def dual_thrust(asset, start_date, end_date, scenarios, config):
nearby = config["nearby"]
rollrule = config["rollrule"]
start_d = misc.day_shift(start_date, "-2b")
file_prefix = config["file_prefix"] + "_" + asset + "_"
mdf = misc.nearby(asset, nearby, start_d, end_date, rollrule, "m", need_shift=True)
mdf = backtest.cleanup_mindata(mdf, asset)
output = {}
for ix, s in enumerate(scenarios):
config["win"] = s[1]
config["k"] = s[0]
config["m"] = s[2]
config["f"] = s[3]
(res, closed_trades, ts) = dual_thrust_sim(mdf, config)
output[ix] = res
print "saving results for scen = %s" % str(ix)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(ix) + "_trades.csv"
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(ix) + "_dailydata.csv"
ts.to_csv(fname)
fname = file_prefix + "stats.csv"
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def dual_thrust( asset, start_date, end_date, scenarios, config):
nearby = config['nearby']
rollrule = config['rollrule']
start_d = misc.day_shift(start_date, '-4b')
file_prefix = config['file_prefix'] + '_' + asset + '_'
ddf = misc.nearby(asset, nearby, start_d, end_date, rollrule, 'd', need_shift=True)
mdf = misc.nearby(asset, nearby, start_d, end_date, rollrule, 'm', need_shift=True)
mdf = backtest.cleanup_mindata(mdf, asset)
#ddf = dh.conv_ohlc_freq(mdf, 'D')
output = {}
for ix, s in enumerate(scenarios):
config['win'] = s[1]
config['k'] = s[0]
config['m'] = s[2]
(res, closed_trades, ts) = dual_thrust_sim( ddf, mdf, config)
output[ix] = res
print 'saving results for scen = %s' % str(ix)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(ix) + '_trades.csv'
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(ix) + '_dailydata.csv'
ts.to_csv(fname)
fname = file_prefix + 'stats.csv'
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def aberration( asset, start_date, end_date, freqs, windows, config):
nearby = config['nearby']
rollrule = config['rollrule']
file_prefix = config['file_prefix'] + '_' + asset + '_'
df = misc.nearby(asset, nearby, start_date, end_date, rollrule, 'm', need_shift=True)
df = backtest.cleanup_mindata(df, asset)
output = {}
for ix, freq in enumerate(freqs):
xdf = dh.conv_ohlc_freq(df, freq)
for iy, win in enumerate(windows):
idx = ix*10+iy
config['win'] = win
(res, closed_trades, ts) = aberration_sim( xdf, config)
output[idx] = res
print 'saving results for scen = %s' % str(idx)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(idx) + '_trades.csv'
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(idx) + '_dailydata.csv'
ts.to_csv(fname)
fname = file_prefix + 'stats.csv'
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def fisher_swing(asset, start_date, end_date, freqs, windows, config):
nearby = config["nearby"]
rollrule = config["rollrule"]
file_prefix = config["file_prefix"] + "_" + asset + "_"
df = misc.nearby(asset, nearby, start_date, end_date, rollrule, "m", need_shift=True)
df = backtest.cleanup_mindata(df, asset)
output = {}
for ix, freq in enumerate(freqs):
xdf = dh.conv_ohlc_freq(df, freq)
for iy, win in enumerate(windows):
idx = ix * 10 + iy
config["win"] = win
config["freq"] = freq
(res, closed_trades, ts) = fisher_swing_sim(df, xdf, config)
output[idx] = res
print "saving results for scen = %s" % str(idx)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(idx) + "_trades.csv"
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(idx) + "_dailydata.csv"
ts.to_csv(fname)
fname = file_prefix + "stats.csv"
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def r_breaker( asset, start_date, end_date, scenarios, freqs, config):
nearby = config['nearby']
rollrule = config['rollrule']
start_d = misc.day_shift(start_date, '-1b')
file_prefix = config['file_prefix'] + '_' + asset + '_'
ddf = misc.nearby(asset, nearby, start_date, end_date, rollrule, 'd', need_shift=True)
mdf = misc.nearby(asset, nearby, start_date, end_date, rollrule, 'm', need_shift=True)
mdf = backtest.cleanup_mindata(mdf, asset)
#ddf = dh.conv_ohlc_freq(mdf, 'D')
output = {}
for ix, freq in enumerate(freqs):
if freq !='1min':
df = dh.conv_ohlc_freq(mdf, freq)
else:
df = mdf
for iy, k in enumerate(scenarios):
idx = ix*10+iy
config['k'] = k
(res, closed_trades, ts) = r_breaker_sim( ddf, df, config)
output[idx] = res
print 'saving results for scen = %s' % str(idx)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(idx) + '_trades.csv'
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(idx) + '_dailydata.csv'
ts.to_csv(fname)
fname = file_prefix + 'stats.csv'
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def fisher_swing(asset, start_date, end_date, freqs, windows, config):
nearby = config['nearby']
rollrule = config['rollrule']
file_prefix = config['file_prefix'] + '_' + asset + '_'
df = misc.nearby(asset,
nearby,
start_date,
end_date,
rollrule,
'm',
need_shift=True)
df = backtest.cleanup_mindata(df, asset)
output = {}
for ix, freq in enumerate(freqs):
xdf = dh.conv_ohlc_freq(df, freq)
for iy, win in enumerate(windows):
idx = ix * 10 + iy
config['win'] = win
config['freq'] = freq
(res, closed_trades, ts) = fisher_swing_sim(df, xdf, config)
output[idx] = res
print 'saving results for scen = %s' % str(idx)
all_trades = {}
for i, tradepos in enumerate(closed_trades):
all_trades[i] = strat.tradepos2dict(tradepos)
fname = file_prefix + str(idx) + '_trades.csv'
trades = pd.DataFrame.from_dict(all_trades).T
trades.to_csv(fname)
fname = file_prefix + str(idx) + '_dailydata.csv'
ts.to_csv(fname)
fname = file_prefix + 'stats.csv'
res = pd.DataFrame.from_dict(output)
res.to_csv(fname)
return
def get_cont_data(asset,
start_date,
end_date,
freq='1m',
nearby=1,
rollrule='-10b'):
if nearby == 0:
mdf = mysqlaccess.load_min_data_to_df('fut_min',
asset,
start_date,
end_date,
minid_start=300,
minid_end=2114,
database='hist_data')
mdf['contract'] = asset
else:
mdf = misc.nearby(asset,
nearby,
start_date,
end_date,
rollrule,
'm',
need_shift=True,
database='hist_data')
mdf = backtest.cleanup_mindata(mdf, asset)
xdf = dh.conv_ohlc_freq(mdf,
freq,
extra_cols=['contract'],
bar_func=dh.bar_conv_func2)
return xdf
def conv_db_htick2min(db_table,
inst_file,
out_table='hist_fut_min',
database='hist_data',
dstep=10):
conf_dict = {}
instIDs = []
if inst_file == '':
instIDs = get_col_dist_values(database + '.' + db_table, 'instID', {})
conf_dict = {'instIDs': instIDs}
try:
inst_file = 'instID_file.json'
with open(inst_file, 'w') as ofile:
json.dump(conf_dict, ofile)
except:
pass
else:
with open(inst_file, 'r') as infile:
conf_dict = json.load(infile)
instIDs = conf_dict['instIDs']
dbconfig = {
'user': '******',
'password': '******',
'host': 'localhost',
'database': database
}
cnx = mysqlconn.connect(**dbconfig)
for inst in instIDs:
field_dict = {'instID': "\'" + inst + "\'"}
datestr_list = get_col_dist_values(database + '.' + db_table, 'date',
field_dict)
mdata = pd.DataFrame()
prod = misc.inst2product(inst)
exch = misc.inst2exch(inst)
num_run = (len(datestr_list) + dstep - 1) / dstep
for idx in range(num_run):
s_idx = idx * dstep
e_idx = min((idx + 1) * dstep - 1, len(datestr_list) - 1)
sdate = datetime.datetime.strptime(datestr_list[s_idx],
"%Y-%m-%d").date()
edate = datetime.datetime.strptime(datestr_list[e_idx],
"%Y-%m-%d").date()
df = load_hist_tick(db_table, inst, sdate, edate)
mdf = conv_ohlc_freq(df, '1Min')
mdf['min_id'] = (
(mdf.index.hour + 6) % 24) * 100 + mdf.index.minute
mdf = backtest.cleanup_mindata(mdf, prod)
mdf.index.name = 'datetime'
mdf['instID'] = inst
mdf['exch'] = exch
mdf = mdf.reset_index()
mdf.set_index(['instID', 'exch', 'datetime'], inplace=True)
mdf.to_sql(name=out_table,
flavor='mysql',
con=cnx,
if_exists='append')
cnx.commit()
print inst, sdate, edate, len(mdf)
cnx.close()
return
def prepare_data_env(self, inst, mid_day = True):
if self.instruments[inst].ptype == instrument.ProductType.Option:
return
if self.daily_data_days > 0 or mid_day:
self.logger.debug('Updating historical daily data for %s' % self.scur_day.strftime('%Y-%m-%d'))
daily_start = workdays.workday(self.scur_day, -self.daily_data_days, CHN_Holidays)
daily_end = self.scur_day
self.day_data[inst] = mysqlaccess.load_daily_data_to_df('fut_daily', inst, daily_start, daily_end)
df = self.day_data[inst]
if len(df) > 0:
self.instruments[inst].price = df['close'][-1]
self.instruments[inst].last_update = 0
self.instruments[inst].prev_close = df['close'][-1]
for fobj in self.day_data_func[inst]:
ts = fobj.sfunc(df)
df[ts.name]= pd.Series(ts, index=df.index)
if self.min_data_days > 0 or mid_day:
self.logger.debug('Updating historical min data for %s' % self.scur_day.strftime('%Y-%m-%d'))
d_start = workdays.workday(self.scur_day, -self.min_data_days, CHN_Holidays)
d_end = self.scur_day
min_start = int(self.instruments[inst].start_tick_id/1000)
min_end = int(self.instruments[inst].last_tick_id/1000)+1
mindata = mysqlaccess.load_min_data_to_df('fut_min', inst, d_start, d_end, minid_start=min_start, minid_end=min_end, database = 'blueshale')
mindata = backtest.cleanup_mindata(mindata, self.instruments[inst].product)
self.min_data[inst][1] = mindata
if len(mindata)>0:
min_date = mindata.index[-1].date()
if (len(self.day_data[inst].index)==0) or (min_date > self.day_data[inst].index[-1]):
ddf = data_handler.conv_ohlc_freq(mindata, 'd')
self.cur_day[inst]['open'] = float(ddf.open[-1])
self.cur_day[inst]['close'] = float(ddf.close[-1])
self.cur_day[inst]['high'] = float(ddf.high[-1])
self.cur_day[inst]['low'] = float(ddf.low[-1])
self.cur_day[inst]['volume'] = int(ddf.volume[-1])
self.cur_day[inst]['openInterest'] = int(ddf.openInterest[-1])
self.cur_min[inst]['datetime'] = pd.datetime(*mindata.index[-1].timetuple()[0:-3])
self.cur_min[inst]['open'] = float(mindata.ix[-1,'open'])
self.cur_min[inst]['close'] = float(mindata.ix[-1,'close'])
self.cur_min[inst]['high'] = float(mindata.ix[-1,'high'])
self.cur_min[inst]['low'] = float(mindata.ix[-1,'low'])
self.cur_min[inst]['volume'] = self.cur_day[inst]['volume']
self.cur_min[inst]['openInterest'] = self.cur_day[inst]['openInterest']
self.cur_min[inst]['min_id'] = int(mindata.ix[-1,'min_id'])
self.instruments[inst].price = float(mindata.ix[-1,'close'])
self.instruments[inst].last_update = 0
self.logger.debug('inst=%s tick data loaded for date=%s' % (inst, min_date))
for m in self.min_data_func[inst]:
if m != 1:
self.min_data[inst][m] = data_handler.conv_ohlc_freq(self.min_data[inst][1], str(m)+'min')
df = self.min_data[inst][m]
for fobj in self.min_data_func[inst][m]:
ts = fobj.sfunc(df)
df[ts.name]= pd.Series(ts, index=df.index)
def conv_db_htick2min(db_table, inst_file, out_table = 'hist_fut_min', database = 'hist_data', dstep = 10):
conf_dict = {}
instIDs = []
if inst_file == '':
instIDs = get_col_dist_values(database + '.' + db_table, 'instID',{})
conf_dict = {'instIDs': instIDs}
try:
inst_file = 'instID_file.json'
with open(inst_file, 'w') as ofile:
json.dump(conf_dict, ofile)
except:
pass
else:
with open(inst_file, 'r') as infile:
conf_dict = json.load(infile)
instIDs = conf_dict['instIDs']
dbconfig = {'user': '******', 'password':'******', 'host':'localhost', 'database': database}
cnx = mysqlconn.connect(**dbconfig)
for inst in instIDs:
field_dict = {'instID': "\'"+inst+"\'"}
datestr_list = get_col_dist_values(database + '.' + db_table, 'date', field_dict)
mdata = pd.DataFrame()
prod = misc.inst2product(inst)
exch = misc.inst2exch(inst)
num_run = (len(datestr_list)+dstep-1)/dstep
for idx in range(num_run):
s_idx = idx * dstep
e_idx = min((idx + 1) *dstep - 1, len(datestr_list)-1)
sdate = datetime.datetime.strptime(datestr_list[s_idx], "%Y-%m-%d").date()
edate = datetime.datetime.strptime(datestr_list[e_idx], "%Y-%m-%d").date()
df = load_hist_tick(db_table, inst, sdate, edate)
mdf = conv_ohlc_freq(df, '1Min')
mdf['min_id'] = ((mdf.index.hour + 6) % 24) * 100 + mdf.index.minute
mdf = backtest.cleanup_mindata(mdf, prod)
mdf.index.name = 'datetime'
mdf['instID'] = inst
mdf['exch'] = exch
mdf = mdf.reset_index()
mdf.set_index(['instID', 'exch', 'datetime'], inplace = True)
mdf.to_sql(name = out_table, flavor = 'mysql', con = cnx, if_exists='append')
cnx.commit()
print inst, sdate, edate, len(mdf)
cnx.close()
return
def prepare_data_env(self, inst, mid_day=True):
if self.instruments[inst].ptype == instrument.ProductType.Option:
return
if self.daily_data_days > 0 or mid_day:
#self.logger.debug('Updating historical daily data for %s' % self.scur_day.strftime('%Y-%m-%d'))
daily_start = workdays.workday(self.scur_day,
-self.daily_data_days, CHN_Holidays)
daily_end = self.scur_day
ddf = mysqlaccess.load_daily_data_to_df('fut_daily',
inst,
daily_start,
daily_end,
index_col=None)
if len(ddf) > 0:
self.instruments[inst].price = ddf['close'].iloc[-1]
self.instruments[inst].last_update = 0
self.instruments[inst].prev_close = ddf['close'].iloc[-1]
for fobj in self.day_data_func[inst]:
ts = fobj.sfunc(ddf)
if type(ts).__name__ == 'Series':
if ts.name in ddf.columns:
self.logger.warning(
'TimeSeries name %s is already in the columns for inst = %s'
% (ts.name, inst))
ddf[ts.name] = ts
elif type(ts).__name__ == 'DataFrame':
for col_name in ts.columns:
if col_name in ddf.columns:
self.logger.warning(
'TimeSeries name %s is already in the columns for inst = %s'
% (col_name, inst))
ddf[col_name] = ts[col_name]
self.day_data[inst] = data_handler.DynamicRecArray(dataframe=ddf)
if self.min_data_days > 0 or mid_day:
#self.logger.debug('Updating historical min data for %s' % self.scur_day.strftime('%Y-%m-%d'))
d_start = workdays.workday(self.scur_day, -self.min_data_days,
CHN_Holidays)
d_end = self.scur_day
min_start = int(self.instruments[inst].start_tick_id / 1000)
min_end = int(self.instruments[inst].last_tick_id / 1000) + 1
mdf = mysqlaccess.load_min_data_to_df('fut_min',
inst,
d_start,
d_end,
minid_start=min_start,
minid_end=min_end,
database='blueshale',
index_col=None)
mdf = backtest.cleanup_mindata(mdf,
self.instruments[inst].product,
index_col=None)
mdf['bar_id'] = self.conv_bar_id(mdf['min_id'], inst)
if len(mdf) > 0:
min_date = mdf['date'].iloc[-1]
if (len(self.day_data[inst]) == 0) or (
min_date > self.day_data[inst].data['date'][-1]):
ddf = data_handler.conv_ohlc_freq(mdf, 'd', index_col=None)
self.cur_day[inst]['open'] = float(ddf.open[-1])
self.cur_day[inst]['close'] = float(ddf.close[-1])
self.cur_day[inst]['high'] = float(ddf.high[-1])
self.cur_day[inst]['low'] = float(ddf.low[-1])
self.cur_day[inst]['volume'] = int(ddf.volume[-1])
self.cur_day[inst]['openInterest'] = int(
ddf.openInterest[-1])
self.cur_min[inst]['datetime'] = pd.datetime(
*mdf['datetime'].iloc[-1].timetuple()[0:-3])
self.cur_min[inst]['date'] = mdf['date'].iloc[-1]
self.cur_min[inst]['open'] = float(mdf['open'].iloc[-1])
self.cur_min[inst]['close'] = float(mdf['close'].iloc[-1])
self.cur_min[inst]['high'] = float(mdf['high'].iloc[-1])
self.cur_min[inst]['low'] = float(mdf['low'].iloc[-1])
self.cur_min[inst]['volume'] = self.cur_day[inst]['volume']
self.cur_min[inst]['openInterest'] = self.cur_day[inst][
'openInterest']
self.cur_min[inst]['min_id'] = int(mdf['min_id'].iloc[-1])
self.cur_min[inst]['bar_id'] = self.conv_bar_id(
self.cur_min[inst]['min_id'], inst)
self.instruments[inst].price = float(mdf['close'].iloc[-1])
self.instruments[inst].last_update = 0
#self.logger.debug('inst=%s tick data loaded for date=%s' % (inst, min_date))
if 1 not in self.min_data_func[inst]:
self.min_data[inst][1] = data_handler.DynamicRecArray(
dataframe=mdf)
for m in sorted(self.min_data_func[inst]):
if m != 1:
bar_func = lambda ts: self.conv_bar_id(ts, inst)
mdf_m = data_handler.conv_ohlc_freq(
mdf,
str(m) + 'min',
index_col=None,
bar_func=bar_func,
extra_cols=['bar_id'])
else:
mdf_m = mdf
for fobj in self.min_data_func[inst][m]:
ts = fobj.sfunc(mdf_m)
if type(ts).__name__ == 'Series':
if ts.name in mdf_m.columns:
self.logger.warning(
'TimeSeries name %s is already in the columns for inst = %s'
% (ts.name, inst))
mdf_m[ts.name] = ts
elif type(ts).__name__ == 'DataFrame':
for col_name in ts.columns:
if col_name in mdf_m.columns:
self.logger.warning(
'TimeSeries name %s is already in the columns for inst = %s'
% (col_name, inst))
mdf_m[col_name] = ts[col_name]
self.min_data[inst][m] = data_handler.DynamicRecArray(
dataframe=mdf_m)
def prepare_data_env(self, inst, mid_day=True):
if self.instruments[inst].ptype == instrument.ProductType.Option:
return
if self.daily_data_days > 0 or mid_day:
self.logger.debug('Updating historical daily data for %s' %
self.scur_day.strftime('%Y-%m-%d'))
daily_start = workdays.workday(self.scur_day,
-self.daily_data_days, CHN_Holidays)
daily_end = self.scur_day
self.day_data[inst] = mysqlaccess.load_daily_data_to_df(
'fut_daily', inst, daily_start, daily_end)
df = self.day_data[inst]
print inst
if len(df) > 0:
self.instruments[inst].price = df['close'][-1]
self.instruments[inst].last_update = 0
self.instruments[inst].prev_close = df['close'][-1]
for fobj in self.day_data_func[inst]:
ts = fobj.sfunc(df)
df[ts.name] = pd.Series(ts, index=df.index)
if self.min_data_days > 0 or mid_day:
self.logger.debug('Updating historical min data for %s' %
self.scur_day.strftime('%Y-%m-%d'))
d_start = workdays.workday(self.scur_day, -self.min_data_days,
CHN_Holidays)
d_end = self.scur_day
min_start = int(self.instruments[inst].start_tick_id / 1000)
min_end = int(self.instruments[inst].last_tick_id / 1000) + 1
mindata = mysqlaccess.load_min_data_to_df('fut_min',
inst,
d_start,
d_end,
minid_start=min_start,
minid_end=min_end,
database='blueshale')
mindata = backtest.cleanup_mindata(mindata,
self.instruments[inst].product)
self.min_data[inst][1] = mindata
if len(mindata) > 0:
min_date = mindata.index[-1].date()
if (len(self.day_data[inst].index)
== 0) or (min_date > self.day_data[inst].index[-1]):
ddf = data_handler.conv_ohlc_freq(mindata, 'd')
self.cur_day[inst]['open'] = float(ddf.open[-1])
self.cur_day[inst]['close'] = float(ddf.close[-1])
self.cur_day[inst]['high'] = float(ddf.high[-1])
self.cur_day[inst]['low'] = float(ddf.low[-1])
self.cur_day[inst]['volume'] = int(ddf.volume[-1])
self.cur_day[inst]['openInterest'] = int(
ddf.openInterest[-1])
self.cur_min[inst]['datetime'] = pd.datetime(
*mindata.index[-1].timetuple()[0:-3])
self.cur_min[inst]['open'] = float(mindata.ix[-1, 'open'])
self.cur_min[inst]['close'] = float(mindata.ix[-1,
'close'])
self.cur_min[inst]['high'] = float(mindata.ix[-1, 'high'])
self.cur_min[inst]['low'] = float(mindata.ix[-1, 'low'])
self.cur_min[inst]['volume'] = self.cur_day[inst]['volume']
self.cur_min[inst]['openInterest'] = self.cur_day[inst][
'openInterest']
self.cur_min[inst]['min_id'] = int(mindata.ix[-1,
'min_id'])
self.instruments[inst].price = float(mindata.ix[-1,
'close'])
self.instruments[inst].last_update = 0
self.logger.debug('inst=%s tick data loaded for date=%s' %
(inst, min_date))
for m in self.min_data_func[inst]:
if m != 1:
self.min_data[inst][m] = data_handler.conv_ohlc_freq(
self.min_data[inst][1],
str(m) + 'min')
df = self.min_data[inst][m]
for fobj in self.min_data_func[inst][m]:
ts = fobj.sfunc(df)
df[ts.name] = pd.Series(ts, index=df.index)
def hist_realized_vol_by_product(prodcode,
start_d,
end_d,
periods=12,
tenor='-1m',
writeDB=False):
cont_mth, exch = mysqlaccess.prod_main_cont_exch(prodcode)
contlist = contract_range(prodcode, exch, cont_mth, start_d, end_d)
exp_dates = [get_opt_expiry(cont, inst2contmth(cont)) for cont in contlist]
data = {
'is_dtime': True,
'data_column': 'close',
'data_freq': '30min',
'xs': [0.5, 0.25, 0.75],
'xs_names': ['atm', 'v25', 'v75'],
'xs_func': 'bs_delta_to_ratio',
'rehedge_period': 1,
'term_tenor': tenor,
'database': 'hist_data'
}
option_input = {
'otype': True,
'rd': 0.0,
'rf': 0.0,
'end_vol': 0.0,
'ref_vol': 0.5,
'pricer_func': 'bsopt.BSOpt',
'delta_func': 'bsopt.BSDelta',
'is_dtime': data['is_dtime'],
}
freq = data['data_freq']
for cont, expiry in zip(contlist, exp_dates):
expiry_d = expiry.date()
if expiry_d > end_d:
break
p_str = '-' + str(int(tenor[1:-1]) * periods) + tenor[-1]
d_start = day_shift(expiry_d, p_str)
if freq == 'd':
df = mysqlaccess.load_daily_data_to_df('fut_daily',
cont,
d_start,
expiry_d,
database=data['database'],
index_col=None)
else:
mdf = mysqlaccess.load_min_data_to_df('fut_min',
cont,
d_start,
expiry_d,
minid_start=300,
minid_end=2115,
database=data['database'],
index_col=None)
mdf = backtest.cleanup_mindata(mdf, prodcode, index_col=None)
mdf['bar_id'] = dh.bar_conv_func2(mdf['min_id'])
df = dh.conv_ohlc_freq(mdf,
freq,
bar_func=dh.bar_conv_func2,
extra_cols=['bar_id'],
index_col=None)
option_input['expiry'] = expiry
data['dataframe'] = df
vol_df = realized_termstruct(option_input, data)
print cont, expiry_d, vol_df