def hist_vol(dt_start, df_future_c1_daily, df_res, name_code):
if name_code == c.Util.STR_CU:
m = 100
else:
m = 1
df_future_c1_daily.loc[:, 'histvol_10'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=10) * m
df_future_c1_daily.loc[:, 'histvol_20'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=20) * m
df_future_c1_daily.loc[:, 'histvol_30'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=30) * m
df_future_c1_daily.loc[:, 'histvol_60'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=20 * 3) * m
df_future_c1_daily.loc[:, 'histvol_120'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=20 * 6) * m
df_tmp = df_future_c1_daily[
df_future_c1_daily[c.Util.DT_DATE] >= dt_start].dropna()
df_tmp = df_tmp.sort_values(by=c.Util.DT_DATE,
ascending=False).reset_index(drop=True)
df_res.loc[:, 'U:date'] = df_tmp[c.Util.DT_DATE]
df_res.loc[:, 'V:histvol_10'] = df_tmp.loc[:, 'histvol_10']
df_res.loc[:, 'W:histvol_20'] = df_tmp.loc[:, 'histvol_20']
df_res.loc[:, 'X:histvol_30'] = df_tmp.loc[:, 'histvol_30']
df_res.loc[:, 'Y:histvol_60'] = df_tmp.loc[:, 'histvol_60']
df_res.loc[:, 'Z:histvol_120'] = df_tmp.loc[:, 'histvol_120']
return df_res
def hist_vol1(df_future_c1_daily):
m = 100
df_future_c1_daily.loc[:, 'histvol_10'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=10) * m
df_future_c1_daily.loc[:, 'histvol_20'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=20) * m
df_future_c1_daily.loc[:, 'histvol_30'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=30) * m
df_future_c1_daily.loc[:, 'histvol_60'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=60) * m
df_future_c1_daily.loc[:, 'histvol_90'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=90) * m
return df_future_c1_daily
def init(self):
df_future_histvol = self.df_f_c1.copy()
df_future_histvol['amt_hv'] = histvol.hist_vol(
df_future_histvol[c.Util.AMT_CLOSE], n=self.n_hv)
self.dt_start = max(self.df_f_c1[c.Util.DT_DATE].values[0],
self.df_metrics[c.Util.DT_DATE].values[0])
self.end_date = min(self.df_f_c1[c.Util.DT_DATE].values[-1],
self.df_metrics[c.Util.DT_DATE].values[-1])
self.df_metrics = self.df_metrics[
self.df_metrics[c.Util.DT_DATE] >= self.dt_start].reset_index(
drop=True)
self.df_f_c1 = self.df_f_c1[
self.df_f_c1[c.Util.DT_DATE] >= self.dt_start].reset_index(
drop=True)
self.df_f_all = self.df_f_all[
self.df_f_all[c.Util.DT_DATE] >= self.dt_start].reset_index(
drop=True)
self.df_vol = pd.merge(self.df_vol,
df_future_histvol[[c.Util.DT_DATE, 'amt_hv']],
on=c.Util.DT_DATE).set_index(c.Util.DT_DATE)
self.optionset = BaseOptionSet(self.df_metrics)
self.optionset.init()
self.hedging = SytheticOption(self.df_f_c1,
rf=self.rf,
df_futures_all_daily=self.df_f_all)
self.hedging.init()
self.hedging.amt_option = 1 / 1000 # 50ETF与IH点数之比
self.account = BaseAccount(init_fund=c.Util.BILLION, rf=self.rf)
self.prepare_timing()
def __init__(self, start_date, end_date):
self.ttm = 30
self.buywrite = BuyWrite.BUY
self.fund = Util.BILLION
self.invest_underlying_ratio = 0.7
# self.invest_underlying_ratio = 1
self.slippage = 2
self.start_date = start_date
self.end_date = end_date
hist_date = start_date - datetime.timedelta(days=40)
df_future_c1 = get_dzqh_cf_c1_minute(start_date, end_date, 'if')
df_future_c1_daily = get_dzqh_cf_c1_daily(hist_date, end_date, 'if')
df_futures_all_daily = get_dzqh_cf_daily(
start_date, end_date, 'if') # daily data of all future contracts
df_index = get_index_mktdata(
start_date, end_date,
'index_300sh') # daily data of underlying index
df_index = df_index[df_index[Util.DT_DATE].isin(
Util.DZQH_CF_DATA_MISSING_DATES) == False].reset_index(drop=True)
# df_index.to_csv('df_index.csv')
self.trade_dates = sorted(df_future_c1_daily[Util.DT_DATE].unique())
self.df_vol_1m = Histvol.hist_vol(df_future_c1_daily)
# df_parkinson_1m = Histvol.parkinson_number(df_future_c1_daily)
self.df_garman_klass = Histvol.garman_klass(df_future_c1_daily)
# df_hist_vol = self.df_vol_1m.join(self.df_garman_klass, how='left')
# df_hist_vol.to_csv('../../data/df_hist_vol.csv')
self.underlying = BaseInstrument(df_data=df_index)
self.underlying.init()
self.synthetic_option = SytheticOption(
df_c1_data=df_future_c1,
# df_c1_daily=df_future_c1_daily,
df_futures_all_daily=df_futures_all_daily,
df_index_daily=df_index)
self.synthetic_option.init()
self.account = BaseAccount(self.fund, leverage=20.0, rf=0.0)
self.trading_desk = Trade()
self.init_spot = self.synthetic_option.underlying_state_daily[
Util.AMT_CLOSE]
self.df_analysis = pd.DataFrame()
def hist_vol(dt_start, df_future_c1_daily, df_res, name_code):
m = 100
df_future_c1_daily.loc[:, 'histvol_10'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=10) * m
df_future_c1_daily.loc[:, 'histvol_20'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=20) * m
df_future_c1_daily.loc[:, 'histvol_30'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=30) * m
df_future_c1_daily.loc[:, 'histvol_60'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=60) * m
df_future_c1_daily.loc[:, 'histvol_90'] = Histvol.hist_vol(
df_future_c1_daily[c.Util.AMT_CLOSE], n=90) * m
# df_tmp = df_future_c1_daily[df_future_c1_daily[c.Util.DT_DATE] >= dt_start].dropna()
df_tmp = df_future_c1_daily.sort_values(
by=c.Util.DT_DATE, ascending=False).reset_index(drop=True)
df_res.loc[:, name_code + ':U:date'] = df_tmp[c.Util.DT_DATE]
df_res.loc[:, name_code + ':V:histvol_10'] = df_tmp.loc[:, 'histvol_10']
df_res.loc[:, name_code + ':W:histvol_20'] = df_tmp.loc[:, 'histvol_20']
df_res.loc[:, name_code + ':X:histvol_30'] = df_tmp.loc[:, 'histvol_30']
df_res.loc[:, name_code + ':Y:histvol_60'] = df_tmp.loc[:, 'histvol_60']
df_res.loc[:, name_code + ':Z:histvol_90'] = df_tmp.loc[:, 'histvol_90']
return df_res
def LLKSR_analysis(dt_start, series_iv, df_future_c1_daily, name_code):
iv_atm = series_iv
df_estimated_iv = pd.DataFrame()
df_estimated_iv['LLKSR_iv_5'] = LLKSR(iv_atm, 5)
df_estimated_iv['LLKSR_iv_10'] = LLKSR(iv_atm, 10)
df_estimated_iv['LLKSR_iv_20'] = LLKSR(iv_atm, 20)
df_future_c1_daily.loc[:, 'histvol_20'] = Histvol.hist_vol(df_future_c1_daily[c.Util.AMT_CLOSE], n=20) * 100
df_histvol = df_future_c1_daily[df_future_c1_daily[c.Util.DT_DATE] >= dt_start].dropna().reset_index(drop=True)
dates = list(df_histvol[c.Util.DT_DATE])
f1 = pu.plot_line_chart(dates, [list(df_estimated_iv['LLKSR_iv_5']), list(df_estimated_iv['LLKSR_iv_10']),
list(df_estimated_iv['LLKSR_iv_20'])],
['隐含波动率LLKSR趋势线 (h=5)', '隐含波动率LLKSR趋势线 (h=10)', '隐含波动率LLKSR趋势线 (h=20)'])
f2 = pu.plot_line_chart(dates, [list(df_estimated_iv['LLKSR_iv_10']), list(df_histvol['histvol_20'])],
['隐含波动率LLKSR趋势线 (h=10)', '历史波动率LLKSR趋势线 (h=10)'])
f3 = pu.plot_line_chart(dates, [list(df_estimated_iv['LLKSR_iv_10']), list(iv_atm)],
['隐含波动率LLKSR趋势线 (h=10)', 'iv_atm'])
f1.savefig('../data/' + name_code + '_iv_LLKSRs.png', dpi=300, format='png', bbox_inches='tight')
f2.savefig('../data/' + name_code + '_iv_hv_LLKSR.png', dpi=300, format='png', bbox_inches='tight')
f3.savefig('../data/' + name_code + '_iv_LLKSR.png', dpi=300, format='png', bbox_inches='tight')
from Utilities.PlotUtil import PlotUtil pu = PlotUtil() start_date = datetime.date(2010, 1, 1) end_date = datetime.date(2018, 8, 8) dt_histvol = start_date """ 50ETF option """ name_code = c.Util.STR_IH name_code_option = c.Util.STR_50ETF # df_metrics = get_data.get_50option_mktdata(start_date, end_date) df_future_c1_daily = get_data.get_dzqh_cf_c1_daily(dt_histvol, end_date, name_code) # df_future_c1_daily = get_data.get_index_mktdata(start_date,end_date,'index_50sh') """ 历史波动率 """ df_cc_1m = Histvol.hist_vol(df_future_c1_daily) # df_p_1m = Histvol.parkinson_number(df_future_c1_daily) # df_gk_1m = Histvol.garman_klass(df_future_c1_daily) df_data = df_future_c1_daily.join(df_cc_1m,on=c.Util.DT_DATE,how='left') # df_data = df_data.join(df_p_1m,on=c.Util.DT_DATE,how='left') # df_data = df_data.join(df_gk_1m,on=c.Util.DT_DATE,how='left') df_cc_1w = Histvol.hist_vol(df_future_c1_daily,n=5) # df_p_1w = Histvol.parkinson_number(df_future_c1_daily,n=5) # df_gk_1w = Histvol.garman_klass(df_future_c1_daily,n=5) df_data = df_data.join(df_cc_1w,on=c.Util.DT_DATE,how='left') # df_data = df_data.join(df_p_1w,on=c.Util.DT_DATE,how='left') # df_data = df_data.join(df_gk_1w,on=c.Util.DT_DATE,how='left') df_cc_3m = Histvol.hist_vol(df_future_c1_daily,n=60) # df_p_3m = Histvol.parkinson_number(df_future_c1_daily,n=60)
from Utilities import timebase
import numpy as np
pu = PlotUtil()
start_date = datetime.date(2010, 1, 1)
end_date = datetime.date.today()
dt_histvol = start_date - datetime.timedelta(days=40)
# min_holding = 18
name_code = name_code_option = c.Util.STR_CU
# df_metrics = get_data.get_comoption_mktdata(start_date, end_date,name_code)
df_future_c1_daily = get_data.get_mktdata_future_c1_daily(
dt_histvol, end_date, name_code)
# df_future_c1_daily = get_data.get_index_mktdata(dt_histvol, end_date, name_code)
""" 历史波动率 """
vol_10 = Histvol.hist_vol(df_future_c1_daily[c.Util.AMT_CLOSE], n=10)
vol_20 = Histvol.hist_vol(df_future_c1_daily[c.Util.AMT_CLOSE], n=20)
vol_30 = Histvol.hist_vol(df_future_c1_daily[c.Util.AMT_CLOSE], n=30)
vol_60 = Histvol.hist_vol(df_future_c1_daily[c.Util.AMT_CLOSE], n=60)
vol_90 = Histvol.hist_vol(df_future_c1_daily[c.Util.AMT_CLOSE], n=90)
df = df_future_c1_daily[[c.Util.DT_DATE]]
df['hist_vol_10'] = vol_10
df['hist_vol_20'] = vol_20
df['hist_vol_30'] = vol_30
df['hist_vol_60'] = vol_60
df['hist_vol_90'] = vol_90
df = df.dropna()
df = df.sort_values(c.Util.DT_DATE, ascending=False)
print(df)
df.to_csv('../../../data/' + name_code + '_histvol.csv')
m_id_c1 = dict_core_underlying[namecode]
# m_df_future_c1 = get_mktdata_future_c1(dt_start, dt_date, namecode)
m_df_future_c1 = get_data.get_future_c1_by_option_daily(dt_start,
dt_date,
namecode,
min_holding=5)
# m_df_future = get_mktdata_future(admin.table_futures_mktdata(), m_id_c1, dt_yesterday, dt_date)
m_dict_iv_call, m_dict_iv_put = iv_at_the_money(dt_date, dt_yesterday,
namecode)
m_iv_call_today = m_dict_iv_call[dt_date]
m_iv_put_today = m_dict_iv_put[dt_date]
m_iv_call_yesterday = m_dict_iv_call[dt_yesterday]
m_iv_put_yesterday = m_dict_iv_put[dt_yesterday]
m_df_future_c1['hist_60-1'] = calculate_histvol(
m_df_future_c1['amt_close'], 60)
m_df_future_c1['hist_60-2'] = Histvol.hist_vol(m_df_future_c1['amt_close'],
60)
m_hisvol_1M = list(calculate_histvol(m_df_future_c1['amt_close'],
20))[-1] * 100
m_hisvol_3M = list(calculate_histvol(m_df_future_c1['amt_close'],
60))[-1] * 100
dict_iv = iv_htbr(dt_date, dt_yesterday, namecode)
iv_today = dict_iv[dt_date]
iv_last = dict_iv[dt_yesterday]
text_2 = '隐含波动率方面，{}期权{}月合约隐含波动率整体{}，其中，看跌期权平值合约隐含波动率为{:.2f}%，' \
'较前一周({:.2f}%){}约{:.1f}%，' \
'看涨期权平值合约隐含波动率为{:.2f}%，较前一周({:.2f}%){}约{:.1f}%。' \
'从认沽认购平价隐含波动率来看，{}期权隐含波动率为{:.2f}%，前一周该值为{:.2f}%，{}{:.2f}%。' \
'期货主力合约近1M历史波动率为{:.2f}%，近3M历史波动率为{:.2f}%，' \
'平值期权隐含波动率较近1M历史波动率{}，较近3M历史波动率{}{}。'\
'\n \n'.format(