def main():
#codes = session.execute('select distinct(code) from day_k_data;').fetchall()
codes = [['MOMO']]
rate = {}
start_date = '2017-01-04'
date_delta = -date_today_delta(start_date, '%Y-%m-%d')
norm_type = 'character'
quota_index = 0
codes = [code[0] for code in codes]
#date_deltas = range(50,200,20)
#start_dates = [date_add(start_date,i) for i in range(10,50,10)]
#df = pd.read_sql('select * from day_k_data where code="MOMO" order by date asc;',engine)
#rate = single_test(df,start_dates,date_deltas,norm_type,quota_index)
#open_market_dates = session.execute('select distinct(code),date from day_k_data;').fetchall()
for code in codes[:1]:
print code
try:
df = pd.read_sql(
'select * from day_k_data where code="' + code +
'" order by date asc;', engine)
for i in range(len(df['date'])):
df.date[i] = df.date[i].split(' ')[0]
r = back_test(df, start_date, date_delta, norm_type, quota_index)
if r:
rate[code] = r
except Exception, e:
print e, 'line 212'
continue
def write_to_database(options_info):
for code in options_info:
for date in options_info[code]:
if date_today_delta(date) <= 0:
continue
dfop = pd.DataFrame(options_info[code][date]).set_index('17Strike')
dfop.insert(0,'date',date_today)
dfop = dfop.drop_duplicates()
dfop.to_sql('option_price',engine,if_exists='append')
def write_to_database(options_info):
df_back = pd.DataFrame()
for date in options_info:
if date_today_delta(date) <= 0:
continue
dfop = pd.DataFrame(options_info[date])
dfop.insert(0, 'date', date_today)
if df_back.empty:
df_back = dfop
df_back = pd.concat([df_back, dfop], axis=0)
df_back = df_back.set_index('17Strike')
df_back.to_sql('option_greeks', engine, if_exists='append')
#coding:utf-8
import numpy as np
from scipy.stats import norm
import datetime, commfunction
# 设定参数
r = 0.03975 # risk-free interest rate
expire_day = 'Jul 21, 2017' # time to expire
t = float(commfunction.date_today_delta(
expire_day, format='%b %d, %Y')) / 365 # time to expire (30 days)
q = 0 # dividend yield
S0 = 40.59 # underlying price
X = 39 # strike price
mktprice_call = 2.56 # market price
#mktprice_put = 5.7 # market price
# 用二分法求implied volatility，暂只针对call option
sigma = 0.65487 # initial volatility
def call_implied_sigma(S0, X, r, t, sigma, mktprice, q=0):
C = 0
upper = 1
lower = 0
while abs(C - mktprice) > 1e-4:
d1 = (np.log(S0 / X) +
(r - q + sigma**2 / 2) * t) / (sigma * np.sqrt(t))
d2 = d1 - sigma * np.sqrt(t)
C = S0 * np.exp(-q * t) * norm.cdf(d1) - X * np.exp(
-r * t) * norm.cdf(d2)
if C - mktprice > 0:
options_info[code][date][column_name].append('')
return options_info
for stock in stocks:
stock = 'YY'
options_info = get_option_pg_datas(
[stock],
pre='http://www.nasdaq.com/symbol/',
tail='/option-chain/greeks?dateindex=-1')
option_price_history = pd.read_sql(
'select * from option_greeks where `16Root`="' + stock +
'" order by date asc;', engine)
options_info = to_dataframe(options_info)
for d in options_info['18Puts']:
if date_today_delta(d) <= 0:
continue
for s in list(options_info[options_info['18Puts'] == d]['17Strike']):
t_price = options_info[options_info['18Puts'] == d]
t_price = t_price[t_price['17Strike'] == s].set_index('date')
h_prices = option_price_history[option_price_history['18Puts'] ==
d]
h_prices = h_prices[h_prices['17Strike'] == s].set_index('date')
iv = pd.concat(
[h_prices[['15IV', '24IV']], t_price[['15IV', '24IV']]],
axis=0)
print stock, d, s
print iv
try:
iv.plot()
plt.legend(('Calls_IV', 'PUTS_IV'), loc='upper left')