def TableUpdate1(data_timestamp, data, children):
#### ---- Populating table
if (data_timestamp != None) & (children != None):
if len(data) > 0:
data = pd.DataFrame(data)
data['Amount'] = data['Amount'].astype(float)
data['Price'] = data['Price'].astype(float)
data['Strike'] = data['Strike'].astype(float)
data['ImpVol'] = data['ImpVol'].astype(float)
data["Day"] = pd.to_datetime(data["Day"])
data["Maturity"] = pd.to_datetime(data["Maturity"])
data["Financial"] = data['Amount'] * data['Price']
data["Financial"] = data["Financial"].round(2)
spot = pd.DataFrame(json.loads(json.loads(str(children))['spot']))
spot["TRADE_DATE"] = pd.to_datetime(spot["TRADE_DATE"])
days = (data.apply(lambda row: businessDuration(
startdate=row['Day'], enddate=row['Maturity'], unit='day'),
axis=1)).values
# ImpyVola=[round(Func.ImpliedVola(spot[spot["Instrument"]!="BRSELICD=CBBR"].TRDPRC_1.values[0].astype(float),
# data["Strike"].values[j].astype(float),
# spot[spot["Instrument"]=="BRSELICD=CBBR"].TRDPRC_1.values[0].astype(float)/100,
# .70,
# data["Price"].values[j].astype(float),
# days[j],
# data["type"],a=-12.0, b=12.0, xtol=1e-8)*100,2) for j in range(len(data))]
ImpyVola = data['ImpVol'].values
GREEKS = Func.OpitionsPrice(
spot[spot["Instrument"] != "BRSELICD=CBBR"].TRDPRC_1.values[0].
astype(float), data["Strike"].values.astype(float),
spot[spot["Instrument"] ==
"BRSELICD=CBBR"].TRDPRC_1.values[0].astype(float) / 100,
np.array(ImpyVola) / 100, days)
data[['Valor', 'Delta', 'Gamma', 'Vega', 'Theta', 'Rho',
'Etas']] = pd.DataFrame([[0 for i in range(7)]],
columns=[
'Valor', 'Delta', 'Gamma',
'Vega', 'Theta', 'Rho', 'Etas'
],
index=data.index)
callGreek = GREEKS[list(GREEKS.columns[GREEKS.columns.str.contains(
'C', regex=True)])][data.type.str.contains('C',
regex=True).values]
putGreek = GREEKS[list(GREEKS.columns[GREEKS.columns.str.contains(
'P', regex=True)])][data.type.str.contains('P',
regex=True).values]
callGreek.columns = [
'Valor', 'Delta', 'Gamma', 'Vega', 'Theta', 'Rho', 'Etas'
]
putGreek.columns = [
'Valor', 'Delta', 'Gamma', 'Vega', 'Theta', 'Rho', 'Etas'
]
data.loc[
list(data.type.str.contains('C', regex=True).values),
['Valor', 'Delta', 'Gamma', 'Vega', 'Theta', 'Rho', 'Etas'
]] = round(callGreek, 3).values
data.loc[
list(data.type.str.contains('P', regex=True).values),
['Valor', 'Delta', 'Gamma', 'Vega', 'Theta', 'Rho', 'Etas'
]] = round(putGreek, 3).values
data["ImpVol"] = ImpyVola
data["Delta"] = data.Delta.values
data["Gamma"] = data.Gamma.values
data["Vega"] = data.Vega.values
data["Theta"] = data.Theta.values
data["Rho"] = data.Rho.values
return (data[[
'Financial', 'Delta', 'Gamma', 'Vega', 'Theta', 'Rho'
]].to_dict('records'))
else:
return []
else:
return []
def CalcNetChart(row_ids, data, children, value):
if (row_ids != None) & (data != None):
if (len(row_ids) > 0) & (len(data) > 0):
ek.set_app_key(value)
ek.set_timeout(60 * 5)
data = pd.DataFrame(data)
data = data[data.index.isin(row_ids)]
data['Amount'] = data['Amount'].astype(float)
data['Price'] = data['Price'].astype(float)
data['Strike'] = data['Strike'].astype(float)
data["Day"] = pd.to_datetime(data["Day"])
data["Maturity"] = pd.to_datetime(data["Maturity"])
data["Financial"] = data['Amount'] * data['Price']
data["Financial"] = data["Financial"].round(2)
spot = pd.DataFrame(json.loads(json.loads(str(children))['spot']))
spot['TRDPRC_1'] = spot['TRDPRC_1'].astype(float)
spot["TRADE_DATE"] = pd.to_datetime(spot["TRADE_DATE"])
ssp = spot[
spot["Instrument"] != "BRSELICD=CBBR"].TRDPRC_1.values[0]
days = (data.apply(lambda row: businessDuration(
startdate=row['Day'], enddate=row['Maturity'], unit='day'),
axis=1)).values
days = list(set(days))
vola, err = ek.get_data([
spot[
spot["Instrument"] != "BRSELICD=CBBR"].Instrument.values[0]
], [
'TR.Volatility5D', 'TR.Volatility10D', "TR.Volatility20D",
"TR.Volatility30D", "TR.Volatility60D", "TR.Volatility90D"
])
volaV = (sum((
vola.T[0].values[1:] *
(abs(np.array([5, 10, 20, 30, 60, 90]) - 15) + 1) /
(sum(abs(np.array([5, 10, 20, 30, 60, 90]) - 15) + 1))))) / 100
vola.columns = ['Instrument', 5, 10, 20, 30, 60, 90]
vola[[5, 10, 20, 30, 60, 90]] = vola[[5, 10, 20, 30, 60, 90]] / 100
days = list(np.unique(days))
dists = []
vollist = []
for d in days:
y_interp = scipy.interpolate.interp1d(
vola.columns[1:].astype(float),
vola.T[0].values[1:].astype(float))
volaV = y_interp(d)
Distrib = 1 - np.random.normal(0,
(volaV / (252**0.5) * (d**0.5)),
size=5000)
Distrib = Distrib * ssp
vollist.append(volaV)
dists.append(Distrib)
Price = dists[pd.DataFrame(dists).max(1).argmax()]
Price.sort()
net = []
for k in range(len(data)):
if data['Amount'].iloc[k] >= 0:
if "C" in data['type'].iloc[k]:
Rt = -data['Strike'].iloc[k] - data['Price'].iloc[
k] + Price
Rt[Rt <=
-data['Price'].iloc[k]] = -data['Price'].iloc[k]
Rt = Rt * abs(data['Amount'].iloc[k])
net.append(list(Rt))
else:
Rt = data['Strike'].iloc[k] - data['Price'].iloc[
k] - Price
Rt[Rt <=
-data['Price'].iloc[k]] = -data['Price'].iloc[k]
Rt = Rt * abs(data['Amount'].iloc[k])
net.append(list(Rt))
else:
if "C" in data['type'].iloc[k]:
Rt = data['Strike'].iloc[k] + data['Price'].iloc[
k] - Price
Rt[Rt >= data['Price'].iloc[k]] = data['Price'].iloc[k]
Rt = Rt * abs(data['Amount'].iloc[k])
net.append(list(Rt))
else:
Rt = -data['Strike'].iloc[k] + data['Price'].iloc[
k] + Price
Rt[Rt >= data['Price'].iloc[k]] = data['Price'].iloc[k]
Rt = Rt * abs(data['Amount'].iloc[k])
net.append(list(Rt))
pay = pd.DataFrame(net)
pay.columns = Price
pays = pay
# print(pays.values)
pay = pay.sum()
tempTT = []
for j in range(len(set(days))):
tempT = []
for s in range(len(data["Strike"])):
temp = Func.OpitionsPrice(
Price, data["Strike"].values.astype(float)[s],
spot[spot["Instrument"] == "BRSELICD=CBBR"].TRDPRC_1.
values[0].astype(float) / 100,
np.array(data['ImpVol'].values[j]) / 100, days[j])
if "C" in data["type"].values[s]:
temp = list(
(temp.ValorC.values - data['Price'].values[s]) *
data['Amount'].values[s])
else:
temp = list(
(temp.ValorP.values - data['Price'].values[s]) *
data['Amount'].values[s])
tempT.append(temp)
tempTT.append(tempT)
fig = go.FigureWidget(
make_subplots(shared_xaxes=True,
specs=[[{
"secondary_y": True
}]],
print_grid=False))
trace3 = go.Scatter(name="0 line",
x=pay.index,
y=np.array([0 for i in Distrib]),
xaxis='x1',
yaxis='y2',
line=dict(color='black', width=2, dash='dash'))
fig.add_trace(trace3, secondary_y=False)
trace1 = go.Scatter(name="Payoff",
x=pay.index,
y=pay.values,
xaxis='x1',
yaxis='y2',
mode='lines',
fill='tozeroy')
fig.add_trace(trace1, secondary_y=False)
for i in range(len(tempTT)):
trace5 = go.Scatter(name="Price - " + str(days[i]) + ' Days',
x=pay.index,
y=pd.DataFrame(tempTT[i]).sum().values,
xaxis='x1',
yaxis='y2',
mode='lines')
fig.add_trace(trace5, secondary_y=False)
for lin, i in zip(pays.values, pays.index):
trace4 = go.Scatter(name=data.Instrument.values[i],
x=pay.index,
y=lin,
xaxis='x1',
yaxis='y2',
line=dict(width=2, dash='dash'))
fig.add_trace(trace4, secondary_y=False)
for i, j in zip(days, dists):
trace2 = ff.create_distplot([j],
[str(i) + " Days - Probabilidade"],
bin_size=.5,
curve_type='normal',
show_hist=False,
show_rug=False)
fig.add_trace(trace2['data'][0], secondary_y=True)
prob = round(sum(pay.values > 0) / len(Distrib) * 100, 2)
return [fig, html.H6("Profit probability: " + str(prob) + "%")]
else:
fig = go.FigureWidget(
make_subplots(shared_xaxes=True,
specs=[[{
"secondary_y": True
}]],
print_grid=False))
trace3 = go.Scatter(name="0 line",
x=[0],
y=[0],
xaxis='x1',
yaxis='y2',
line=dict(color='black', width=2, dash='dash'))
fig.add_trace(trace3, secondary_y=False)
return [fig, html.H6(str(0) + "%")]
else:
fig = go.FigureWidget(
make_subplots(shared_xaxes=True,
specs=[[{
"secondary_y": True
}]],
print_grid=False))
trace3 = go.Scatter(name="0 line",
x=[0],
y=[0],
xaxis='x1',
yaxis='y2',
line=dict(color='black', width=2, dash='dash'))
fig.add_trace(trace3, secondary_y=False)
return [fig, html.H6(str(0) + "%")]
days=(data.apply(lambda row: businessDuration(startdate=row['TRADE_DATE'],enddate=row['EXPIR_DATE'],unit='day'), axis=1)).values
j=0
ImpyVola=[round(Func.ImpliedVola(spot[spot["Instrument"]!="BRSELICD=CBBR"].TRDPRC_1.values[0].astype(float),
data["STRIKE_PRC"].values[j].astype(float),
spot[spot["Instrument"]=="BRSELICD=CBBR"].TRDPRC_1.values[0].astype(float)/100,
.70,
data["TRDPRC_1"].values[j].astype(float),
days[j],
# (((data["EXPIR_DATE"].values[j])-(data["TRADE_DATE"].values[j])).astype('timedelta64[D]').astype(int)),
data["PUTCALLIND"],a=-12.0, b=12.0, xtol=1e-8)*100,2) for j in range(len(data))]
GREEKS=Func.OpitionsPrice(spot[spot["Instrument"]!="BRSELICD=CBBR"].TRDPRC_1.values[0].astype(float),
data["STRIKE_PRC"].values.astype(float),
spot[spot["Instrument"]=="BRSELICD=CBBR"].TRDPRC_1.values[0].astype(float)/100,
np.array(ImpyVola)/100,
days)
data[['Valor','Delta','Gamma','Vega','Theta','Rho','Etas']]=pd.DataFrame([[0 for i in range(7)]], columns=['Valor','Delta','Gamma','Vega','Theta','Rho','Etas'],index=data.index)
callGreek=GREEKS[list(GREEKS.columns[GREEKS.columns.str.contains('C', regex=True)])][data.PUTCALLIND.str.contains('C', regex=True).values]
putGreek=GREEKS[list(GREEKS.columns[GREEKS.columns.str.contains('P', regex=True)])][data.PUTCALLIND.str.contains('P', regex=True).values]
callGreek.columns=['Valor','Delta','Gamma','Vega','Theta','Rho','Etas']
putGreek.columns=['Valor','Delta','Gamma','Vega','Theta','Rho','Etas']
data.loc[list(data.PUTCALLIND.str.contains('C', regex=True).values),['Valor','Delta','Gamma','Vega','Theta','Rho','Etas']]=callGreek.values
data.loc[list(data.PUTCALLIND.str.contains('P', regex=True).values),['Valor','Delta','Gamma','Vega','Theta','Rho','Etas']]=putGreek.values
