def load_data(cls):
# Read data from file
logging.info(f'Reading data from: {cls.lc_file.name}')
logging.info(f'Working in directory: {cls.lc_file.parent.resolve()}')
try:
time, flux, flux_err = np.loadtxt(cls.lc_file, unpack=True)
except ValueError:
try:
time, flux = np.loadtxt(cls.lc_file, unpack=True)
except ValueError:
logging.error(
f"Error: Couldn't load data from the lc_file: {cls.lc_file}"
)
sys.exit(1)
if cls.settings.include_errors:
if flux_err is None:
logging.error(
f"Error: Need a flux_err column when include_errors is True"
)
sys.exit(1)
# Convert time to days and flux and err to ppm (if needed). Make copy to separate memory of arrays for C
cls.time = time.copy() # keep days
# cls.time = time.copy() / (24.*3600.) # seconds to days
# cls.flux = flux.copy() # keep ppm
cls.flux = (flux.copy() - 1.0) * 1e6 # frac to ppm
if cls.settings.include_errors:
# cls.flux_err = flux_err.copy() # keep ppm
cls.flux_err = flux_err.copy() * 1e6 # frac to ppm
def outlet(self, unit, own_data=True):
c_double_p = ctypes.POINTER(ctypes.c_double)
time_ptr = c_double_p()
data_ptr = c_double_p()
n_time = ctypes.c_int()
n_ports = ctypes.c_int()
n_comp = ctypes.c_int()
result = self.__api.getSolutionOutlet(self.__driver, unit,
ctypes.byref(time_ptr),
ctypes.byref(data_ptr),
ctypes.byref(n_time),
ctypes.byref(n_ports),
ctypes.byref(n_comp))
n_time = n_time.value
n_ports = n_ports.value
n_comp = n_comp.value
data = numpy.ctypeslib.as_array(data_ptr,
shape=(n_time, n_ports, n_comp))
time = numpy.ctypeslib.as_array(time_ptr, shape=(n_time, ))
if own_data:
return (time.copy(), data.copy())
else:
return (time, data)
def check_day_conflict(time: list, start: int, end: int) -> bool:
if end < start:
return False
if len(time) == 0:
return True
time = time.copy()
time.append((start, 0))
time.append((end, 1))
time.sort(key=lambda t: t[1], reverse=True)
time.sort(key=lambda t: t[0])
cur = 0
for time, op in time:
if op == 0:
# start
cur += 1
else:
#op == 1 end
cur -= 1
if not (cur == 0 or cur == 1):
return False
return cur == 0
def __init__(self, time, flux, fmax=40, fmin=0.000001, model='sin'):
models = ['sin', 'pda', 'sin_analytic']
if model not in models:
sys.exit(
f'{model} not an available option for model. Try one in {models}'
)
self.time = time.copy()
self.flux = flux.copy()
self.lc = lk.LightCurve(self.time, self.flux)
self.resid = flux.copy()
self.resid_lc = lk.LightCurve(self.time, self.resid)
self.iterations = []
self.iters = 0
self.fmax = fmax
self.fmin = np.min([fmin, 0.00001])
self.result = []
self.nofp = 1
self.var = np.var(flux)
pdg = lk.LightCurve(time,
flux).to_periodogram(maximum_frequency=fmax,
minimum_frequency=self.fmin)
pdg_func = interp.interp1d(pdg.frequency,
pdg.power,
fill_value='extrapolate')
self.power_val = integ.quad(pdg_func,
self.fmin,
self.fmax,
limit=5000,
epsabs=1.49e-05,
epsrel=1.49e-03)[0]
self.n = len(time)
self.aic_val = self.n * np.log(
np.sum(self.flux**2) / self.n) + self.n + 2 * self.nofp
self.bic_val = self.n * np.log(
np.sum(self.flux**2) / self.n) + self.nofp * np.log(self.n)
self.stop = 0
self.model = model
if self.model == "pda":
self.have_primary = False
self.have_secondary = True
p_end = nframe / 2
for res_ind, vel_res in enumerate(vel):
for loop_ind in range(p_start, p_end):
vc[res_ind][frame_ind - 1 - loop_ind].append(
np.mean(
np.sum(vel[res_ind] * vel_ref[loop_ind][res_ind],
axis=1)))
base[res_ind][frame_ind - 1 - loop_ind].append(
np.mean(np.sum(vel_ref[loop_ind][res_ind]**2, axis=1)))
if sub_ind % 10 == 9:
print 'Reading chunk', chunk_index + 1, 'and frame', chunk_index * chunk_size + sub_ind + 1
# if chunk_index == 0:
# break
time = np.arange(nframe - nframe / 2 + 1) * dt
data = time.copy()
data_raw = time.copy()
pt_start = len(time) / 2
nvcf = []
vcf = []
print 'frames read:', chunk_index * chunk_size + sub_ind + 1
fig = plt.figure()
for i, item in enumerate(vc):
nvcf.append([])
vcf.append([])
for j in range(len(item)):
nvcf[-1].append(np.mean(item[j]) / np.mean(base[i][j]))
vcf[-1].append(np.mean(item[j]))
nvcf[i] = np.array(nvcf[i])
vcf[i] = np.array(vcf[i]) * 1e6 / dt**2 ### units are m^2/s^2
def __init__(self, struct, pid, tflux, cflux, ratio, time, phottype, sleep,
tx, ty, cx, cy, r, br1, br2, naxis1, naxis2, sigdet, contpix,
driftlimit):
"""Default constructor."""
maxcolumn = 7
self.struct = struct
self.infile = struct._HDUList__file.name
self.name = self.struct[0].header['OBJECT']
self.pid = pid
self.dtime = time.copy()
self.tflux = tflux
self.cflux = cflux
self.ratio = ratio
self.min_xlim = 10
self.radius = r['comparison']
self.r = r
self.br1 = br1
self.br2 = br2
self.tx = tx
self.ty = ty
self.cx = cx
self.cy = cy
self.phottype = phottype
self.naxis1 = naxis1
self.naxis2 = naxis2
self.sigdet = sigdet
self.contpix = contpix
self.driftlimit = driftlimit
self.niter = 5
self.sigback = 5
self.fft = False
self.stopplay = False
self.sleep = sleep
self.zbox = []
self.npoint = 4
self.id = 0
self.nframes = len(self.struct)
self.header = self.struct[int(self.pid[self.id])].header
self.goodframes = self.dtime * 0 + 1
if self.phottype == 'circular': self.npoint = 24
# Setup widget
QtGui.QMainWindow.__init__(self)
# Set main widget
self.main = QtGui.QWidget(self)
# Set window title
self.setWindowTitle("SlotView %s" % self.infile)
#set up the different pages
self.slotPage = QtGui.QWidget()
#set up the differen panels
self.set_optionpanel()
self.set_imagepanel()
self.set_controlpanel()
self.set_plotpanel()
self.set_infopanel()
# Set up the layout
slotLayout = QtGui.QVBoxLayout(self.slotPage)
slotLayout.addWidget(self.plotpanel)
slotLayout.addWidget(self.optipanel)
slotLayout.addWidget(self.imdisplay)
slotLayout.addWidget(self.contpanel)
slotLayout.addWidget(self.infopanel)
#create the tabs
#self.tabWidget=QtGui.QTabWidget()
#self.tabWidget.addTab(self.slotPage, 'Slot')
#layout the widgets
mainLayout = QtGui.QVBoxLayout(self.main)
mainLayout.addWidget(self.slotPage)
#mainLayout.addWidget(self.tabWidget)
#self.setLayout(mainLayout)
# Set focus to main widget
self.main.setFocus()
# Set the main widget as the central widget
self.setCentralWidget(self.main)
# Destroy widget on close
self.setAttribute(QtCore.Qt.WA_DeleteOnClose)
# Close when config dialog is closed
self.connect(self.main, QtCore.SIGNAL('keyPressEvent'),
self.keyPressEvent)
for (x, y, w, h) in f:
cv2.rectangle(i, (x, y), (x + w, y + h), (0, 0, 255), 3)
X.append(x)
Y.append(y)
W.append(w)
H.append(h)
if (len(X) == 2):
t = i[Y[0]:Y[0] + min(H), X[0]:X[0] + min(W), :].copy()
i[Y[0]:Y[0] + min(H),
X[0]:X[0] + min(W), :] = i[Y[1]:Y[1] + min(H),
X[1]:X[1] + min(W), :].copy()
i[Y[1]:Y[1] + min(H), X[1]:X[1] + min(W), :] = t.copy()
print(r)
cv2.imshow('img', i)
k = cv2.waitKey(1)
if (k == ord('q')):
cv2.destroyAllWindows()
v.release()
break
## l=cv2.add(i,b)
## j=cv2.cvtColor(i,cv2.COLOR_BGR2GRAY)
## r,g=cv2.threshold(j,127,255,0)
## cv2.imshow('black',d)
## cv2.imshow('digital',g)
def __init__(self, time, flux, mode):
self.time = time.copy()
self.flux = flux.copy()
self.lc = lk.LightCurve(self.time, self.flux)
self.mode = mode
def __init__(self, struct, pid, tflux, cflux, ratio, time, phottype, sleep,
tx, ty, cx, cy, r, br1, br2, naxis1, naxis2, sigdet, contpix, driftlimit):
"""Default constructor."""
maxcolumn=7
self.struct = struct
self.infile=struct._HDUList__file.name
self.name=self.struct[0].header['OBJECT']
self.pid=pid
self.dtime=time.copy()
self.tflux=tflux
self.cflux=cflux
self.ratio=ratio
self.min_xlim=10
self.radius=r['comparison']
self.r=r
self.br1=br1
self.br2=br2
self.tx=tx
self.ty=ty
self.cx=cx
self.cy=cy
self.phottype=phottype
self.naxis1=naxis1
self.naxis2=naxis2
self.sigdet=sigdet
self.contpix=contpix
self.driftlimit=driftlimit
self.niter=5
self.sigback=5
self.fft=False
self.stopplay=False
self.sleep=sleep
self.zbox=[]
self.npoint=4
self.id=0
self.nframes=len(self.struct)
self.header=self.struct[int(self.pid[self.id])].header
self.goodframes=self.dtime*0+1
if self.phottype=='circular': self.npoint=24
# Setup widget
QtGui.QMainWindow.__init__(self)
# Set main widget
self.main = QtGui.QWidget(self)
# Set window title
self.setWindowTitle("SlotView %s" % self.infile)
#set up the different pages
self.slotPage=QtGui.QWidget()
#set up the differen panels
self.set_optionpanel()
self.set_imagepanel()
self.set_controlpanel()
self.set_plotpanel()
self.set_infopanel()
# Set up the layout
slotLayout = QtGui.QVBoxLayout(self.slotPage)
slotLayout.addWidget(self.plotpanel)
slotLayout.addWidget(self.optipanel)
slotLayout.addWidget(self.imdisplay)
slotLayout.addWidget(self.contpanel)
slotLayout.addWidget(self.infopanel)
#create the tabs
#self.tabWidget=QtGui.QTabWidget()
#self.tabWidget.addTab(self.slotPage, 'Slot')
#layout the widgets
mainLayout = QtGui.QVBoxLayout(self.main)
mainLayout.addWidget(self.slotPage)
#mainLayout.addWidget(self.tabWidget)
#self.setLayout(mainLayout)
# Set focus to main widget
self.main.setFocus()
# Set the main widget as the central widget
self.setCentralWidget(self.main)
# Destroy widget on close
self.setAttribute(QtCore.Qt.WA_DeleteOnClose)
# Close when config dialog is closed
self.connect(self.main, QtCore.SIGNAL('keyPressEvent'), self.keyPressEvent)
coverage = np.ma.getdata(tas[0:12, :, :]) * 0
ref_period = np.where((time > 19860000) & (time < 20160000))[0]
time_in_year = [t - int(t / 10000.) * 10000 for t in time[ref_period]]
month = [int(t / 100.) - 1 for t in time_in_year]
for i, t in zip(range(len(ref_period)), ref_period):
coverage[month[i], :, :] += np.ma.getmask(tas[t, :, :])
coverage[coverage > 10] = np.nan
coverage = np.ma.masked_invalid(coverage)
coverage_ext = tas[0:1008, :, :].copy()
for i, m in zip(range(1008), range(12) * 84):
coverage_ext[i, :, :] = coverage[m, :, :]
tas_ext = np.concatenate((tas, coverage_ext), axis=0)
time_ext = time.copy()
for year in range(2017, 2101):
extension = time[0:12] - 18500000 + year * 10000
time_ext = np.concatenate((time_ext, extension))
nc_in = Dataset('CRU.nc', "r")
# copy netcdf and write zoomed file
out_file = 'CRU_extended.nc'
os.system("rm " + out_file)
nc_out = Dataset(out_file, "w")
for dname, the_dim in nc_in.dimensions.iteritems():
nc_out.createDimension(dname,
len(the_dim) if not the_dim.isunlimited() else None)
# Copy variables
def __init__(self, struct, pid, tflux, cflux, ratio, time, phottype, sleep, vig_lo, vig_hi, \
tx, ty, cx, cy, r, br1, br2, naxis1, naxis2, clobber, logfile, verbose):
"""As the data is measured, plots the target and companion, the drift, both light curves and the ratio
returns status
"""
#set up the variables
status=0
maxcolumn=7
self.struct = struct
self.infile=struct._HDUList__file.name
self.pid=pid
self.dtime=time.copy()
self.tflux=tflux
self.cflux=cflux
self.ratio=ratio
self.min_xlim=10
self.radius=r['comparison']
self.r=r
self.br1=br1
self.br2=br2
self.tx=tx
self.ty=ty
self.cx=cx
self.cy=cy
self.phottype=phottype
self.naxis1=naxis1
self.naxis2=naxis2
self.logfile=logfile
self.clobber=clobber
self.verbose=verbose
self.fft=False
self.stopplay=False
self.sleep=sleep
self.zbox=[]
self.newphot=0
self.npoint=4
if self.phottype=='circular':
self.npoint=24
if status==0:
self.id=0
self.nframes=len(self.struct)
self.header=self.struct[int(self.pid[self.id])].header
self.goodframes=self.dtime*0+1
# Setup widget
QtGui.QMainWindow.__init__(self)
# Set main widget
self.main = QtGui.QWidget(self)
# Set window title
self.setWindowTitle("Slotview: "+self.infile)
#self.root.bind("<Destroy>", self.destroy)
#self.root.bind("D", self.deleteframe)
#self.root.bind("u", self.undeleteframe)
#self.root.bind("n", self.call_playone)
#self.root.bind("b", self.call_revone)
#self.root.bind("?", self.help)
#self.root.bind("q", self.destroy)
#self.root.bind("<Button-1>", self.callback)
#set up the variables for which graphs to plot
#self.ratiovar=Tk.IntVar(master=self.root, value=1)
#self.star1var=Tk.IntVar(master=self.root, value=0)
#self.star2var=Tk.IntVar(master=self.root, value=0)
#self.slotfig=plt.figure(figsize=(8,1.5),dpi=72)
#plot the data
#self.plotdataarray()
#self.lcfig=plt.figure(figsize=(8,5),dpi=72)
#plot the light curve
#self.lcx1=self.dtime.min()
#self.lcx2=self.dtime.max()
#self.plotlightcurve()
inrow=4
lcrow=0
pcrow=1
darow=2
cprow=3
qurow=5
#add light curve plot
#self.lccanvas = FigureCanvasTkAgg(self.lcfig, master=self.root)
#self.lccanvas.show()
#self.lccanvas.get_tk_widget().grid(row = lcrow, column = 0, columnspan = maxcolumn, sticky = 'news')
#self.lccanvas.mpl_connect('button_press_event',self.lcpickstar)
#self.lccanvas.mpl_connect('motion_notify_event',self.lcdrawbox)
#self.lccanvas.mpl_connect('button_release_event',self.lczoom)
#add data array plot
#self.canvas = FigureCanvasTkAgg(self.slotfig, master=self.root)
#self.canvas.show()
#self.canvas.blit()
#self.canvas.get_tk_widget().grid(row = darow, column = 0, columnspan = maxcolumn, sticky = 'news')
#self.canvas.mpl_connect('key_press_event',self.newphoto)
#add the control widget
#self.cpFrame = Tk.Frame(master=self.root)
#self.cpFrame.grid(row=cprow, column=0, columnspan=maxcolumn, sticky='ew')
#self.frevbutton = Tk.Button(master=self.cpFrame, text='< <', width=5, command=self.freverse)
#self.frevbutton.grid(row=0, column=0, sticky='ew')
#self.revbutton = Tk.Button(master=self.cpFrame, text='<',width=5, command=self.reverse)
#self.revbutton.grid(row=0, column=1, sticky='ew')
#self.rev1button = Tk.Button(master=self.cpFrame, text='-',width=5, command=self.revone)
#self.rev1button.grid(row=0, column=2, sticky='ew')
#self.play1button = Tk.Button(master=self.cpFrame, text='+',width=5, command=self.playone)
#self.play1button.grid(row=0, column=4, sticky='ew')
#self.playbutton = Tk.Button(master=self.cpFrame, text='>',width=5, command=self.play)
#self.playbutton.grid(row=0, column=5, sticky='ew')
#self.fplaybutton = Tk.Button(master=self.cpFrame, text='> >',width=5, command=self.fplay)
#self.fplaybutton.grid(row=0, column=6, sticky='ew')
#self.stopbutton = Tk.Button(master=self.cpFrame, text='Stop',width=5, command=self.stop)
#self.stopbutton.grid(row=0, column=3, sticky='ew')
#add the information panel
#self.idtext= Tk.StringVar(master=self.root )
#self.imgtext= Tk.StringVar(master=self.root )
#self.timetext= Tk.StringVar(master=self.root )
#self.idLabel = Tk.Label(master=self.root, fg='#000000',textvariable=self.idtext, relief='solid')
#self.idLabel.grid(row=inrow, column=0, sticky='ew')
#self.imgLabel = Tk.Label(master=self.root, textvariable=self.imgtext, relief='solid')
#self.imgLabel.grid(row=inrow, column=1, columnspan=3, sticky='ew')
#self.timeLabel = Tk.Label(master=self.root, textvariable=self.timetext, relief='solid')
#self.timeLabel.grid(row=inrow, column=4, columnspan=3, sticky='ew')
#self.setinfolabels()
#add the plot control panel
#self.ratiobutton=Tk.Checkbutton(master=self.root, text='Flux Ratio', variable=self.ratiovar, \
# command=self.calllccheck)
#self.ratiobutton.grid(row=pcrow, column=0, sticky='ew')
#self.star1button=Tk.Checkbutton(master=self.root, text='Star1 Flux', variable=self.star1var, \
# command=self.calllccheck)
#self.star1button.grid(row=pcrow, column=1, sticky='ew')
#self.star2button=Tk.Checkbutton(master=self.root, text='Star2 Flux', variable=self.star2var, \
# command=self.calllccheck)
#self.star2button.grid(row=pcrow, column=2, sticky='ew')
#self.resetbutton = Tk.Button(master=self.root, text='Reset', command=self.callreset)
#self.resetbutton.grid(row=pcrow, column=6, sticky='ew')
#self.savebutton = Tk.Button(master=self.root, text='save', command=self.callsave)
#self.savebutton.grid(row=pcrow, column=5, sticky='ew')
#add the quit button
#self.quFrame = Tk.Frame(master=self.root)
#self.quFrame.grid(row=qurow, column=0, columnspan=maxcolumn, sticky='ew')
#self.exitbutton = Tk.Button(master=self.quFrame, text='Quit', command=self.exit)
#self.exitbutton.grid(row=0, column=3, sticky='ew')
#create the tabs
self.tabWidget=QtGui.QTabWidget()
#layout the widgets
mainLayout = QtGui.QVBoxLayout(self.main)
mainLayout.addWidget(self.tabWidget)
# Set the main widget as the central widget
self.setCentralWidget(self.main)
# Destroy widget on close
self.setAttribute(QtCore.Qt.WA_DeleteOnClose)
return
def main():
st.sidebar.header("Explorador de ativos")
n_sprites = st.sidebar.radio("Escolha uma opção",
options=[
"Análise técnica e fundamentalista",
"Comparação de ativos",
"Descobrir novos ativos"
],
index=0)
st.sidebar.markdown(
'É preciso ter paciência e disciplina para se manter firme em suas convicções quando o mercado insiste que você está errado.!'
)
st.sidebar.markdown('Benjamin Graham')
st.sidebar.markdown('Email para contato: [email protected]')
st.sidebar.markdown('Portfólio: https://github.com/lucasvascrocha')
# ------------------------------ INÍCIO ANÁLISE TÉCNICA E FUNDAMENTALISTA ----------------------------
if n_sprites == "Análise técnica e fundamentalista":
st.image('https://media.giphy.com/media/rM0wxzvwsv5g4/giphy.gif',
width=400)
#image = Image.open('imagens/logo.jpg')
#st.image(image, use_column_width=True)
st.title('Análise Técnica e fundamentalista')
st.subheader('Escolha o ativo que deseja analisar e pressione enter')
nome_do_ativo = st.text_input('Nome do ativo')
st.write(
'Este explorador funciona melhor para ações, porém também suporta alguns fundos imobiliários'
)
st.write(
'Os parâmetros utilizados em grande maioria foram seguindo as teorias de Benjamin Graham'
)
if nome_do_ativo != "":
nome_do_ativo = str(nome_do_ativo + '.SA')
st.subheader('Analisando os dados')
df = Ticker(nome_do_ativo, country='Brazil')
time = df.history(period='max')
st.dataframe(time.tail())
# ------------------------------ RESUMO ----------------------------
resumo = pd.DataFrame(df.summary_detail)
resumo = resumo.transpose()
if len(nome_do_ativo) == 8:
fundamentus = get_specific_data(nome_do_ativo[:5])
fundamentus = pd.DataFrame([fundamentus])
pfizer = yf.Ticker(nome_do_ativo)
info = pfizer.info
st.title('PERFIL DA EMPRESA')
st.subheader(info['longName'])
st.markdown('** Setor **: ' + info['sector'])
st.markdown('** Atividade **: ' + info['industry'])
st.markdown('** Website **: ' + info['website'])
try:
fundInfo = {
'Dividend Yield (%) -12 meses':
round(info['dividendYield'] * 100, 2),
'P/L':
fundamentus['P/L'][0],
'P/VP':
fundamentus['P/VP'][0],
'Próximo pagamento de dividendo:':
(pfizer.calendar.transpose()
['Earnings Date'].dt.strftime('%d/%m/%Y')[0])
}
fundDF = pd.DataFrame.from_dict(fundInfo, orient='index')
fundDF = fundDF.rename(columns={0: 'Valores'})
st.subheader('Informações fundamentalistas')
st.table(fundDF)
except:
exit
else:
st.write(
'---------------------------------------------------------------------'
)
st.dataframe(resumo)
pfizer = yf.Ticker(nome_do_ativo)
info = pfizer.info
st.title('Company Profile')
st.subheader(info['longName'])
try:
st.markdown('** Sector **: ' + info['sector'])
st.markdown('** Industry **: ' + info['industry'])
st.markdown('** Website **: ' + info['website'])
except:
exit
# ------------------------------ GRÁFICOS DE RENDIMENTO ----------------------------
if len(nome_do_ativo) == 8:
import datetime
fundamentalist = df.income_statement()
fundamentalist['data'] = fundamentalist[
'asOfDate'].dt.strftime('%d/%m/%Y')
fundamentalist = fundamentalist.drop_duplicates('asOfDate')
fundamentalist = fundamentalist.loc[
fundamentalist['periodType'] == '12M']
#volatilidade
TRADING_DAYS = 360
returns = np.log(time['close'] / time['close'].shift(1))
returns.fillna(0, inplace=True)
volatility = returns.rolling(
window=TRADING_DAYS).std() * np.sqrt(TRADING_DAYS)
vol = pd.DataFrame(volatility.iloc[-360:]).reset_index()
#sharpe ratio
sharpe_ratio = returns.mean() / volatility
sharpe = pd.DataFrame(sharpe_ratio.iloc[-360:]).reset_index()
div = time.reset_index()
div['year'] = pd.to_datetime(div['date']).dt.strftime('%Y')
div_group = div.groupby('year').agg({
'close': 'mean',
'dividends': 'sum'
})
div_group['dividendo(%)'] = round(
(div_group['dividends'] * 100) / div_group['close'], 4)
from plotly.subplots import make_subplots
fig = make_subplots(
rows=3,
cols=2,
specs=[[{
"type": "bar"
}, {
"type": "bar"
}], [{
"type": "bar"
}, {
"type": "bar"
}], [{
"type": "scatter"
}, {
"type": "scatter"
}]],
subplot_titles=("Receita Total", "Lucro", 'Dividendos (%)',
'Dividendos unitário R$', 'Volatilidade',
'Sharpe ratio (Retorno/ Risco)'))
fig.add_trace(go.Bar(
x=pfizer.financials.transpose().index,
y=pfizer.financials.transpose()['Total Revenue']),
row=1,
col=1)
fig.add_trace(go.Bar(x=pfizer.financials.transpose().index,
y=pfizer.financials.transpose()
['Net Income From Continuing Ops']),
row=1,
col=2)
fig.add_trace(go.Bar(
x=div_group.reset_index().tail(5)['year'],
y=div_group.reset_index().tail(5)['dividendo(%)']),
row=2,
col=1)
fig.add_trace(go.Bar(
x=div_group.reset_index().tail(5)['year'],
y=div_group.reset_index().tail(5)['dividends']),
row=2,
col=2)
fig.add_trace(go.Scatter(x=vol['date'], y=vol['close']),
row=3,
col=1)
fig.add_trace(go.Scatter(x=sharpe['date'], y=sharpe['close']),
row=3,
col=2)
fig.update_layout(height=800, showlegend=False)
st.plotly_chart(fig)
else:
#volatilidade
TRADING_DAYS = 160
returns = np.log(time['close'] / time['close'].shift(1))
returns.fillna(0, inplace=True)
volatility = returns.rolling(
window=TRADING_DAYS).std() * np.sqrt(TRADING_DAYS)
vol = pd.DataFrame(volatility.iloc[-160:]).reset_index()
#sharpe ratio
sharpe_ratio = returns.mean() / volatility
sharpe = pd.DataFrame(sharpe_ratio.iloc[-160:]).reset_index()
from plotly.subplots import make_subplots
fig = make_subplots(
rows=1,
cols=2,
specs=[[{
"type": "scatter"
}, {
"type": "scatter"
}]],
subplot_titles=('Volatilidade',
'Sharpe ratio (Retorno/ Risco)'))
fig.add_trace(go.Scatter(x=vol['date'], y=vol['close']),
row=1,
col=1)
fig.add_trace(go.Scatter(x=sharpe['date'], y=sharpe['close']),
row=1,
col=2)
fig.update_layout(height=800, showlegend=False)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE Candlestick----------------------------
fig = make_subplots(rows=2,
cols=1,
shared_xaxes=True,
vertical_spacing=0.03,
subplot_titles=('OHLC', 'Volume'),
row_width=[0.2, 0.7])
# Plot OHLC on 1st row
fig.add_trace(go.Candlestick(x=time.reset_index()['date'][-90:],
open=time['open'][-90:],
high=time['high'][-90:],
low=time['low'][-90:],
close=time['close'][-90:],
name="OHLC"),
row=1,
col=1)
# Bar trace for volumes on 2nd row without legend
fig.add_trace(go.Bar(x=time.reset_index()['date'][-90:],
y=time['volume'][-90:],
showlegend=False),
row=2,
col=1)
# Do not show OHLC's rangeslider plot
fig.update(layout_xaxis_rangeslider_visible=False)
fig.update_layout(
autosize=False,
width=800,
height=800,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE Retorno acumulado----------------------------
layout = go.Layout(title="Retorno acumulado",
xaxis=dict(title="Data"),
yaxis=dict(title="Retorno"))
fig = go.Figure(layout=layout)
fig.add_trace(
go.Scatter(
x=time.reset_index()['date'][-365:],
y=time.reset_index()['close'][-365:].pct_change().cumsum(),
mode='lines',
line_width=3,
line_color='rgb(0,0,0)'))
fig.update_layout(
autosize=False,
width=800,
height=800,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE Médias móveis----------------------------
rolling_50 = time['close'].rolling(window=50)
rolling_mean_50 = rolling_50.mean()
rolling_20 = time['close'].rolling(window=20)
rolling_mean_20 = rolling_20.mean()
rolling_10 = time['close'].rolling(window=10)
rolling_mean_10 = rolling_10.mean()
layout = go.Layout(title="Médias móveis",
xaxis=dict(title="Data"),
yaxis=dict(title="Preço R$"))
fig = go.Figure(layout=layout)
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-120:],
y=time["close"][-120:],
mode='lines',
line_width=3,
name='Real',
line_color='rgb(0,0,0)'))
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-120:],
y=rolling_mean_50[-120:],
mode='lines',
name='MM(50)',
opacity=0.6))
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-120:],
y=rolling_mean_20[-120:],
mode='lines',
name='MM(20)',
opacity=0.6))
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-120:],
y=rolling_mean_10[-120:],
mode='lines',
name='MM(10)',
opacity=0.6,
line_color='rgb(100,149,237)'))
# fig.add_trace(go.Candlestick(x=time.reset_index()['date'][-120:], open=time['open'][-120:],high=time['high'][-120:],low=time['low'][-120:],close=time['close'][-120:]))
fig.update_layout(
autosize=False,
width=800,
height=800,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE Retração de Fibonacci----------------------------
time_fibo = time.copy()
periodo_fibonacci = int(
st.number_input(label='periodo fibonacci', value=90))
Price_Min = time_fibo[-periodo_fibonacci:]['low'].min()
Price_Max = time_fibo[-periodo_fibonacci:]['high'].max()
Diff = Price_Max - Price_Min
level1 = Price_Max - 0.236 * Diff
level2 = Price_Max - 0.382 * Diff
level3 = Price_Max - 0.618 * Diff
st.write('0% >>' f'{round(Price_Max,2)}')
st.write('23,6% >>' f'{round(level1,2)}')
st.write('38,2% >>' f'{round(level2,2)}')
st.write('61,8% >>' f'{round(level3,2)}')
st.write('100% >>' f'{round(Price_Min,2)}')
time_fibo['Price_Min'] = Price_Min
time_fibo['level1'] = level1
time_fibo['level2'] = level2
time_fibo['level3'] = level3
time_fibo['Price_Max'] = Price_Max
layout = go.Layout(title=f'Retração de Fibonacci',
xaxis=dict(title="Data"),
yaxis=dict(title="Preço"))
fig = go.Figure(layout=layout)
fig.add_trace(
go.Scatter(
x=time_fibo[-periodo_fibonacci:].reset_index()['date'],
y=time_fibo[-periodo_fibonacci:].close,
mode='lines',
line_width=3,
name='Preço real',
line_color='rgb(0,0,0)'))
fig.add_trace(
go.Scatter(
x=time_fibo[-periodo_fibonacci:].reset_index()['date'],
y=time_fibo[-periodo_fibonacci:].Price_Min,
mode='lines',
line_width=0.5,
name='100%',
line_color='rgb(255,0,0)',
))
fig.add_trace(
go.Scatter(
x=time_fibo[-periodo_fibonacci:].reset_index()['date'],
y=time_fibo[-periodo_fibonacci:].level3,
mode='lines',
line_width=0.5,
name='61,8%',
line_color='rgb(255,255,0)',
fill='tonexty',
fillcolor="rgba(255, 0, 0, 0.2)"))
fig.add_trace(
go.Scatter(
x=time_fibo[-periodo_fibonacci:].reset_index()['date'],
y=time_fibo[-periodo_fibonacci:].level2,
mode='lines',
line_width=0.5,
name='38,2%',
line_color='rgb(0,128,0)',
fill='tonexty',
fillcolor="rgba(255, 255, 0, 0.2)"))
fig.add_trace(
go.Scatter(
x=time_fibo[-periodo_fibonacci:].reset_index()['date'],
y=time_fibo[-periodo_fibonacci:].level1,
mode='lines',
line_width=0.5,
name='23,6%',
line_color='rgb(128,128,128)',
fill='tonexty',
fillcolor="rgba(0, 128, 0, 0.2)"))
fig.add_trace(
go.Scatter(
x=time_fibo[-periodo_fibonacci:].reset_index()['date'],
y=time_fibo[-periodo_fibonacci:].Price_Max,
mode='lines',
line_width=0.5,
name='0%',
line_color='rgb(0,0,255)',
fill='tonexty',
fillcolor="rgba(128, 128, 128, 0.2)"))
fig.update_layout(
autosize=False,
width=800,
height=800,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE RSI----------------------------
periodo_RSI = int(st.number_input(label='periodo RSI', value=90))
delta = time['close'][-periodo_RSI:].diff()
up, down = delta.copy(), delta.copy()
up[up < 0] = 0
down[down > 0] = 0
period = 14
rUp = up.ewm(com=period - 1, adjust=False).mean()
rDown = down.ewm(com=period - 1, adjust=False).mean().abs()
time['RSI_' + str(period)] = 100 - 100 / (1 + rUp / rDown)
time['RSI_' + str(period)].fillna(0, inplace=True)
layout = go.Layout(title=f'RSI {periodo_RSI}',
xaxis=dict(title="Data"),
yaxis=dict(title="%RSI"))
fig = go.Figure(layout=layout)
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-periodo_RSI:],
y=round(time['RSI_14'][-periodo_RSI:], 2),
mode='lines',
line_width=3,
name=f'RSI {periodo_RSI}',
line_color='rgb(0,0,0)'))
fig.update_layout(
autosize=False,
width=800,
height=800,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE pivôs----------------------------
periodo_pivo = int(st.number_input(label='periodo RSI', value=20))
time['PP'] = pd.Series(
(time['high'] + time['low'] + time['close']) / 3)
time['R1'] = pd.Series(2 * time['PP'] - time['low'])
time['S1'] = pd.Series(2 * time['PP'] - time['high'])
time['R2'] = pd.Series(time['PP'] + time['high'] - time['low'])
time['S2'] = pd.Series(time['PP'] - time['high'] + time['low'])
layout = go.Layout(title=f'Pivô',
xaxis=dict(title="Data"),
yaxis=dict(title="Preço"))
fig = go.Figure(layout=layout)
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-periodo_pivo:],
y=round(time['close'][-periodo_pivo:], 2),
mode='lines',
line_width=3,
name=f'preço real',
line_color='rgb(0,0,0)'))
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-periodo_pivo:],
y=round(time['PP'][-periodo_pivo:], 2),
mode='lines',
line_width=1,
name=f'Ponto do pivô',
line_color='rgb(0,128,0)'))
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-periodo_pivo:],
y=round(time['R1'][-periodo_pivo:], 2),
mode='lines',
line_width=1,
name=f'Resistência 1',
line_color='rgb(100,149,237)'))
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-periodo_pivo:],
y=round(time['S1'][-periodo_pivo:], 2),
mode='lines',
line_width=1,
name=f'Suporte 1',
line_color='rgb(100,149,237)'))
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-periodo_pivo:],
y=round(time['R2'][-periodo_pivo:], 2),
mode='lines',
line_width=1,
name=f'Resistência 2',
line_color='rgb(255,0,0)'))
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-periodo_pivo:],
y=round(time['S2'][-periodo_pivo:], 2),
mode='lines',
line_width=1,
name=f'Suporte 2',
line_color='rgb(255,0,0)'))
fig.update_layout(
autosize=False,
width=800,
height=800,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE Bolinger----------------------------
periodo_bolinger = int(
st.number_input(label='periodo Bolinger', value=180))
time['MA20'] = time['close'].rolling(20).mean()
time['20 Day STD'] = time['close'].rolling(window=20).std()
time['Upper Band'] = time['MA20'] + (time['20 Day STD'] * 2)
time['Lower Band'] = time['MA20'] - (time['20 Day STD'] * 2)
layout = go.Layout(title=f'Banda de Bolinger',
xaxis=dict(title="Data"),
yaxis=dict(title="Preço"))
fig = go.Figure(layout=layout)
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-periodo_bolinger:],
y=round(time['Upper Band'][-periodo_bolinger:], 2),
mode='lines',
line_width=1,
name=f'Banda superior',
line_color='rgb(255,0,0)'))
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-periodo_bolinger:],
y=round(time['Lower Band'][-periodo_bolinger:], 2),
mode='lines',
line_width=1,
name=f'Banda inferior',
line_color='rgb(255,0,0)',
fill='tonexty',
fillcolor="rgba(255, 0, 0, 0.1)",
opacity=0.2))
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-periodo_bolinger:],
y=round(time['close'][-periodo_bolinger:], 2),
mode='lines',
line_width=3,
name=f'preço real',
line_color='rgb(0,0,0)'))
fig.add_trace(
go.Scatter(x=time.reset_index()['date'][-periodo_bolinger:],
y=round(time['MA20'][-periodo_bolinger:], 2),
mode='lines',
line_width=2,
name=f'MM 20',
line_color='rgb(0,128,0)'))
fig.update_layout(
autosize=False,
width=800,
height=800,
)
st.plotly_chart(fig)
# ------------------------------ Previsões----------------------------
st.subheader('Previsões')
st.write(
'As previsões são feitas levando em conta apenas o movimento gráfico, porém o movimento do preço de um ativo é influenciado por diversos outros fatores, com isso, deve se considerar as previsões como uma hipótese de o preço do ativo variar somente pela sua variação gráfica'
)
st.write(
'Previsão considerando os últimos 365 dias, pode ser entendida como uma tendência dos dados segundo o último ano'
)
time = time.reset_index()
time = time[['date', 'close']]
time.columns = ['ds', 'y']
#Modelling
m = Prophet()
m.fit(time[-360:])
future = m.make_future_dataframe(periods=30)
forecast = m.predict(future[-30:])
from fbprophet.plot import plot_plotly, plot_components_plotly
fig1 = plot_plotly(m, forecast)
st.plotly_chart(fig1)
#st.plotly_chart(m, forecast)
fig2 = m.plot_components(forecast)
st.plotly_chart(fig2)
#st.write('Previsão considerando as últimas semanas, pode ser entendida como uma tendência dos dados segundo os últimos dias. Leva em consideração diversos fatores como: Índice de força relativa RSI, oscilador estocástico %K, Indicador Willian %R além do movimento gráfico dos últimos dias')
#predict = stocker.predict.tomorrow(nome_do_ativo)
#st.write('Previsão para o dia:',f'{predict[2]}','é que a ação feche no valor de: R$',f'{predict[0]}')
#preço_ontem= round(time['y'][-1:].values[0],2)
#if predict[0] < preço_ontem:
#st.write('Previsão para o dia:',f'{predict[2]}','é que a ação caia de ',f'{preço_ontem}', 'para valor de: R$ ',f'{predict[0]}')
#else:
#st.write('Previsão para o dia:',f'{predict[2]}','é que a ação suba de ',f'{preço_ontem}', 'para valor de: R$ ',f'{predict[0]}')
# ------------------------------ INÍCIO Comparação de ativos ------------------------------------------------------------------------------------
if n_sprites == "Comparação de ativos":
st.image('https://media.giphy.com/media/JtBZm3Getg3dqxK0zP/giphy.gif',
width=300)
#image = Image.open('imagens/logo.jpg')
#st.image(image, use_column_width=True)
st.title('Comparação de ativos')
st.subheader('Escolha até 4 ativos para comparar')
nome_do_ativo1 = st.text_input('Nome do 1º ativo')
nome_do_ativo2 = st.text_input('Nome do 2º ativo')
nome_do_ativo3 = st.text_input('Nome do 3º ativo')
nome_do_ativo4 = st.text_input('Nome do 4º ativo')
if nome_do_ativo4 != "":
st.subheader('Analisando os dados')
nome_do_ativo1 = str(nome_do_ativo1 + '.SA')
nome_do_ativo2 = str(nome_do_ativo2 + '.SA')
nome_do_ativo3 = str(nome_do_ativo3 + '.SA')
nome_do_ativo4 = str(nome_do_ativo4 + '.SA')
df = Ticker([
nome_do_ativo1, nome_do_ativo2, nome_do_ativo3, nome_do_ativo4
],
country='Brazil')
time = df.history(
start='2018-01-01',
end=(dt.datetime.today() +
dt.timedelta(days=1)).strftime(format='20%y-%m-%d'))
lista = get_data()
todos = pd.DataFrame(flatten(lista).keys()).transpose()
todos.columns = todos.iloc[0]
for i in range(len(lista)):
todos = pd.concat([todos, pd.DataFrame(lista[i]).transpose()])
todos = todos.iloc[1:]
todos['P/L'] = todos['P/L'].str.replace('.', '')
todos['DY'] = todos['DY'].str.replace('%', '')
todos['Liq.2m.'] = todos['Liq.2m.'].str.replace('.', '')
todos['Pat.Liq'] = todos['Pat.Liq'].str.replace('.', '')
todos = todos.replace(',', '.', regex=True)
todos = todos.apply(pd.to_numeric, errors='ignore')
comparar = todos.loc[todos.index.isin([
nome_do_ativo1[:5], nome_do_ativo2[:5], nome_do_ativo3[:5],
nome_do_ativo4[:5]
])]
st.dataframe(comparar)
# ------------------------------ INÍCIO Comparação DY ---------------
layout = go.Layout(title="DY",
xaxis=dict(title="Ativo"),
yaxis=dict(title="DY %"))
fig = go.Figure(layout=layout)
fig.add_trace(
go.Bar(x=comparar.sort_values('DY', ascending=True).index,
y=comparar.sort_values('DY', ascending=True)['DY']))
fig.update_layout(
autosize=False,
width=800,
height=400,
)
st.plotly_chart(fig)
# ------------------------------ INÍCIO Comparação P/L ---------------
layout = go.Layout(title="P/L",
xaxis=dict(title="Ativo"),
yaxis=dict(title="P/L"))
fig = go.Figure(layout=layout)
fig.add_trace(
go.Bar(x=comparar.sort_values('P/L', ascending=True).index,
y=comparar.sort_values('P/L', ascending=True)['P/L']))
fig.update_layout(
autosize=False,
width=800,
height=400,
)
st.plotly_chart(fig)
# ------------------------------ INÍCIO Comparação P/V---------------
layout = go.Layout(title="P/VP",
xaxis=dict(title="Ativo"),
yaxis=dict(title="P/VP"))
fig = go.Figure(layout=layout)
fig.add_trace(
go.Bar(x=comparar.sort_values('P/VP', ascending=True).index,
y=comparar.sort_values('P/VP', ascending=True)['P/VP']))
fig.update_layout(
autosize=False,
width=800,
height=400,
)
st.plotly_chart(fig)
# ------------------------------ INÍCIO Comparação P/L * P/VP---------------
layout = go.Layout(title="P/L X P/VP",
xaxis=dict(title="Ativo"),
yaxis=dict(title="P/L X P/VP"))
fig = go.Figure(layout=layout)
fig.add_trace(
go.Bar(x=comparar.index, y=comparar['P/L'] * comparar['P/VP']))
fig.update_layout(
autosize=False,
width=800,
height=400,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE retorno acumulado----------------------------
periodo_inicio = int(
st.number_input(label='periodo retorno acumulado', value=360))
ret = time.reset_index()
layout = go.Layout(title="Retorno acumulado",
xaxis=dict(title="Data"),
yaxis=dict(title="Retorno"))
fig = go.Figure(layout=layout)
for i in range(len(ret['symbol'].unique())):
fig.add_trace(
go.Scatter(
x=ret.loc[ret['symbol'] == ret['symbol'].unique()
[i]][-periodo_inicio:]['date'],
y=ret.loc[ret['symbol'] == ret['symbol'].unique()[i]]
[-periodo_inicio:]['close'].pct_change().cumsum(),
mode='lines',
name=ret.reset_index()['symbol'].unique()[i]))
fig.update_layout(
autosize=False,
width=800,
height=800,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE MÉDIAS MÓVEIS 50----------------------------
rolling_50 = time['close'].rolling(window=50)
rolling_mean_50 = rolling_50.mean()
rolling_mean_50 = pd.DataFrame(rolling_mean_50.reset_index())
# mm50 = time.reset_index()
layout = go.Layout(title="MÉDIAS MÓVEIS 50",
xaxis=dict(title="Data"),
yaxis=dict(title="Preço R$"))
fig = go.Figure(layout=layout)
for i in range(len(rolling_mean_50['symbol'].unique())):
fig.add_trace(
go.Scatter(
x=rolling_mean_50.loc[
rolling_mean_50['symbol'] ==
rolling_mean_50['symbol'].unique()[i]]['date'],
y=rolling_mean_50.loc[
rolling_mean_50['symbol'] ==
rolling_mean_50['symbol'].unique()[i]]['close'],
mode='lines',
name=time.reset_index()['symbol'].unique()[i]))
fig.update_layout(
autosize=False,
width=800,
height=800,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE MÉDIAS MÓVEIS 20----------------------------
rolling_50 = time['close'].rolling(window=20)
rolling_mean_50 = rolling_50.mean()
rolling_mean_50 = pd.DataFrame(rolling_mean_50.reset_index())
# mm50 = time.reset_index()
layout = go.Layout(title="MÉDIAS MÓVEIS 20",
xaxis=dict(title="Data"),
yaxis=dict(title="Preço R$"))
fig = go.Figure(layout=layout)
for i in range(len(rolling_mean_50['symbol'].unique())):
fig.add_trace(
go.Scatter(
x=rolling_mean_50.loc[
rolling_mean_50['symbol'] ==
rolling_mean_50['symbol'].unique()[i]]['date'],
y=rolling_mean_50.loc[
rolling_mean_50['symbol'] ==
rolling_mean_50['symbol'].unique()[i]]['close'],
mode='lines',
name=time.reset_index()['symbol'].unique()[i]))
fig.update_layout(
autosize=False,
width=800,
height=800,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE volatilidade---------------------------
TRADING_DAYS = 360
returns = np.log(time['close'] / time['close'].shift(1))
returns.fillna(0, inplace=True)
volatility = returns.rolling(
window=TRADING_DAYS).std() * np.sqrt(TRADING_DAYS)
vol = pd.DataFrame(volatility).reset_index()
vol = vol.dropna()
layout = go.Layout(title=f"Volatilidade",
xaxis=dict(title="Data"),
yaxis=dict(title="Volatilidade"))
fig = go.Figure(layout=layout)
for i in range(len(vol['symbol'].unique())):
fig.add_trace(
go.Scatter(x=vol.loc[vol['symbol'] ==
vol['symbol'].unique()[i]]['date'],
y=vol.loc[vol['symbol'] ==
vol['symbol'].unique()[i]]['close'],
name=vol['symbol'].unique()[i]))
fig.update_layout(
autosize=False,
width=800,
height=400,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE sharpe_ratio---------------------------
sharpe_ratio = returns.mean() / volatility
sharpe = pd.DataFrame(sharpe_ratio).reset_index()
sharpe = sharpe.dropna()
layout = go.Layout(title=f"SHARP (Risco / Volatilidade)",
xaxis=dict(title="Data"),
yaxis=dict(title="Sharp"))
fig = go.Figure(layout=layout)
for i in range(len(sharpe['symbol'].unique())):
fig.add_trace(
go.Scatter(
x=sharpe.loc[sharpe['symbol'] ==
sharpe['symbol'].unique()[i]]['date'],
y=sharpe.loc[sharpe['symbol'] ==
sharpe['symbol'].unique()[i]]['close'],
name=sharpe['symbol'].unique()[i]))
fig.update_layout(
autosize=False,
width=800,
height=400,
)
st.plotly_chart(fig)
# ------------------------------ GRÁFICOS DE correlação--------------------------
st.subheader('Correlação')
time = time.reset_index()
time = time[['symbol', 'date', 'close']]
df_1 = time.loc[time['symbol'] == time['symbol'].unique()[0]]
df_1 = df_1.set_index('date')
df_1.columns = df_1.columns.values + '-' + df_1.symbol.unique()
df_1.drop(df_1.columns[0], axis=1, inplace=True)
df_2 = time.loc[time['symbol'] == time['symbol'].unique()[1]]
df_2 = df_2.set_index('date')
df_2.columns = df_2.columns.values + '-' + df_2.symbol.unique()
df_2.drop(df_2.columns[0], axis=1, inplace=True)
df_3 = time.loc[time['symbol'] == time['symbol'].unique()[2]]
df_3 = df_3.set_index('date')
df_3.columns = df_3.columns.values + '-' + df_3.symbol.unique()
df_3.drop(df_3.columns[0], axis=1, inplace=True)
df_4 = time.loc[time['symbol'] == time['symbol'].unique()[3]]
df_4 = df_4.set_index('date')
df_4.columns = df_4.columns.values + '-' + df_4.symbol.unique()
df_4.drop(df_4.columns[0], axis=1, inplace=True)
merged = pd.merge(pd.merge(pd.merge(df_1,
df_2,
left_on=df_1.index,
right_on=df_2.index,
how='left'),
df_3,
left_on='key_0',
right_on=df_3.index,
how='left'),
df_4,
left_on='key_0',
right_on=df_4.index,
how='left').rename({
'key_0': 'date'
}, axis=1).set_index('date')
retscomp = merged.pct_change()
plt.figure(figsize=(10, 8))
sns.heatmap(retscomp.corr(), annot=True)
st.pyplot()
# ------------------------------ GRÁFICOS DE mapa de risco--------------------------
map = returns.reset_index()
layout = go.Layout(title=f"Mapa de Risco x Retorno",
xaxis=dict(title="Retorno esperado"),
yaxis=dict(title="Risco"))
fig = go.Figure(layout=layout)
for i in range(len(map['symbol'].unique())):
fig.add_trace(
go.Scatter(
x=[
map.loc[map['symbol'] == map['symbol'].unique()[i]]
['close'].mean() * 100
],
y=[
map.loc[map['symbol'] == map['symbol'].unique()[i]]
['close'].std() * 100
],
name=map['symbol'].unique()[i],
marker=dict(size=30)))
#fig.add_trace(go.Scatter(x=[map['close'].mean()], y=[map['close'].std()],text=map['symbol'].unique()))
fig.update_xaxes(zeroline=True,
zerolinewidth=2,
zerolinecolor='Red') #, range=[-0.005, 0.01])
fig.update_yaxes(zeroline=True,
zerolinewidth=2,
zerolinecolor='Red') #, range=[-0.01, 0.1])
fig.update_traces(textposition='top center')
fig.update_layout(
autosize=False,
width=800,
height=600,
)
st.plotly_chart(fig)
# ------------------------------ INÍCIO Comparação de ativos ------------------------------------------------------------------------------------
if n_sprites == "Descobrir novos ativos":
st.image('https://media.giphy.com/media/3ohs4gux2zjc7f361O/giphy.gif',
width=400)
#image = Image.open('imagens/logo.jpg')
#st.image(image, use_column_width=True)
st.title('Descobrir novos ativos')
PL_mínimo = int(st.number_input(label='PL_mínimo', value=10))
PL_máximo = int(st.number_input(label='PL_máximo', value=15))
PVP_mínimo = int(st.number_input(label='PVP_mínimo', value=0.7))
PVP_máximo = int(st.number_input(label='PVP_máximo', value=1.5))
DY_mínimo = int(st.number_input(label='DY_mínimo', value=4))
DY_máximo = int(st.number_input(label='DY_máximo', value=30))
lista = get_data()
todos = pd.DataFrame(flatten(lista).keys()).transpose()
todos.columns = todos.iloc[0]
for i in range(len(lista)):
todos = pd.concat([todos, pd.DataFrame(lista[i]).transpose()])
todos = todos.iloc[1:]
todos['P/L'] = todos['P/L'].str.replace('.', '')
todos['DY'] = todos['DY'].str.replace('%', '')
todos['Liq.2m.'] = todos['Liq.2m.'].str.replace('.', '')
todos['Pat.Liq'] = todos['Pat.Liq'].str.replace('.', '')
todos = todos.replace(',', '.', regex=True)
todos = todos.apply(pd.to_numeric, errors='ignore')
if st.checkbox("Filtrar"):
st.dataframe(todos.loc[(todos['P/L'] >= PL_mínimo)
& (todos['P/L'] <= PL_máximo) &
(todos['P/VP'] >= PVP_mínimo) &
(todos['P/VP'] <= PVP_máximo) &
(todos['DY'] >= DY_mínimo) &
(todos['DY'] <= DY_máximo)])