] == "CO"):
mytype = 1
else:
print "error"
strike = float(row[11])
T = (dt.datetime.strptime(row[9], "%Y/%m/%d").date() -
today).days / 365.0
qList.append(Quote(price, strike, T, mytype))
ivList = []
for i in qList:
# print (i.optionType, i.strike, riskFree, i.T, spot, i.optionPrice)
bsm = BSM.BSM(i.optionType, i.strike, risk_free, i.T, spot)
ivList.append(bsm.get_iv(i.optionType, i.optionPrice))
svi_sample_list = []
for i in range(0, len(ivList)):
svi_sample_list.append(
Svi_sample(ivList[i], (math.log(qList[i].strike / spot))))
#svi_sample_list_sub = svi_sample_list[1:6]+svi_sample_list[28:34]
svi_sample_list_sub = svi_sample_list[0:7] + svi_sample_list[28:35]
for i6 in svi_sample_list_sub:
print i6.iv, ",", i6.k
return svi_sample_list_sub
# read csv file
with open(args.csvfile, "r") as file:
reader = csv.reader(file)
header = next(reader)
for row in reader:
z.append(float(row[4]))
Nt = len(z)
init = z[0]
# calc BSM model
fig = plt.figure(figsize=(16, 10))
t = range(Nt)
for i in range(args.Nsample):
model = bsm.BSM(Nt, args.mu, args.sigma, t_init=0.0, y_init=init)
y = model.predict()
rows = args.Nsample / 2
cols = args.Nsample / rows
plt.subplot(cols, rows, i + 1)
# plot BTC data
plt.plot(t, z, "bs:", markeredgecolor="black")
# plot BSM model
plt.plot(t, y, "ro-", markeredgecolor="black")
plt.xlabel("time")
plt.ylabel("BTC")
plt.yscale("log")
del model
fig.suptitle("(mu,sigma)=(" + str(args.mu) + "," + str(args.sigma) + ")",
fontsize=30)
plt.savefig("BTC_BSM_mu" + str(args.mu) + "_s" + str(args.sigma) + ".png")
import BSM
T=34/365
r=None
spot=2.3440
option_dict={}
bsm_list=[]
#read data
file_name="D://option.csv"
with open(file_name) as f:
f_csv = csv.reader(f)
headers = next(f_csv)
for row in f_csv:
if(row[0]=="C"):
temp=1
elif row[0]=="P":
temp=-1
else:
print "error"
exit(0)
option_dict[(temp,float(row[1]))]=tuple([float(x) for x in row[5:11]])
for i in option_dict:
bsm=BSM.BSM(i[0],i[1],r,T,spot)
bsm.update
bsm_list.append()
def get_iv(self):
bsm = BSM.BSM(self.option_type, self.strike, self.interest,
self.param_t, self.future_price)
return bsm.get_iv(self.option_type, self.quote.last_price)
parser.add_argument("-se", "--seed", type=int, default=-1)
args = parser.parse_args()
z = list()
# read csv file
with open(args.csvfile, "r") as file:
reader = csv.reader(file)
header = next(reader)
for row in reader:
z.append(float(row[4]))
Nt = len(z)
init = z[0]
# calc BSM model
model = bsm.BSM(Nt, args.mu, args.sigma, y_init=init, seed=args.seed)
y = model.predict()
# plot
t = range(Nt)
# plot BSM model
plt.plot(t, y, "ro-", label="BSM model", markeredgecolor="black")
# plot BTC data
plt.plot(t, z, "bs:", label="market price", markeredgecolor="black")
plt.yscale("log")
plt.xlabel("time")
plt.ylabel("BTC")
plt.legend()
plt.show()