def iv_at_the_money(dt_date, dt_last, name_code):
dict_iv_call = {}
dict_iv_put = {}
df_metrics = get_data.get_comoption_mktdata(dt_last, dt_date, name_code)
optionset = BaseOptionSet(df_metrics, rf=0.0)
optionset.init()
dt_maturity = optionset.select_maturity_date(0, 0)
call_list, put_list = optionset.get_options_list_by_moneyness_mthd1(
0, dt_maturity)
atm_call = optionset.select_higher_volume(call_list)
atm_put = optionset.select_higher_volume(put_list)
iv_call = atm_call.get_implied_vol() * 100
iv_put = atm_put.get_implied_vol() * 100
dict_iv_call.update({dt_last: iv_call})
dict_iv_put.update({dt_last: iv_put})
optionset.go_to(dt_date)
dt_maturity = optionset.select_maturity_date(0, 0)
call_list, put_list = optionset.get_options_list_by_moneyness_mthd1(
0, dt_maturity)
atm_call = optionset.select_higher_volume(call_list)
atm_put = optionset.select_higher_volume(put_list)
iv_call = atm_call.get_implied_vol() * 100
iv_put = atm_put.get_implied_vol() * 100
dict_iv_call.update({dt_date: iv_call})
dict_iv_put.update({dt_date: iv_put})
return dict_iv_call, dict_iv_put
def iv_volume_weighted(dt_date, dt_last, name_code):
dict_iv = {}
df_metrics = get_data.get_comoption_mktdata(dt_last, dt_date, name_code)
optionset = BaseOptionSet(df_metrics)
optionset.init()
dt_maturity = optionset.select_maturity_date(0, 0)
iv_volume_weighted = optionset.get_volume_weighted_iv(dt_maturity) * 100
dict_iv.update({dt_last: iv_volume_weighted})
optionset.go_to(dt_date)
dt_maturity = optionset.select_maturity_date(0, 0)
iv_volume_weighted = optionset.get_volume_weighted_iv(dt_maturity) * 100
dict_iv.update({dt_date: iv_volume_weighted})
return dict_iv
def iv_htbr(dt_date, dt_last, name_code):
dict_iv = {}
df_metrics = get_data.get_comoption_mktdata(dt_last, dt_date, name_code)
optionset = BaseOptionSet(df_metrics, rf=0)
optionset.init()
dt_maturity = optionset.select_maturity_date(0, 0)
iv_curve_htbr = optionset.get_implied_vol_curves_htbr(dt_maturity)
call_list, put_list = optionset.get_options_list_by_moneyness_mthd1(
0, dt_maturity)
atm_k = call_list[0].applicable_strike()
iv = iv_curve_htbr[iv_curve_htbr[c.Util.AMT_APPLICABLE_STRIKE] == atm_k][
c.Util.PCT_IV_PUT_BY_HTBR].values[0] * 100
dict_iv.update({dt_last: iv})
optionset.go_to(dt_date)
dt_maturity = optionset.select_maturity_date(0, 0)
iv_curve_htbr = optionset.get_implied_vol_curves_htbr(dt_maturity)
call_list, put_list = optionset.get_options_list_by_moneyness_mthd1(
0, dt_maturity)
atm_k = call_list[0].applicable_strike()
iv = iv_curve_htbr[iv_curve_htbr[c.Util.AMT_APPLICABLE_STRIKE] == atm_k][
c.Util.PCT_IV_PUT_BY_HTBR].values[0] * 100
dict_iv.update({dt_date: iv})
return dict_iv
def get_atm_iv_average(optionset, maturity):
atm_call, atm_put = get_atm_options(optionset, maturity)
iv_call = atm_call.get_implied_vol()
iv_put = atm_put.get_implied_vol()
iv_avg = (iv_call + iv_put) / 2
return iv_avg
pu = PlotUtil()
start_date = datetime.date(2018, 9, 12)
end_date = datetime.date(2018, 9, 13)
nbr_maturity = 0
min_holding = 8
df_daily = get_data.get_comoption_mktdata(start_date, end_date, Util.STR_SR)
optionset = BaseOptionSet(df_data=df_daily, rf=0.015)
optionset.init()
# optionset.go_to(end_date)
maturity = optionset.select_maturity_date(nbr_maturity,
min_holding=min_holding)
iv_htr = optionset.get_atm_iv_by_htbr(maturity)
iv_avg = get_atm_iv_average(optionset, maturity)
htbr = optionset.get_htb_rate(maturity)
print('iv_htr : ', iv_htr)
print('iv_avg : ', iv_avg)
print('htb rate : ', htbr)
# curve = optionset.get_implied_vol_curves(maturity)
end_date = datetime.date(2018, 9, 28)
last_week = datetime.date(2018, 9, 21)
start_date = last_week
# start_date = datetime.date(2017, 4, 1)
dt_histvol = start_date - datetime.timedelta(days=200)
min_holding = 5
""""""
df_res = pd.DataFrame()
pu = PlotUtil()
""" 白糖 """
name_code = c.Util.STR_SR
core_id = 'sr_1901'
df_metrics = get_data.get_comoption_mktdata(start_date, end_date, name_code)
df_future_c1_daily = get_data.get_future_c1_by_option_daily(dt_histvol, end_date, name_code, min_holding)
d1 = max(df_metrics[c.Util.DT_DATE].values[0], df_future_c1_daily[c.Util.DT_DATE].values[0])
df_metrics = df_metrics[(df_metrics[c.Util.DT_DATE] >= d1) & (df_metrics[c.Util.DT_DATE] <= end_date)].reset_index(
drop=True)
""" 隐含波动率期限结构 """
dt_1 = last_week - datetime.timedelta(days=7)
dt_2 = last_week - datetime.timedelta(days=14)
""" PCR """
df_res = pcr(d1, end_date, name_code, df_res)
print('2.PCR Finished')
""" 隐含波动率 """
df_res = implied_vol(df_metrics, df_res, [dt_2, dt_1, last_week, end_date],name_code)
df_res = df_res.reset_index(drop=True)
print('4.隐含波动率 Finished')
from data_access.get_data import get_50option_mktdata, get_comoption_mktdata
start_date = datetime.date(2018,8,27)
end_date = datetime.date(2018,8,31)
calendar = ql.China()
daycounter = ql.ActualActual()
util = BktUtil()
optionMetrics = admin.table_option_metrics()
conn = admin.conn_metrics()
name_option = 'sr'
print(name_option)
df_option_metrics = get_comoption_mktdata(start_date,end_date,name_option)
bkt_optionset = BktOptionSet(df_option_metrics)
while bkt_optionset.index <= len(bkt_optionset.dt_list):
evalDate = bkt_optionset.eval_date
option_metrics = bkt_optionset.collect_option_metrics()
try:
for r in option_metrics:
res = optionMetrics.select((optionMetrics.c.id_instrument == r['id_instrument'])
&(optionMetrics.c.dt_date == r['dt_date'])).execute()
if res.rowcount > 0:
optionMetrics.delete((optionMetrics.c.id_instrument == r['id_instrument'])
&(optionMetrics.c.dt_date == r['dt_date'])).execute()
conn.execute(optionMetrics.insert(), r)
print(evalDate, ' : option metrics -- inserted into data base succefully')
except Exception as e:
print(e)
print(evalDate, 'No complete collar portfolio constructed, try next day !')
"""按当日价格调整保证金，计算投资组合盯市价值"""
bkt_account.mkm_update_portfolio(evalDate,self.portfolio)
print(evalDate, ' , ', bkt_account.npv, bkt_account.mtm_long_positions, bkt_account.mtm_short_positions)
"""Back Test Settings"""
start_date = datetime.date(2017, 6, 1)
# start_date = datetime.date(2018, 1, 20)
end_date = datetime.date(2018, 5, 21)
"""Collect Mkt Date"""
vol_data = pd.read_excel('../data/AE1_HISTVOL.xlsx').rename(columns={'IV':'amt_close','Date':'dt_date'}).sort_values(by='dt_date',ascending=True)
index_data = pd.read_excel('../data/AE1_HISTVOL.xlsx').rename(columns={'PRICE':'amt_close','Date':'dt_date'}).sort_values(by='dt_date',ascending=True).dropna()
df_option_metrics = get_comoption_mktdata(start_date, end_date, 'm')
"""Run Backtest"""
bkt_strategy = BktStrategyStraddle(df_option_metrics)
bkt_strategy.set_min_holding_days(8)
df_vol_boll = bkt_strategy.get_bollinger_signal_calculate(vol_data,start_date,cd_long=60)
df_index_boll = bkt_strategy.get_bollinger_signal_calculate(index_data,start_date,cd_long=60)
# df_vol_pctl = bkt_strategy.get_percentile_signal(vol_data,start_date,cd_long=60)
bkt_strategy.set_volatility_boll(df_vol_boll)
bkt_strategy.set_index_boll(df_index_boll)
# bkt_strategy.set_volatility_boll(df_vol_pctl)
df_vol_boll.to_csv('../df_m_vol_boll.csv')
# df_vol_pctl.to_csv('../df_m_vol_pctl.csv')