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()
""" Volatility Strategy: Straddle """
d1 = df_future_c1_daily[c.Util.DT_DATE].values[0]
d2 = df_metrics[c.Util.DT_DATE].values[0]
d = max(d1, d2)
print(d1, d2, d)
df_metrics = df_metrics[df_metrics[c.Util.DT_DATE] >= d].reset_index(drop=True)
df_c1 = df_future_c1_daily[
df_future_c1_daily[c.Util.DT_DATE] >= d].reset_index(drop=True)
optionset = BaseOptionSet(df_metrics)
optionset.init()
d1 = optionset.eval_date
df_c1 = df_c1.rename(columns={c.Util.ID_INSTRUMENT: 'id_future'})
df_c1.loc[:, c.Util.ID_INSTRUMENT] = name_code
hedging = SytheticOption(df_c1, frequency=c.FrequentType.DAILY)
hedging.init()
account = BaseAccount(init_fund=c.Util.BILLION, leverage=1.0, rf=0.03)
maturity1 = optionset.select_maturity_date(nbr_maturity=0, min_holding=15)
empty_position = True
unit_p = None
unit_c = None
atm_strike = None
buy_write = c.BuyWrite.WRITE
print(optionset.eval_date, hedging.eval_date)
while optionset.eval_date <= end_date:
if account.cash <= 0: break
if maturity1 > end_date: # Final close out all.
close_out_orders = account.creat_close_out_order()
for order in close_out_orders:
""" Volatility Strategy: Straddle """
d1 = df_future_c1_daily[c.Util.DT_DATE].values[0]
d2 = df_metrics[c.Util.DT_DATE].values[0]
d = max(d1, d2)
print(d1, d2, d)
df_metrics = df_metrics[df_metrics[c.Util.DT_DATE] >= d].reset_index(drop=True)
df_c1 = df_future_c1_daily[
df_future_c1_daily[c.Util.DT_DATE] >= d].reset_index(drop=True)
df_c_all = df_futures_all_daily[
df_futures_all_daily[c.Util.DT_DATE] >= d].reset_index(drop=True)
optionset = BaseOptionSet(df_metrics)
optionset.init()
d1 = optionset.eval_date
hedging = SytheticOption(df_c1,
frequency=c.FrequentType.DAILY,
df_c1_daily=df_c1,
df_futures_all_daily=df_c_all)
hedging.init()
print(optionset.eval_date, hedging.eval_date)
account = BaseAccount(init_fund=c.Util.BILLION, leverage=1.0, rf=0.03)
empty_position = True
unit_p = None
unit_c = None
atm_strike = None
buy_write = c.BuyWrite.WRITE
maturity1 = optionset.select_maturity_date(nbr_maturity=0, min_holding=15)
id_future = hedging.current_state[c.Util.ID_FUTURE]
idx_hedge = 0
flag_hedge = False
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_vol_1m = Histvol.hist_vol(df_future_c1_daily)
# df_parkinson_1m = Histvol.parkinson_number(df_future_c1_daily)
# df_garman_klass = Histvol.garman_klass(df_future_c1_daily)
# df_hist_vol = df_vol_1m.join(df_parkinson_1m, how='left')
# df_hist_vol = df_hist_vol.join(df_garman_klass, how='left')
df_future_c1_daily = df_future_c1_daily[
df_future_c1_daily[Util.DT_DATE] >= start_date].reset_index(drop=True)
synthetic_option = SytheticOption(df_c1_minute=df_future_c1,
df_c1_daily=df_future_c1_daily,
df_futures_all_daily=df_futures_all_daily,
df_index_daily=df_index)
synthetic_option.init()
underlying = BaseInstrument(df_data=df_index)
underlying.init()
account = BaseAccount(2 * Util.BILLION, leverage=10.0, rf=0.0)
trading_desk = Trade()
#####################################################################
# """ Init position """
strike = synthetic_option.underlying_index_state_daily[Util.AMT_CLOSE]
dt_maturity = synthetic_option.eval_date + datetime.timedelta(days=30)
vol = 0.2
Option = EuropeanOption(strike, dt_maturity, OptionType.PUT)
delta = synthetic_option.get_black_delta(Option, vol)
id_future = synthetic_option.current_state[Util.ID_INSTRUMENT]
class SyntheticOptionHedgedPortfolio():
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 next(self):
if self.synthetic_option.is_last_minute():
self.underlying.next()
self.synthetic_option.next()
def init_portfolio(self, fund):
""" Init position """
dt_maturity = self.synthetic_option.eval_date + datetime.timedelta(
days=self.ttm)
# strike = self.underlying.mktprice_close()
strike = self.synthetic_option.mktprice_close()
# dt_maturity = self.synthetic_option.eval_date + datetime.timedelta(days=30)
self.Option = EuropeanOption(strike, dt_maturity, OptionType.PUT)
# self.Option = EuropeanOption(strike, dt_maturity, OptionType.CALL)
""" 用第一天的日收盘价开仓标的现货多头头寸 """
underlying_unit = np.floor(fund * self.invest_underlying_ratio /
self.underlying.mktprice_close())
order_underlying = self.account.create_trade_order(
self.underlying, LongShort.LONG, underlying_unit)
execution_record = self.underlying.execute_order(
order_underlying,
slippage=0,
execute_type=ExecuteType.EXECUTE_ALL_UNITS)
self.account.add_record(execution_record, self.underlying)
self.synthetic_option.amt_option = underlying_unit
""" 用第一天的成交量加权均价开仓/调整复制期权头寸 """
vol = self.get_vol()
self.delta = self.synthetic_option.get_black_delta(self.Option, vol)
synthetic_unit = self.synthetic_option.get_synthetic_unit(
self.delta, self.buywrite)
self.synthetic_option.synthetic_unit = synthetic_unit
if synthetic_unit > 0:
long_short = LongShort.LONG
else:
long_short = LongShort.SHORT
order = self.account.create_trade_order(self.synthetic_option,
long_short, synthetic_unit)
execution_record = self.synthetic_option.execute_order_by_VWAP(
order, slippage=0, execute_type=ExecuteType.EXECUTE_ALL_UNITS)
self.account.add_record(execution_record, self.synthetic_option)
self.account.daily_accounting(self.synthetic_option.eval_date)
self.add_additional_to_account()
self.disp()
self.next()
def rebalance_sythetic_option(self):
""" Reset and Rebalance sythetic option """
dt_maturity = self.synthetic_option.eval_date + datetime.timedelta(
days=self.ttm)
# strike = self.underlying.mktprice_close()
strike = self.synthetic_option.mktprice_close()
self.Option = EuropeanOption(strike, dt_maturity, OptionType.PUT)
# self.Option = EuropeanOption(strike, dt_maturity, OptionType.CALL)
""" 用成交量加权均价调整复制期权头寸 """
vol = self.get_vol()
self.delta = self.synthetic_option.get_black_delta(self.Option, vol)
synthetic_unit = self.synthetic_option.get_rebalancing_unit(
self.delta, self.Option, vol,
self.synthetic_option.mktprice_close(), DeltaBound.NONE,
self.buywrite)
self.synthetic_option.synthetic_unit += synthetic_unit
if synthetic_unit > 0:
long_short = LongShort.LONG
else:
long_short = LongShort.SHORT
order = self.account.create_trade_order(self.synthetic_option,
long_short, synthetic_unit)
execution_record = self.synthetic_option.execute_order_by_VWAP(
order, slippage=0, execute_type=ExecuteType.EXECUTE_ALL_UNITS)
self.account.add_record(execution_record, self.synthetic_option)
self.account.daily_accounting(self.synthetic_option.eval_date)
self.add_additional_to_account()
self.disp()
self.next()
return
def hedge(self, dt_end=None):
id_future = self.synthetic_option.current_state[Util.ID_INSTRUMENT]
if dt_end is None:
dt_end = self.Option.dt_maturity
dt_time_end = datetime.datetime(dt_end.year, dt_end.month, dt_end.day,
15, 00, 0)
while self.synthetic_option.has_next(
) and self.synthetic_option.eval_datetime <= dt_time_end:
if id_future != self.synthetic_option.current_state[
Util.ID_INSTRUMENT]:
long_short = self.account.trade_book.loc[id_future,
Util.TRADE_LONG_SHORT]
hold_unit = -self.account.trade_book.loc[id_future,
Util.TRADE_UNIT]
spot = self.synthetic_option.mktprice_close()
vol = self.get_vol()
self.delta = self.synthetic_option.get_black_delta(
self.Option, vol, spot)
synthetic_unit = self.synthetic_option.get_synthetic_unit(
self.delta, self.buywrite) # 按照移仓换月日的收盘价计算Delta
id_c2 = self.synthetic_option.current_state[Util.ID_INSTRUMENT]
open_unit = synthetic_unit
self.synthetic_option.synthetic_unit += open_unit - hold_unit
close_execution_record, open_execution_record \
= self.synthetic_option.shift_contract_by_VWAP(id_c1=id_future,
id_c2=id_c2,
hold_unit=hold_unit,
open_unit=open_unit,
long_short=long_short,
slippage=self.slippage,
execute_type=ExecuteType.EXECUTE_ALL_UNITS
)
self.account.add_record(close_execution_record,
self.synthetic_option)
self.synthetic_option._id_instrument = id_c2
self.account.add_record(open_execution_record,
self.synthetic_option)
""" 更新当前持仓头寸 """
""" USE SAME UNIT TO SHIFT CONTRACT AND USE CLOSE PRICE TO REBALANCING DELTA CHANGE. """
print(' Relancing after shift contract, ',
self.synthetic_option.eval_date)
id_future = id_c2
if self.synthetic_option.eval_datetime.time() == datetime.time(
9, 30, 0):
self.next()
if self.synthetic_option.eval_date == self.synthetic_option.get_next_state_date(
):
if not self.if_hedge('5min'):
self.next()
continue
self.rebalancing()
if self.synthetic_option.is_last_minute():
self.account.daily_accounting(
self.synthetic_option.eval_date) # 该日的收盘结算
self.add_additional_to_account()
self.disp()
if self.synthetic_option.eval_date == dt_end and self.synthetic_option.is_last_minute(
):
self.close_out()
self.next()
def if_hedge(self, cd):
if cd == '1h':
""" 1h """
if self.synthetic_option.eval_datetime.minute == 0:
return True
else:
return False
elif cd == '10min':
""" 10min """
if self.synthetic_option.eval_datetime.minute % 10 == 0:
return True
else:
return False
elif cd == '5min':
""" 5min """
if self.synthetic_option.eval_datetime.minute % 5 == 0:
return True
else:
return False
elif cd == '1min':
return True
elif cd == 'half_day':
if self.synthetic_option.eval_datetime.time() == datetime.time(11, 29, 00) or \
self.synthetic_option.eval_datetime.time() == datetime.time(14, 59, 00):
return True
else:
return False
def rebalancing(self):
vol = self.get_vol()
self.delta = self.synthetic_option.get_black_delta(self.Option, vol)
rebalance_unit = self.synthetic_option.get_rebalancing_unit(
self.delta, self.Option, vol,
self.synthetic_option.mktprice_close(),
DeltaBound.WHALLEY_WILLMOTT, self.buywrite)
self.synthetic_option.synthetic_unit += rebalance_unit
if rebalance_unit > 0:
long_short = LongShort.LONG
else:
long_short = LongShort.SHORT
order = self.account.create_trade_order(self.synthetic_option,
long_short, rebalance_unit)
execution_record = self.synthetic_option.execute_order(
order,
slippage=self.slippage,
execute_type=ExecuteType.EXECUTE_ALL_UNITS)
self.account.add_record(execution_record, self.synthetic_option)
def close_out(self):
while not self.synthetic_option.is_last_minute():
self.next()
close_out_orders = self.account.creat_close_out_order()
for order in close_out_orders:
execution_record = self.account.dict_holding[
order.id_instrument].execute_order(
order,
slippage=0,
execute_type=ExecuteType.EXECUTE_ALL_UNITS)
self.account.add_record(
execution_record,
self.account.dict_holding[order.id_instrument])
self.account.daily_accounting(self.synthetic_option.eval_date)
self.add_additional_to_account()
self.disp()
self.synthetic_option.sythetic_unit = 0
""" Final NPV check """
self.df_records = pd.DataFrame(self.account.list_records)
total_pnl = self.df_records[Util.TRADE_REALIZED_PNL].sum()
final_npv = (self.fund + total_pnl) / self.fund
print('calculate final npv from adding up realized pnl ; ', final_npv)
def get_vol(self):
date = self.synthetic_option.eval_date
if date in self.df_vol_1m.index:
# vol = self.df_vol_1m.loc[date, Util.AMT_HISTVOL]
vol = self.df_garman_klass.loc[self.synthetic_option.eval_date,
Util.AMT_GARMAN_KLASS]
else:
dt1 = Util.largest_element_less_than(port.trade_dates, date)
vol = self.df_garman_klass.loc[dt1, Util.AMT_GARMAN_KLASS]
# vol = self.df_vol_1m.loc[dt1, Util.AMT_HISTVOL]
return vol
def add_additional_to_account(self):
# self.account.account.loc[
# self.synthetic_option.eval_date, 'underlying_npv_1'] = self.invest_underlying_ratio * self.underlying.mktprice_close() / self.init_spot + 1 - self.invest_underlying_ratio
self.account.account.loc[
self.synthetic_option.eval_date,
'underlying_npv'] = self.underlying.mktprice_close(
) / self.init_spot
self.account.account.loc[
self.synthetic_option.eval_date,
'underlying_price'] = self.underlying.mktprice_close()
self.account.account.loc[
self.synthetic_option.eval_date,
'if_c1'] = self.synthetic_option.mktprice_close()
self.account.account.loc[self.synthetic_option.eval_date, 'hedge_position'] \
= - self.account.trade_book[self.account.trade_book[Util.TRADE_LONG_SHORT] == LongShort.SHORT][
Util.TRADE_UNIT].sum()
self.account.account.loc[self.synthetic_option.eval_date, 'hedge_ratio'] = \
self.account.account.loc[self.synthetic_option.eval_date, Util.PORTFOLIO_SHORT_POSITION_SCALE] / \
self.account.account.loc[self.synthetic_option.eval_date, Util.PORTFOLIO_LONG_POSITION_SCALE]
self.account.account.loc[self.synthetic_option.eval_date, 'pct_margin_unrealized_pnl'] = \
self.account.account.loc[
self.synthetic_option.eval_date, Util.MARGIN_UNREALIZED_PNL] / self.account.init_fund
self.account.account.loc[self.synthetic_option.eval_date, 'pct_nonmargin_unrealized_pnl'] = \
self.account.account.loc[
self.synthetic_option.eval_date, Util.NONMARGIN_UNREALIZED_PNL] / self.account.init_fund
self.account.account.loc[self.synthetic_option.eval_date, 'pct_realized_pnl'] = \
self.account.account.loc[self.synthetic_option.eval_date, Util.TRADE_REALIZED_PNL] / self.account.init_fund
self.account.account.loc[self.synthetic_option.eval_date,
'delta'] = self.delta
def save_results(self):
self.df_records.to_csv('../../data/trade_records.csv')
self.account.account.to_csv('../../data/account.csv')
# self.df_hedge_info = pd.DataFrame(self.list_hedge_info)
# self.df_hedge_info.to_csv('../../data/hedge_info.csv')
self.df_analysis.to_csv('../../data/df_analysis.csv')
self.account.trade_book_daily.to_csv('../../data/trade_book_daily.csv')
def disp(self):
if self.synthetic_option.eval_date != self.underlying.eval_date:
print('Date miss matched!')
try:
average_cost = int(self.account.trade_book[self.account.trade_book[
Util.TRADE_LONG_SHORT] == LongShort.SHORT][
Util.AVERAGE_POSITION_COST].values[0])
except:
average_cost = 0
pass
print(
self.synthetic_option.eval_datetime,
self.account.account.loc[self.synthetic_option.eval_date,
Util.PORTFOLIO_NPV],
self.underlying.mktprice_close() / self.init_spot,
# self.account.account.loc[self.synthetic_option.eval_date, 'hedge_position'],
# self.synthetic_option.synthetic_unit,
int(self.Option.strike),
int(self.underlying.mktprice_close()),
int(self.synthetic_option.mktprice_close()),
average_cost,
round(self.delta, 2),
round(
self.account.account.loc[self.synthetic_option.eval_date,
'hedge_ratio'], 2),
# self.account.cash,
round(
100 * self.account.account.loc[self.synthetic_option.eval_date,
'pct_margin_unrealized_pnl'],
1),
'%',
round(
100 * self.account.account.loc[self.synthetic_option.eval_date,
'pct_nonmargin_unrealized_pnl'],
1),
'%',
round(
100 * self.account.account.loc[self.synthetic_option.eval_date,
'pct_realized_pnl'], 1),
'%',
self.underlying.eval_date,
)
def reset_option_maturity(self, dt_maturity=None):
if dt_maturity is None:
dt_maturity = self.synthetic_option.eval_date + datetime.timedelta(
days=self.ttm)
self.Option.dt_maturity = dt_maturity
def reset_option_strike(self):
strike = self.synthetic_option.mktprice_close()
self.Option.strike = strike
def analysis(self, dt_start, dt_end):
""" Replicate Period Result Analysis """
self.df_records = pd.DataFrame(self.account.list_records)
analysis = pd.Series()
df_hedge_records = self.df_records[
(self.df_records[Util.ID_INSTRUMENT] != 'index_300sh')
& (self.df_records[Util.DT_TRADE] >= dt_start) &
(self.df_records[Util.DT_TRADE] <= dt_end)]
init_stock_value = self.account.account.loc[
dt_start, Util.PORTFOLIO_TRADES_VALUE]
init_stock_price = \
self.underlying.df_data[self.underlying.df_data[Util.DT_DATE] == dt_start][Util.AMT_CLOSE].values[0]
terminal_stock_price = \
self.underlying.df_data[self.underlying.df_data[Util.DT_DATE] == dt_end][Util.AMT_CLOSE].values[0]
replicate_pnl = df_hedge_records[Util.TRADE_REALIZED_PNL].sum()
option_payoff = self.synthetic_option.amt_option * max(
init_stock_price - terminal_stock_price, 0)
replicate_cost = replicate_pnl - option_payoff
replicate_cost_future = replicate_pnl - self.synthetic_option.amt_option * max(
self.Option.strike - terminal_stock_price, 0)
pct_replicate_cost = replicate_cost / init_stock_value
pct_replicate_pnl = replicate_pnl / init_stock_value
transaction_cost = df_hedge_records[Util.TRANSACTION_COST].sum()
init_portfolio_value = self.account.account.loc[dt_start,
Util.PORTFOLIO_VALUE]
trade_value = np.abs(df_hedge_records[Util.TRADE_BOOK_VALUE]).sum()
ratio = trade_value / init_portfolio_value
pct_underlying_pnl = (terminal_stock_price -
init_stock_price) / init_stock_price
analysis['ratio'] = ratio
analysis['dt_start'] = dt_start
analysis['dt_end'] = dt_end
analysis['init_stock_value'] = init_stock_value
analysis['replicate_pnl'] = replicate_pnl
analysis['option_payoff'] = option_payoff
analysis['replicate_cost_spot'] = replicate_cost
analysis['replicate_cost_future'] = replicate_cost_future
analysis['pct_replicate_cost'] = pct_replicate_cost
analysis['pct_replicate_pnl'] = pct_replicate_pnl
analysis['pct_underlying_pnl'] = pct_underlying_pnl
analysis['transaction_cost'] = transaction_cost
analysis['dt_maturity'] = self.Option.dt_maturity
self.df_analysis = self.df_analysis.append(analysis, ignore_index=True)
class VolTrading(object):
def __init__(self, start_date, end_date, df_metrics, df_vol,
df_future_c1_daily, df_futures_all_daily):
self.min_holding = 20
self.slippage = 0
self.nbr_maturity = 0
self.moneyness_rank = 0
self.m_notional = 1
self.rf = 0.03
self.n_premium_std = 90 #用于计算权利金溢价1倍标准差的数据期限
self.n_hv = 20 # 用与期权期限相匹配的历史波动率期限
self.n_close_by_maturity = 5
self.min_premium = 2.0 / 100.0 # 对冲成本对应的开平仓最低隐含波动率溢价
self.n_llt = 5
self.cd_option_price = c.CdTradePrice.CLOSE
self.cd_future_price = c.CdTradePrice.CLOSE
self.cd_price = c.CdPriceType.CLOSE
self.start_date = start_date
self.end_date = end_date
self.df_metrics = df_metrics
self.df_vol = df_vol
self.df_f_c1 = df_future_c1_daily
self.df_f_all = df_futures_all_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 prepare_timing(self):
self.timing_hviv()
self.timing_llt()
def timing_hviv(self):
self.df_vol['amt_premium'] = self.df_vol[
c.Util.PCT_IMPLIED_VOL] - self.df_vol['amt_hv']
self.df_vol['amt_1std'] = c.Statistics.standard_deviation(
self.df_vol['amt_premium'], n=self.n_premium_std)
self.df_vol['amt_2std'] = 2 * c.Statistics.standard_deviation(
self.df_vol['amt_premium'], n=self.n_premium_std)
self.df_vol['percentile_95'] = c.Statistics.percentile(
self.df_vol[c.Util.PCT_IMPLIED_VOL], n=252, percent=0.95)
def timing_llt(self):
self.df_vol['LLT_' + str(self.n_llt)] = LLT(
self.df_vol[c.Util.PCT_IMPLIED_VOL], self.n_llt)
self.df_vol['LLT_signal_' +
str(self.n_llt)] = self.df_vol['LLT_' +
str(self.n_llt)].diff()
def open_signal(self):
pass
def close_signal(self):
pass
def open_signal_llt(self):
dt_date = self.optionset.eval_date
if dt_date not in self.df_vol.index:
return False
if self.df_vol.loc[dt_date, 'LLT_signal_5'] < 0: # 隐含波动率处于下行区间
return True
else:
return False
def close_signal_llt(self):
dt_date = self.optionset.eval_date
if dt_date not in self.df_vol.index:
return False
if self.df_vol.loc[dt_date, 'LLT_signal_5'] > 0: # 隐含波动率处于上行区间
return True
else:
return False
def open_signal_ivhv(self):
dt_date = self.optionset.eval_date
if dt_date not in self.df_vol.index:
return False
amt_premium = self.df_vol.loc[dt_date, 'amt_premium']
amt_1std = self.df_vol.loc[dt_date, 'amt_1std']
if amt_premium > amt_1std and amt_premium > self.min_premium: # 隐含波动率相比历史波动率具有一定溢价
return True
else:
return False
def close_signal_ivhv(self):
dt_date = self.optionset.eval_date
amt_premium = self.df_vol.loc[dt_date, 'amt_premium']
if amt_premium <= self.min_premium:
return True
else:
return False
def close_signal_maturity(self):
dt_maturity = None
for option in self.account.dict_holding.values():
if isinstance(option, BaseOption) and option is not None:
dt_maturity = option.maturitydt()
break
if (dt_maturity -
self.optionset.eval_date).days <= self.n_close_by_maturity:
return True
else:
return False
def strategy(self):
return self.short_straddle()
def short_straddle(self):
maturity = self.optionset.select_maturity_date(
nbr_maturity=self.nbr_maturity, min_holding=self.min_holding)
list_atm_call, list_atm_put = self.optionset.get_options_list_by_moneyness_mthd1(
moneyness_rank=self.moneyness_rank,
maturity=maturity,
cd_price=self.cd_price)
atm_call = self.optionset.select_higher_volume(list_atm_call)
atm_put = self.optionset.select_higher_volume(list_atm_put)
if atm_call is None or atm_put is None:
return
else:
return [atm_call, atm_put]
def excute(self, strategy):
if strategy is None:
return False
else:
pv = self.account.portfolio_total_value
self.option_holding = {}
for option in strategy:
unit = np.floor(
np.floor(pv / option.strike()) /
option.multiplier()) * self.m_notional
order = self.account.create_trade_order(
option,
c.LongShort.SHORT,
unit,
cd_trade_price=self.cd_option_price)
record = option.execute_order(order,
slippage_rate=self.slippage)
self.account.add_record(record, option)
self.option_holding.update({option: unit})
return True
def close_out(self):
close_out_orders = self.account.creat_close_out_order(
cd_trade_price=c.CdTradePrice.CLOSE)
for order in close_out_orders:
execution_record = self.account.dict_holding[order.id_instrument] \
.execute_order(order, slippage_rate=self.slippage, execute_type=c.ExecuteType.EXECUTE_ALL_UNITS)
self.account.add_record(
execution_record,
self.account.dict_holding[order.id_instrument])
def close_out_1(self):
for option in self.account.dict_holding.values():
if isinstance(option, BaseOption):
order = self.account.create_close_order(
option, cd_trade_price=self.cd_option_price)
else:
order = self.account.create_close_order(
option, cd_trade_price=self.cd_future_price)
record = option.execute_order(order, slippage_rate=self.slippage)
self.account.add_record(record, option)
# TODO: Use Delta Bound Model
def delta_hedge(self):
option1 = list(self.option_holding.keys())[0]
iv_htbr = self.optionset.get_iv_by_otm_iv_curve(
dt_maturity=option1.maturitydt(),
strike=option1.applicable_strike())
options_delta = 0
for option in self.option_holding.keys():
unit = self.option_holding[option]
options_delta += unit * option.get_delta(
iv_htbr) * option.multiplier()
hedge_unit = self.hedging.get_hedge_rebalancing_unit(
options_delta, c.BuyWrite.WRITE)
self.hedging.synthetic_unit += -hedge_unit
if hedge_unit > 0:
long_short = c.LongShort.LONG
else:
long_short = c.LongShort.SHORT
order_u = self.account.create_trade_order(
self.hedging,
long_short,
hedge_unit,
cd_trade_price=self.cd_future_price)
record_u = self.hedging.execute_order(order_u,
slippage_rate=self.slippage)
self.account.add_record(record_u, self.hedging)
# print('')
def back_test(self):
empty_position = True
while self.optionset.eval_date <= self.end_date:
# if self.optionset.eval_date == datetime.date(2017, 1, 19):
# print('')
if self.optionset.eval_date >= self.end_date: # Final close out all.
self.close_out()
self.account.daily_accounting(self.optionset.eval_date)
print(self.optionset.eval_date, ' close out ')
print(
self.optionset.eval_date, self.hedging.eval_date,
self.account.account.loc[self.optionset.eval_date,
c.Util.PORTFOLIO_NPV],
int(self.account.cash))
break
# 标的移仓换月
if self.hedging.close_old_contract_month(
self.account, self.slippage,
cd_price=self.cd_future_price):
self.hedging.synthetic_unit = 0
# 平仓
if not empty_position:
if self.close_signal():
self.close_out_1()
self.hedging.synthetic_unit = 0
empty_position = True
# 开仓
if empty_position and self.open_signal():
s = self.strategy()
empty_position = not self.excute(s)
# Delta hedge
if not empty_position:
self.delta_hedge()
self.account.daily_accounting(self.optionset.eval_date)
if not self.optionset.has_next():
break
self.optionset.next()
self.hedging.next()
return self.account