def test_analytical_vega(self):
F = 100
K = 90
sigma = .2
r = .02
t = .5
flag = 'p'
c_put = c_analytical.vega(flag, F, K, t, r, sigma)
py_put = py_analytical.vega(flag, F, K, t, r, sigma)
self.assertTrue(almost_equal(c_put, py_put))
flag = 'c'
c_call = c_analytical.vega(flag, F, K, t, r, sigma)
py_call = py_analytical.vega(flag, F, K, t, r, sigma)
self.assertTrue(almost_equal(c_call, py_call))
def main_fun(now_date, now_time, percent):
vol_sr = pd.Series()
try:
vol_sr['percent'] = percent
part_result_dict = deal_fun(now_date=now_date, now_time=now_time, percent=percent)
F = part_result_dict['spot_px']
vol_sr['S'] = F
r = part_result_dict['rate_weight']
vol_sr['r'] = r
tran_dict = {1: 'first', 2: 'second'}
for i in range(1, 3):
t = part_result_dict['opt_ttm_1'] / 365
for ii in range(1, 3):
K = part_result_dict[f'opt_k_{i}_{ii}']
vol_sr[f'{tran_dict[i]}_K_{ii}'] = K
# 计算 vol
# if F >= K:
# flag = 'call'
# price = part_result_dict[f'{flag}_px_{i}_{ii}']
# vol_sr[f'{tran_dict[i]}_{flag}_px_{ii}'] = price
# sigma = implied_volatility(price, F, K, t, r, flag[0])
# vol_sr[f'{tran_dict[i]}_{flag}_vol_{ii}'] = sigma
# vol_sr[f'{tran_dict[i]}_vol_{ii}'] = vol_sr[f'{tran_dict[i]}_call_vol_{ii}']
# else:
# flag = 'put'
# price = part_result_dict[f'{flag}_px_{i}_{ii}']
# vol_sr[f'{tran_dict[i]}_{flag}_px_{ii}'] = price
# sigma = implied_volatility(price, F, K, t, r, flag[0])
# vol_sr[f'{tran_dict[i]}_{flag}_vol_{ii}'] = sigma
# vol_sr[f'{tran_dict[i]}_vol_{ii}'] = vol_sr[f'{tran_dict[i]}_put_vol_{ii}']
# 计算希腊字母
for flag in ['call', 'put']:
price = result_dict[now_date][f'{flag}_px_{i}_{ii}']
vol_sr[f'{tran_dict[i]}_{flag}_px_{ii}'] = price
sigma = implied_volatility(price, F, K, t, r, flag[0])
vol_sr[f'{tran_dict[i]}_{flag}_vol_{ii}'] = sigma
vol_sr[f'{tran_dict[i]}_{flag}_delta_{ii}'] = delta(flag[0], F, K, t, r, sigma)
vol_sr[f'{tran_dict[i]}_{flag}_gamma_{ii}'] = gamma(flag[0], F, K, t, r, sigma)
vol_sr[f'{tran_dict[i]}_{flag}_rho_{ii}'] = rho(flag[0], F, K, t, r, sigma)
vol_sr[f'{tran_dict[i]}_{flag}_theta_{ii}'] = theta(flag[0], F, K, t, r, sigma)
vol_sr[f'{tran_dict[i]}_{flag}_vega_{ii}'] = vega(flag[0], F, K, t, r, sigma)
vol_sr[f'{tran_dict[i]}_K_weight'] = (F - vol_sr[f'{tran_dict[i]}_K_2']) / \
(vol_sr[f'{tran_dict[i]}_K_1'] - vol_sr[f'{tran_dict[i]}_K_2'])
vol_sr[f'{tran_dict[i]}_vol'] = vol_sr[f'{tran_dict[i]}_K_weight'] * \
vol_sr[f'{tran_dict[i]}_vol_1'] + \
(1 - vol_sr[f'{tran_dict[i]}_K_weight']) * \
vol_sr[f'{tran_dict[i]}_vol_2']
x = (30 - part_result_dict['opt_ttm_2']) / \
(part_result_dict['opt_ttm_1'] - part_result_dict['opt_ttm_2'])
vol_sr[f'opt_ttm_1'] = part_result_dict['opt_ttm_1']
vol_sr[f'opt_ttm_2'] = part_result_dict['opt_ttm_2']
vol_sr[f'time_weight'] = x
vol_sr[f'target_vol'] = x * vol_sr[f'first_vol'] + (1 - x) * vol_sr[f'second_vol']
except Exception as error:
print(error)
vol_sr.name = pd.to_datetime(f'{now_date} {now_time}')
return vol_sr
def get_greeks(OPTION_DICT):
delta_dict = {}
vega_dict = {}
for security in OPTION_DICT.keys():
flag = OPTION_DICT[security]['opt_type']
F = OPTION_DICT[security]['und_price']
K = OPTION_DICT[security]['strike']
t = OPTION_DICT[security]['ttm']
r = 0
sigma = OPTION_DICT[security]['implied_vol']
d1 = delta(flag, F, K, t, r, sigma)
v1 = vega(flag, F, K, t, r, sigma)
delta_dict[security] = d1
vega_dict[security] = v1
'''
delta = re.compile(r'T(\d*)([CP])_delta$')
vega = re.compile(r'T(\d*)([CP])_vega$')
for security in SECURITIES:
delta_groups = delta.search(security)
vega_groups = vega.search(security)
if bool(delta_groups):
delta_dict[security] = SECURITIES[security]
if bool(vega_groups):
vega_dict[security] = SECURITIES[security]
'''
return delta_dict, vega_dict
def f_greeks(t):
iv = bk.implied_volatility.implied_volatility_of_discounted_option_price(
P0, F, K, r, t, flag='c')
gammaC = greek.gamma('c', F, K, t, r, iv)
# gammaP = greek.gamma('p', F, K, t, r, iv) # should be equal to gammaC
vegaC = greek.vega('c', F, K, t, r, iv)
# vegaP = greek.vega ('p', F, K, t, r, iv) # should be equal to vegaP C
thetaC = greek.theta('c', F, K, t, r, iv)
thetaP = greek.theta('p', F, K, t, r, iv)
deltaC = greek.delta('c', F, K, t, r, iv)
deltaP = greek.delta('p', F, K, t, r, iv)
return gammaC, vegaC, thetaC, thetaP, deltaC, deltaP
def get_greeks(OPTION_DICT):
delta_dict = {}
vega_dict = {}
for security in OPTION_DICT.keys():
flag = OPTION_DICT[security]['opt_type']
F = OPTION_DICT[security]['und_price']
K = OPTION_DICT[security]['strike']
t = OPTION_DICT[security]['ttm']
r = 0
sigma = OPTION_DICT[security]['implied_vol']
d1 = delta(flag, F, K, t, r, sigma)
v1 = vega(flag, F, K, t, r, sigma)
delta_dict[security] = d1
vega_dict[security] = v1
return delta_dict, vega_dict
def calcVol(mercFilter='DOL', opcMercFilter=None):
if opcMercFilter is None:
opcMercFilter = mercFilter
opclst = mds.find({'$regex': '^{}'.format(opcMercFilter)},
ds.freqHelper.bday,
mds.b3VS,
False,
type='option')
futlst = mds.find({'$regex': '^{}'.format(mercFilter)},
ds.freqHelper.bday,
mds.b3VS,
False,
type='future')
opclst = [o[1].replace('_1BDay', '') for o in opclst]
futlst = [o[1].replace('_1BDay', '') for o in futlst]
df_fut = mds.read(futlst, library=mds.b3VS)
df_fut = {f.md.name: f for f in df_fut}
#df_opc=
for i, nm in enumerate(opclst):
df = mds.read(nm, ds.freqHelper.bday, mds.b3VS)
df0 = df.copy()
underlying1 = mercFilter + uu.futCodeFromDate(df.md.maturity)
#df.md.underlying
try:
df_fut1 = df_fut[underlying1 + '_b3']
except:
try:
df_fut1 = df_fut[underlying1.replace('0', '') + '_b3']
except:
warn('future not found {}'.format(underlying1))
continue
df_fut1 = df_fut1.reindex(df.index)
du = uu.networkdays(df.index, df.md.maturity)
dc = (df.md.maturity - df.index).days / 365
rf = uu.rCont(uu.getRates(df.index, du))
vol = empty_like(df.close)
vol1 = empty_like(df.close)
df['vol'] = nan
df['delta'] = nan
df['gamma'] = nan
df['theta'] = nan
df['vega'] = nan
px = df.close * exp(rf * ((du + 1) / 252))
for t, px1 in enumerate(px):
if du[t] > 0:
# vol[t] = implied_volatility(px,df_fut1.close[t],df.md.strike,rf[t],du[t]/252,df.md.cp)
try:
df['vol'].values[
t] = implied_volatility_of_undiscounted_option_price(
px1, df_fut1.close[t], df.md.strike, dc[t],
df.md.cp)
df.gamma.values[t] = analytical.gamma(
df.md.cp, df_fut1.close[t], df.md.strike, dc[t], rf[t],
df['vol'][t])
df.theta.values[t] = analytical.theta(
df.md.cp, df_fut1.close[t], df.md.strike, dc[t], rf[t],
df['vol'][t])
df.vega.values[t] = analytical.vega(
df.md.cp, df_fut1.close[t], df.md.strike, dc[t], rf[t],
df['vol'][t])
df.delta.values[t] = analytical.delta(
df.md.cp, df_fut1.close[t], df.md.strike, dc[t], rf[t],
df['vol'][t])
except:
warn('invalid vol (prob below intrinsic)')
else:
df['vol'].values[t] = 0
print('{} i: {}/{}'.format(nm, i, len(opclst)))
if not df0.equals(df):
mds.write(df,
library=mds.b3VS,
check_metadata=False,
prune_previous_version=True)