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')
from alpha import alpha_theo_vol
lb = [.3, .2, 1]
ub = [5, 3, 10]
coeffs = nerdy.wls_fit(alpha_theo_vol,[1,.7,5], times_to_expiration, vol, lb=lb,ub=ub)
alpha_vols = alpha_theo_vol(coeffs, alpha_days/365.)
if GRAPH is True:
sleep(.1)
pyplot.title(epoch2datetime(int(t_date)).date())
term_vols.set_xdata(days)
term_vols.set_ydata(vol)
alpha_curve.set_xdata(alpha_days)
alpha_curve.set_ydata(alpha_vols)
pyplot.draw()
if DEBUG is False:
columns = ('symbol', 't_date',
'pivot_vol', 'mean_vol', 'alpha', 'expirations')
results = (symbol, int(t_date),
fit = np.zeros(z.shape)
for c in range(0,degree+1):
fit = fit + x[c] * (z **c)
error_unweighted = np.sum((delta_wt_avg_implied_vols-fit) ** 2)
error = np.sum(weights*(delta_wt_avg_implied_vols -fit)**4)
return error
import openopt
nlp = openopt.NLP(penalty, np.zeros(4))
r=nlp.solve('ralg')
coeffs2=r.xf
coeffs2=list(coeffs2)
print strikes
def three(c, x):
return c[3] * x**3 + c[2] * x**2 + c[1] * x + c[0]
weights3 = np.exp(-abs(z ** 2))
coeffs3 = nerdy.wls_fit(three, [0]*4, z, delta_wt_avg_implied_vols, weights=weights3)
coeffs3 = list(coeffs3)
coeffs2.reverse(); coeffs3.reverse()
for coeff_list in [coeffs, coeffs2, coeffs3]:
while not len(coeff_list) >= 5:
coeff_list.insert(0,0)
except Exception, e:
fail += 1
print "Exception:", e
ipshell("WTF")
raise Exception, e
else:
if DEBUG is True:
print
print "z:"
print z