price = barrier_option.NPV()
prices.append(price)
# vol2 = black_var_surface.blackVol(daycounter.yearFraction(evalDate, maturitydt),spot+0.1)
# process2 = evaluation.get_bsmprocess_cnstvol(daycounter,calendar,underlying,vol2)
# barrier_option.setPricingEngine(ql.BinomialBarrierEngine(process2, 'crr', 800))
# price2 = barrier_option.NPV()
# vega = (price2-price)/(vol2-vol)
# dsigma_ds = (vol2-vol)/0.1
# print(vol,vol2,vega,dsigma_ds)
# vegas.append(vega)
# tdeltas.append(delta+vega*dsigma_ds)
# delta_maturities.update({maturitydt:deltas})
t = daycounter.yearFraction(evalDate, maturitydt)
# pu.plot_line(axx,cont,list(spot_range),deltas,lgds[cont],'spot','Delta')
# pu.plot_line(axx1,cont,list(spot_range),gammas,lgds[cont],'spot','Gamma')
pu.plot_line(axx2, cont, list(spot_range), prices, lgds[cont], 'spot',
'Price')
# pu.plot_line(axx3,cont,list(spot_range),tdeltas,lgds[cont],'spot','Total Delta')
# axx = pu.set_frame([axx])[0]
# pu.plot_line(axx,cont+1,[barrier*1.01]*len(spot_range),np.arange(-2,10,12/len(spot_range)),'','spot','Delta')
# maxd = 10
# mind = -2
# maxd = 8.5
# mind = 0
# rate = -0.01
# x = np.arange(barrier*(1+rate),barrier,-barrier*rate/100)
# axx.plot([barrier*(1+rate)]*len(spot_range),np.arange(mind,maxd,(maxd-mind)/len(spot_range)), color=pu.c6, linestyle='--', linewidth=1)
# axx.plot([barrier+0.001]*len(spot_range),np.arange(mind,maxd,(maxd-mind)/len(spot_range)), color=pu.c6, linestyle='--', linewidth=1)
# axx.plot(x,[mind]*len(x), color=pu.c6, linestyle='--', linewidth=1)
# axx.plot(x,[maxd]*len(x), color=pu.c6, linestyle='--', linewidth=1)
plt.show()
float(index300sh_df_c['histvol_60']),
float(index300sh_df_c['histvol_20']),
float(index300sh_df_c['histvol_10']),
float(index300sh_df_c['histvol_5'])
]
print(indexid, ' current_vols : ', current_vols)
histvolcone = [
current_vols, max_vols, min_vols, median_vols, p75_vols, p25_vols
]
x = [120, 60, 20, 10, 5]
x_labels = ['1W', '2W', '1M', '3M', '6M']
f2, ax2 = plt.subplots()
ldgs = ['当前水平', '最大值', '最小值', '中位数', '75分位数', '25分位数']
for cont2, y in enumerate(histvolcone):
pu.plot_line(ax2, cont2, x, y, ldgs[cont2], '时间窗口', '波动率（%）')
ax2.legend(bbox_to_anchor=(0., 1.02, 1., .202),
loc=3,
ncol=4,
mode="expand",
borderaxespad=0.)
ax2.set_xticks([5, 10, 20, 60, 120])
ax2.set_xticklabels(x_labels)
f2.set_size_inches((8, 6))
f2.savefig('../save_figure/otc_histvols_' + indexid + '_' +
str(hist_date) + ' - ' + str(evalDate) + '.png',
dpi=300,
format='png')
plt.show()
def implied_vol_analysis(evalDate, w, nameCode, exchangeCode):
pu = PlotUtil()
engine1 = create_engine(
'mysql+pymysql://readonly:[email protected]/mktdata',
echo=False)
Session1 = sessionmaker(bind=engine1)
sess1 = Session1()
engine2 = create_engine(
'mysql+pymysql://readonly:[email protected]/metrics',
echo=False)
Session2 = sessionmaker(bind=engine2)
sess2 = Session2()
optionMetrics = dbt.OptionMetrics
dt_1w = w.tdaysoffset(-1, evalDate,
"Period=W").Data[0][0].strftime("%Y-%m-%d")
dt_2w = w.tdaysoffset(-2, evalDate,
"Period=W").Data[0][0].strftime("%Y-%m-%d")
dt_3w = w.tdaysoffset(-3, evalDate,
"Period=W").Data[0][0].strftime("%Y-%m-%d")
dt_4w = w.tdaysoffset(-4, evalDate,
"Period=W").Data[0][0].strftime("%Y-%m-%d")
dt_5w = w.tdaysoffset(-5, evalDate,
"Period=W").Data[0][0].strftime("%Y-%m-%d")
plt.rcParams['font.sans-serif'] = ['STKaiti']
plt.rcParams.update({'font.size': 15})
"""波动率期限结构"""
dates = [evalDate, dt_1w, dt_2w, dt_3w, dt_4w, dt_5w]
query_f = sess1.query(futureMkt.dt_date, futureMkt.id_instrument.label('id_underlying'),
futureMkt.amt_settlement,futureMkt.flag_night,futureMkt.name_code) \
.filter(or_(futureMkt.dt_date==evalDate,futureMkt.dt_date==dt_1w,futureMkt.dt_date==dt_2w,
futureMkt.dt_date==dt_3w,futureMkt.dt_date==dt_4w,futureMkt.dt_date==dt_5w))\
.filter(futureMkt.name_code == nameCode)\
.filter(futureMkt.flag_night != 1)
query_metrics = sess2.query(optionMetrics.dt_date,optionMetrics.id_instrument,optionMetrics.cd_option_type,
optionMetrics.pct_implied_vol,optionMetrics.amt_option_price) \
.filter(or_(optionMetrics.dt_date == evalDate, optionMetrics.dt_date == dt_1w, optionMetrics.dt_date == dt_2w,
optionMetrics.dt_date == dt_3w, optionMetrics.dt_date == dt_4w, optionMetrics.dt_date == dt_5w)) \
.filter(optionMetrics.name_code == nameCode)
df_future = pd.read_sql(query_f.statement, query_f.session.bind)
df_metrics = pd.read_sql(query_metrics.statement,
query_metrics.session.bind)
for (idx, row) in df_metrics.iterrows():
id_option = row['id_instrument']
if id_option[0] == nameCode or id_option[0:2] == nameCode:
dt_date = row['dt_date']
option_price = row['amt_option_price']
strike = float(id_option[-4:])
if nameCode == 'm':
id_underlying = id_option[:6]
elif nameCode == 'sr':
id_underlying = id_option[:7]
else:
id_underlying = None
contract_month = id_underlying[-4:]
if int(contract_month[-2:]) in [1, 5, 9]:
df_metrics.loc[idx, 'flag'] = 1
underlying_price = df_future[
(df_future['dt_date'] == dt_date)
& (df_future['id_underlying'] == id_underlying
)]['amt_settlement'].values[0]
df_metrics.loc[idx, 'dt_date'] = dt_date.strftime("%Y-%m-%d")
df_metrics.loc[idx, 'id_underlying'] = id_underlying
df_metrics.loc[idx, 'underlying_price'] = underlying_price
df_metrics.loc[idx, 'contract_month'] = id_underlying[-4:]
df_metrics.loc[idx, 'diff'] = abs(strike - underlying_price)
else:
df_metrics.loc[idx, 'flag'] = 0
else:
df_metrics.loc[idx, 'flag'] = 0
df_metrics = df_metrics[df_metrics['flag'] == 1].reset_index()
idx = df_metrics.groupby(['dt_date', 'id_underlying', 'cd_option_type'
])['diff'].transform(min) == df_metrics['diff']
df_iv = df_metrics[idx]
df_call_iv = df_iv[df_iv['cd_option_type'] == 'call'].sort_values(
by=['dt_date',
'id_underlying'], ascending=False).reset_index() # 选取认购平值合约
df_put_iv = df_iv[df_iv['cd_option_type'] == 'put'].sort_values(
by=['dt_date',
'id_underlying'], ascending=False).reset_index() # 选取认沽平值合约
df_call_iv = df_call_iv.drop_duplicates(['dt_date', 'id_underlying'])
df_put_iv = df_put_iv.drop_duplicates(['dt_date', 'id_underlying'])
optiondata_atm_df = df_put_iv[['dt_date', 'contract_month']]
optiondata_atm_df.loc[:, 'implied_vol'] = 100 * (
df_call_iv.loc[:, 'pct_implied_vol'] +
df_put_iv.loc[:, 'pct_implied_vol']) / 2.0
f1, ax1 = plt.subplots()
cont = 0
contracts = []
for d in dates:
df = optiondata_atm_df[optiondata_atm_df['dt_date'] == d]
df = df.sort_values(by=['contract_month'], ascending=True)
pu.plot_line(ax1, cont, range(len(df)), df['implied_vol'], d, '合约月份',
'波动率(%)')
if len(contracts) == 0: contracts = df['contract_month'].tolist()
cont += 1
ax1.legend(bbox_to_anchor=(0., 1.02, 1., .202),
loc=3,
ncol=6,
mode="expand",
borderaxespad=0.,
frameon=False)
ax1.set_xticks(range(len(contracts)))
ax1.set_xticklabels(contracts)
f1.set_size_inches((12, 6))
optiondata_atm_df.to_csv('../data/' + nameCode +
'_implied_vol_term_structure.csv')
f1.savefig('../data/' + nameCode + '_iv_term_structure_' + str(evalDate) +
'.png',
dpi=300,
format='png')
a = pd.merge(rb_data, i_data, on='dt_date')
earningsdata = pd.merge(a, j_data, on='dt_date')
earningsdata[
'amt_earning'] = earningsdata['amt_close_x'] - 1.65 * earningsdata[
'amt_close_y'] - 0.5 * earningsdata['amt_close']
print(earningsdata)
pu = PlotUtil()
f, ax = plt.subplots()
count = 0
pu.plot_line(ax,
0,
earningsdata['dt_date'],
earningsdata['amt_earning'],
lgd='钢厂利润指数')
pu.plot_line(ax,
1,
earningsdata['dt_date'],
earningsdata['amt_close_x'],
lgd='螺纹钢')
pu.plot_line(ax,
2,
earningsdata['dt_date'],
earningsdata['amt_close_y'],
lgd='铁矿石')
pu.plot_line(ax,
3,
earningsdata['dt_date'],
for cm1 in contract_months:
c = optiondata_df['contract_month'].map(lambda x: x == cm1)
c1 = optiondata_df['date'].map(lambda x: x == date)
critiron = c & c1
df = optiondata_df[critiron]
idx = df['atm_dif'].idxmin()
optiondata_atm_df = optiondata_atm_df.append(df.loc[idx],
ignore_index=True)
print('atm_implied_vols')
print(optiondata_atm_df)
f1, ax1 = plt.subplots()
cont = 0
for d in dates:
c2 = optiondata_atm_df['date'].map(lambda a: a == d)
df = optiondata_atm_df[c2]
pu.plot_line(ax1, cont, df['contract_month'], df['implied_vol'], d, '合约月份',
'波动率(%)')
cont += 1
ax1.legend(bbox_to_anchor=(0., 1.02, 1., .202),
loc=3,
ncol=3,
mode="expand",
borderaxespad=0.,
frameon=False)
f1.savefig('../save_figure/implied_vols_' + str(evalDate) + '.png',
dpi=300,
format='png')
#################### Futures and Realised Vol
# Get core contract mktdata
data = w.wsd("SR.CZC", "trade_hiscode", hist_date, evalDate, "")
underlying_df = pd.DataFrame({'date': data.Times, 'code_core': data.Data[0]})
mergedvix_df = rv_df.join(cvix_df.set_index('dt_date'), on='dt_date')
print(merged_df)
dates = merged_df['dt_date'].tolist()
vol_set = [merged_df['intraday_vol'].tolist(),
merged_df['histvol_120'].tolist(),
merged_df['histvol_60'].tolist(),
merged_df['histvol_20'].tolist(),
mergedvix_df['amt_close'].tolist()
]
print(indexid,' histvol_60 : ',merged_df['histvol_60'].tolist()[-1])
f2, ax2 = plt.subplots()
ldgs = ['已实现波动率RV', '历史波动率6M', '历史波动率3M', '历史波动率1M', '中国波指IVIX']
for cont2, y in enumerate(vol_set):
pu.plot_line(ax2, cont2, dates, y, ldgs[cont2], '日期', '波动率（%）')
ax2.legend(bbox_to_anchor=(0., 1.02, 1., .202), loc=3,
ncol=5, mode="expand", borderaxespad=0.)
f2.set_size_inches((12, 5))
f2.savefig('../save_figure/otc_realizedvol_' + indexid + '_' + str(startDate) + ' - ' + str(evalDate) + '.png',
dpi=300, format='png')
histvols_3M.append(merged_df['histvol_60'].tolist())
realizedvols.append(merged_df['intraday_vol'].tolist())
merged_df.to_csv('../save_figure/index_vols'+indexid+'.csv')
f3, ax3 = plt.subplots()
ldgs = ['沪深300指数历史波动率3M','上证50指数历史波动率3M','中证500指数历史波动率3M']
for cont2, y in enumerate(histvols_3M):
pu.plot_line(ax3, cont2, dates, y, ldgs[cont2], '日期', '波动率（%）')
ax3.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,