def unpack_trd(self,st):
rights = {1:"Call",0:"Put",-1:"Spot"}
out = [x.tolist() for x in unstruct(st).T]
out[0] = ["Open" if x==0 else "Close" for x in out[0]] #Leg
out[2] = ["Ask" if x==1 else "Bid" for x in out[2]] #Side
out[3] = [rights[x] for x in out[3]] #Right
return out
def join_search(self,cols): #Join trades with cols requested
ts = self.ts
ts = unstruct(ts[np.where(ts["SIZ"]!=0)[0]])
stk = ts[np.where(ts[:,0]==-1)[0],:]
opt = ts[np.where(ts[:,0]!=-1)[0],:]
opt = utils.sort_array(opt,(3,2,1,0))
lst = []
cols = ["IDX","STR","TNR"] + cols
for l in range(2):
I = opt[np.where(opt[:,1]==l)[0],2]
K = opt[np.where(opt[:,1]==l)[0],3]
st = self.search_raw(I,K,cols,l)
leg = np.zeros((st.shape[0],1))
leg[:] = l
st = np.hstack([leg,st]) #join leg
lst.append(st)
st = np.vstack(lst)
st = np.vstack([ #join right
np.hstack([np.ones((st.shape[0],1)),st]), #Calls
np.hstack([np.zeros((st.shape[0],1)),st]) #Puts
])
st = st[utils.intersect2d(st[:,(0,1,2,3)],opt[:,(0,1,2,3)]),:]
st = utils.sort_array(st,(3,2,1,0))
size = np.expand_dims(opt[:,-1],axis=0).T
opt = np.hstack([size,st]) #join size
return opt,stk #STK: RGT,LEG,IDX,STR,SIZ & OPT: SIZ,RGT,LEG,IDX,STR,TNR + cols requested
def search_raw(self,I,K,cols,leg=0): #Arrays have to be sorted for this!
st = self.rs[cols][np.where(
np.logical_and(
np.in1d(self.rs["IDX"][:,leg],I),
np.in1d(self.rs["STR"][:,leg],K)
))[0],leg]
return unstruct(st)
def surface(symbol, qdate, qtime=None, errors=False, n=False, loc=0, scale=0):
df = query.vols(["*"], ["Date", "Time", "Tenor", "Strike"], symbol, qdate,
qtime)
st = utils.pack(df)
del df
#----------------------------------------------------------------------------------
f = lambda LST: pricer.norm_weights(LST, loc, scale)
wgt = utils.apply(f, 1, st, ["Time", "Tenor"],
["LogStrike"]) #retrive weights vector
sv = pricer.SSVI(errors)
f = lambda LST, VAR, TNR, VOL: sv.calibrate(LST, VAR, TNR, VOL, wgt)
if errors == True:
prm = utils.apply(f,
6,
st, ["Time"],
["LogStrike", "TotAtmfVar", "Tenor", "SmtVol"],
diff=True,
fill=False)
eps = prm[-1]
prm = np.asarray(prm[:-1]).T
else:
prm = utils.apply(f,
5,
st, ["Time"],
["LogStrike", "TotAtmfVar", "Tenor", "SmtVol"],
fill=False)
#----------------------------------------------------------------------------------
reduced = [
"Time", "Group", "Tenor", "SpotMid", "Forward", "CumDivDays", "Div",
"ImpBor", "Rate", "CallH0", "CallH1", "CallKh", "PutH0", "PutH1",
"PutKh", "TotAtmfVar"
]
st = utils.unique_nosort(st[reduced])
prm = utils.restack(prm, st["TotAtmfVar"], tile=False)
rho = sv.correlation(st["TotAtmfVar"], prm[:, 0], prm[:, 1], prm[:, 2])
phi = sv.power_law(st["TotAtmfVar"], prm[:, 3], prm[:, 4])
atm, skw, krt = sv.raw2jw(st["Tenor"] / base, st["TotAtmfVar"], phi, rho)
arr = unstruct(st)
arr = np.column_stack([arr, atm, skw, krt, phi, rho])
df = utils.unpack(arr, qdate, raw=False, symbol=symbol)
if errors == True and n == False:
return df, eps
elif errors == False and n == True:
return df, n
elif errors == True and n == True:
return df, eps, n
else:
return df
def pop(self,leg=0):
rs,fs = self.rs,self.fs
if leg==0:
rs[fct_cols][:,0] = self.params.loc[rs["IDX"][:,0],fct_cols].to_records(index=False)
rs[fct_cols][:,1] = self.params.loc[rs["IDX"][:,0],fct_cols].to_records(index=False)
self.rs = rs
self.build_raw(0)
else:
rs[fct_cols[2:]][:,1] = struct( #Have to convert to unstructured array for broadcasting across cols of the structure
(unstruct(rs[fct_cols[2:]][:,0]) *
(1 + unstruct(fs[fct_cols[2:]][rs["IDX"][:,0]]))),
dtype = (rs[fct_cols[2:]][:,1].dtype)
)
rs[fct_cols[:2]][:,1] = struct(
(unstruct(rs[fct_cols[:2]][:,0]) -
unstruct(fs[fct_cols[:2]][rs["IDX"][:,0]])),
dtype = (rs[fct_cols[:2]][:,1].dtype)
)
self.rs = rs
self.build_raw(1)
def select(st, cols=()):
arr = np.hsplit(unstruct(st[cols]), len(cols))
arr = [np.squeeze(x) for x in arr]
return arr
def apply(f,
returns,
st,
cat_cols=(),
args=(),
asarray=False,
fill=False,
diff=False):
assert (
returns != 1 or diff != True
), "can't handle 1 return and different dimensions -> set diff to False"
if returns > 1:
deq = [deque() for i in range(returns)]
else:
deq = deque()
if asarray == False:
args = unstruct(st[args])
else:
args = np.vstack(args).T
items = [np.unique(st[i]) for i in cat_cols]
for x in product(*items):
cout.info(f"Categories: {x}")
cond = [st[j] == x[i] for j, i in zip(cat_cols, range(len(cat_cols)))]
idx = np.select([np.logical_and.reduce(cond)], [True], False)
fargs = args[idx, :].T
ret = list(f(*fargs))
assert isinstance(
ret, Iterable) != False, "arg func is not returning iterable"
assert diff == False or len(
ret
) == returns, f"arg func returns {len(ret)} vals, but expected {returns}"
if fill == True:
if returns > 1:
for i in range(returns):
ret[i] = np.resize(ret[i], fargs.shape[1])
else:
ret = np.resize(ret, fargs.shape[1])
if returns > 1:
for i in range(returns):
deq[i].append(ret[i])
else:
deq.append(ret)
if returns == 1:
ret = np.hstack(list(deq)).T
elif returns > 1 and diff == False and fill == False:
ret = np.vstack(list(deq)).T
elif returns > 1 and diff == False and fill == True:
ret = [list(flatten(d)) for d in deq]
ret = np.asarray(ret).T
elif returns > 1 and diff == True:
ret = np.asarray(deq).T
ret = [np.hstack(ret[:, i]) for i in range(returns)]
return ret
def main(qdate, symbols, qtime=None):
for symbol in symbols:
start_time = time.time()
weekday = 5 if symbol != "NDX" else 4
quotes = query.front_series(3, weekday, symbol, "USD", qdate, qtime)
if checks(qdate, symbol, quotes, weekday) == 0: continue
cout.info(f"{qdate} {symbol} - initing vol fetch")
st = utils.pack(quotes)
f = pricer.impdivsborrows
yld = utils.apply(f,
2,
st, ["Time", "Tenor"], [
"Moneyness", "SpotMid", "Strike", "Tenor",
"CallBid", "PutAsk", "Rate", "Div", "CumDivDays"
],
fill=True)
f = lambda STR, SPT, SPR: pricer.fit_spread(STR, SPT, SPR, "C")
chs = utils.apply(f,
3,
st, ["Time", "Tenor"],
["Strike", "SpotMid", "CallSpread"],
fill=True)
f = lambda STR, SPT, SPR: pricer.fit_spread(STR, SPT, SPR, "P")
phs = utils.apply(f,
3,
st, ["Time", "Tenor"],
["Strike", "SpotMid", "PutSpread"],
fill=True)
f = lambda TNR, SPT, FRT, CDD, DIV: pricer.forward(
TNR, SPT, FRT, yld[:, 0], CDD, DIV)
args = utils.select(st,
["Tenor", "SpotMid", "Rate", "CumDivDays", "Div"])
fwd = f(*args)
lst = np.log(st["Strike"] / fwd)
f = lambda CPX, SPT, STR, TNR, FRT: pricer.Vol(
"C", CPX, SPT, STR, TNR, FRT, yld[:, 1], chs[:, 0], chs[:, 1],
chs[:, 2]).root(0.01, 4)
args = utils.select(st,
["CallMid", "SpotMid", "Strike", "Tenor", "Rate"])
cvol, ceep = f(*args)
f = lambda PPX, SPT, STR, TNR, FRT: pricer.Vol(
"P", PPX, SPT, STR, TNR, FRT, yld[:, 1], phs[:, 0], phs[:, 1],
phs[:, 2]).root(0.01, 4)
args = utils.select(st,
["PutMid", "SpotMid", "Strike", "Tenor", "Rate"])
pvol, peep = f(*args)
ceep = utils.apply(pricer.interp,
1,
st, ["Time", "Tenor"], [lst, ceep],
asarray=True,
fill=True)
peep = utils.apply(pricer.interp,
1,
st, ["Time", "Tenor"], [lst, peep],
asarray=True,
fill=True)
rvol = [
(i + j) / 2 if np.isnan(i) == False and np.isnan(j) == False else j
if np.isnan(j) == False else i if np.isnan(i) == False else np.nan
for i, j in zip(cvol, pvol)
]
svol = utils.apply(pricer.interp,
1,
st, ["Time", "Tenor"], [lst, rvol],
asarray=True,
fill=True)
var = utils.apply(pricer.totatmfvar,
1,
st, ["Time", "Tenor"], [st["Tenor"], lst, rvol],
asarray=True,
fill=True)
if len(var[np.isnan(var)]) > 0:
var = utils.apply(pricer.totatmfvar,
1,
st, ["Time", "Tenor"], [st["Tenor"], lst, svol],
asarray=True,
fill=True)
ar = np.column_stack([
unstruct(st), yld, chs, phs, fwd, lst, cvol, ceep, pvol, peep,
rvol, svol, var
])
df = utils.unpack(ar)
query.post(df, "Vols", symbol, "append")
elapsed = round((time.time() - start_time) / 60, 3)
cout.info(f"{qdate} {symbol} - elapsed {elapsed} mins")
def surface(symbol,
qdate,
qtime=None,
errors=False,
post=True,
loc=0,
scale=None):
q = f"select * from dbo.{symbol} where Date = convert(datetime,'{qdate}',103) order by Time,Tenor,Strike"
df = query.get("Vols", q)
if qtime is not None: df = df[df["Time"] == qtime]
st = utils.pack(df)
del df
#----------------------------------------------------------------------------------
f = lambda LST: pricer.norm_weights(LST, loc, scale)
wgt = utils.apply(f, 1, st, ["Time", "Tenor"], ["LogStrike"])
sv = pricer.SSVI(errors)
f = lambda LST, VAR, TNR, VOL: sv.calibrate(LST, VAR, TNR, VOL, wgt)
if errors == True:
prm = utils.apply(f,
6,
st, ["Time"],
["LogStrike", "TotAtmfVar", "Tenor", "SmtVol"],
diff=True,
fill=False)
errors = prm[-1]
prm = np.asarray(prm[:-1]).T
else:
prm = utils.apply(f,
5,
st, ["Time"],
["LogStrike", "TotAtmfVar", "Tenor", "SmtVol"],
fill=False)
#----------------------------------------------------------------------------------
reduced = [
"Time", "Tenor", "SpotMid", "Forward", "CumDivDays", "Div", "ImpBor",
"Rate", "CallH0", "CallH1", "CallKh", "PutH0", "PutH1", "PutKh",
"TotAtmfVar"
]
st = utils.unique_nosort(st[reduced])
prm = utils.restack(prm, st["TotAtmfVar"], tile=False)
rho = sv.correlation(st["TotAtmfVar"], prm[:, 0], prm[:, 1], prm[:, 2])
phi = sv.power_law(st["TotAtmfVar"], prm[:, 3], prm[:, 4])
atm, skw, krt = sv.raw2jw(st["Tenor"] / base, st["TotAtmfVar"], phi, rho)
arr = unstruct(st)
arr = np.column_stack([arr, atm, skw, krt, phi, rho])
df = utils.unpack(arr, qdate, False, symbol)
if post == True:
query.post(df, "Params", "main", "replace")
return df
# symbol = "JPM"
# qdate = "16/10/2020"
# qtime = 2040
# errors = True
# df = surface(symbol,qdate,qtime=None,errors=False,post=False)
f = lambda STR,SPT,SPR: pricer.fit_spread(STR,SPT,SPR,"P")
phs = utils.apply(f,3,st,["Time","Tenor"],["Strike","SpotMid","PutSpread"],fill=True)
f = lambda TNR,SPT,FRT,CDD,DIV: pricer.forward(TNR,SPT,FRT,yld[:,0],CDD,DIV)
args = utils.select(st,["Tenor","SpotMid","Rate","CumDivDays","Div"])
fwd = f(*args)
lst = np.log(st["Strike"]/fwd)
f = lambda CPX,SPT,STR,TNR,FRT: pricer.Vol("C",CPX,SPT,STR,TNR,FRT,yld[:,1],chs[:,0],chs[:,1],chs[:,2]).root(0.01,4)
args = utils.select(st,["CallMid","SpotMid","Strike","Tenor","Rate"])
cvol,ceep = f(*args)
f = lambda PPX,SPT,STR,TNR,FRT: pricer.Vol("P",PPX,SPT,STR,TNR,FRT,yld[:,1],phs[:,0],phs[:,1],phs[:,2]).root(0.01,4)
args = utils.select(st,["PutMid","SpotMid","Strike","Tenor","Rate"])
pvol,peep = f(*args)
rvol = (cvol + pvol)/2
svol = utils.apply(pricer.interp,1,st,["Time","Tenor"],[lst,rvol],asarray=True,fill=True)
var = utils.apply(pricer.totatmfvar,1,st,["Time","Tenor"],[st["Tenor"],lst,svol],asarray=True,fill=True)
ar = np.column_stack([unstruct(st),yld,chs,phs,fwd,lst,cvol,ceep,pvol,peep,rvol,svol,var])
df = utils.unpack(ar,qdate)
query.post(df,"Vols",symbol,"append")
elapsed = round((time.time()-start_time)/60,3)
cout.info(f"{symbol} - elapsed {elapsed} mins")
break
cout.terminate()
except:
cout.error("Error")
