debug_t_dates = [1226901600,]
debug_expiration_dates = [1232172000,]
debug_symbols = ['FAS']
# if getting data, clear the existing data first or view_smiles
# will report errors (multiple hits for unique data)
if not DEBUG is True:
sqlite3tools.delete_sqlite_table(conn, TABLENAME)
if DEBUG is True and debug_symbols is not None:
symbols = debug_symbols
for symbol in symbols:
params = dict()
params['symbol'] = symbol
sql = SQL.get_t_dates % params
t_dates = execute_query_DF(conn, sql)('t_date')
if DEBUG is True and debug_t_dates is not None:
t_dates = debug_t_dates
for t_date in t_dates:
print
print symbol + ":",
print str(epoch2datetime(int(t_date)).date()).rjust(11),
params['t_date'] = t_date
sql = SQL.get_spot_closing_price % params
stock_price_data = execute_query_DF(conn, sql)('price_close_ini')
if not len(stock_price_data) == 1:
msg = "More than once spot closing price for %s on %s. Bad dog." % (symbol, t_date)
print len(stock_price_data)
raise Exception, msg
else:
spot_closing_price = stock_price_data[0]
from datetimetools import *
import SQL_view_smiles as SQL
import scipy
import datetime
from matplotlib import pyplot
conn = sqlite3.connect("Levered_Financial_ETF_Option_and_Stock_DB.sqlite3")
symbols = ['FAS','FAZ','SKF','UYG']
symbols = ['FAS',]
for symbol in symbols:
params = dict()
params['symbol'] = symbol
sql = SQL.get_t_dates % params
t_dates = execute_query_DF(conn, sql)
for t_date in t_dates('t_date'):
params['t_date'] = t_date
sql = SQL.get_expiration_dates % params
expiration_dates = execute_query_DF(conn, sql)
for expiration_date in expiration_dates('expiration_date'):
# Lets get some data
params['expiration_date'] = expiration_date
time_to_expiration = (float(expiration_date) - float(t_date)) / seconds_per_year
sql = SQL.get_implied_forward % params
implied_forward = execute_query_DF(conn, sql)('implied_forward')[0]
sql = SQL.get_strike_and_cvol_and_pvol_and_call_delta % params
sql = SQL.get_fit_smile % params
fit_params = execute_query_DF(conn, sql)
if not fit_params.numrows() == 1:
xmax = 500
ymin = .2
ymax = 3.5
pyplot.axis([xmin, xmax, ymin, ymax])
alpha_days = np.arange(0, 500, 2)
lb = [.3, .2, 1]
ub = [5, 3, 10]
if DEBUG is True and debug_symbols is not None:
symbols = debug_symbols
for symbol in symbols:
params = dict()
params['symbol'] = symbol
sql = SQL.get_t_dates % params
t_dates = execute_query_DF(conn, sql)('t_date')
if DEBUG is True and debug_t_dates is not None:
t_dates = debug_t_dates
for t_date in t_dates:
params['t_date'] = t_date
sql = SQL.get_expiration_dates_and_vols % params
data = execute_query_DF(conn, sql)
if data.numrows() < 4:
continue
print t_date
expiration_dates = np.array([int(i) for i in data('expiration_date')])
days = (expiration_dates - int(t_date)) / seconds_per_day
times_to_expiration = days / 365.
vol = data('vol')
pair = ('FAS','FAZ')
fit_parameter = 'vol'
start_date = datetime.date(2009, 1, 2)
end_date = datetime.date(2009, 11, 12)
symbol1 = pair[0]
symbol2 = pair[1]
params = dict()
params['symbol1'] = symbol1
params['symbol2'] = symbol2
params['fit_parameter'] = fit_parameter
params['start_date'] = int(datetime2epoch(start_date))
params['end_date'] = int(datetime2epoch(end_date))
sql = SQL.get_fit_coeff % params
print sql
fit_coeff = execute_query_DF(conn, sql)
coeff1 = fit_coeff('coeff1')
coeff2 = fit_coeff('coeff2')
price1 = fit_coeff('price_close1')
price2 = fit_coeff('price_close2')
t_dates = fit_coeff('t_date')
price_close_ini1 = fit_coeff('price_close_ini1')
price_close_ini2 = fit_coeff('price_close_ini2')
# Linear fit
linear_fit = scipy.polyfit(coeff1, coeff2, deg=1)
xmin = min(coeff1)
xmax = max(coeff1)
fit_x = np.arange(xmin, xmax, (xmax - xmin) / 50.)
fit_y = scipy.polyval(linear_fit, fit_x)
