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from multiprocessing import Value
import matplotlib
matplotlib.use('Agg')
import streamlit as st
from yahooquery import Ticker
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import datetime as dt
from collections import OrderedDict
import style as style
import scrap as scraping
def flatten(d):
'''
Flatten an OrderedDict object
'''
result = OrderedDict()
for k, v in d.items():
if isinstance(v, dict):
result.update(flatten(v))
else:
result[k] = v
return result
def comparacao_ativos():
#código para ativar bootstrap css
st.markdown(
"""
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js" integrity="sha384-JZR6Spejh4U02d8jOt6vLEHfe/JQGiRRSQQxSfFWpi1MquVdAyjUar5+76PVCmYl" crossorigin="anonymous"></script>
""",unsafe_allow_html=True
)
col1, col2,col3 = st.columns([0.1,0.4,0.1])
with col2:
st.title('Comparação de ativos')
st.subheader('Escolha 4 ativos para comparar')
codigo_nome = pd.read_excel('data/classification_b3.xlsx')
nome_do_ativo1 = st.selectbox('Nome do 1º ativo', (codigo_nome['Código']),key=1 )
nome_do_ativo2 = st.selectbox('Nome do 2º ativo', (codigo_nome['Código']),key=2)
nome_do_ativo3 = st.selectbox('Nome do 3º ativo', (codigo_nome['Código']),key=3 )
nome_do_ativo4 = st.selectbox('Nome do 4º ativo', (codigo_nome['Código']),key=4 )
style.space(1)
if nome_do_ativo4 != "":
st.subheader('Analisando os dados')
nome_do_ativo1 = str(nome_do_ativo1 + '.SA').upper()
nome_do_ativo2 = str(nome_do_ativo2 + '.SA').upper()
nome_do_ativo3 = str(nome_do_ativo3 + '.SA').upper()
nome_do_ativo4 = str(nome_do_ativo4 + '.SA').upper()
df = Ticker([nome_do_ativo1,nome_do_ativo2,nome_do_ativo3,nome_do_ativo4],country='Brazil')
time = df.history( start='2020-01-01', end = (dt.datetime.today() + dt.timedelta(days=1)).strftime(format='20%y-%m-%d'))
lista = scraping.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').round(2)
todos.rename(columns={'cotacao': 'Cotação'}, inplace=True)
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)
# st.dataframe(comparar.style.format({"Cotação": "{:.2f}", "P/L": "{:.2f}", "P/VP": "{:.2f}", "P/Ativo": "{:.2f}"
# , "P/EBIT": "{:.2f}", "P/Ativ.Circ.Liq.": "{:.2f}", "EBITDA": "{:.2f}", "Liq.Corr.": "{:.2f}", "Liq.2m.": "{:.2f}"
# , "Pat.Liq": "{:.2f}", "Div.Brut/Pat.": "{:.2f}"
# }))
# ------------------------------ INÍCIO Comparação DY ---------------
col1, col2 = st.columns([0.5,0.5])
with col1:
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( height=500, showlegend=False, paper_bgcolor='rgba(255,255,255,0.9)', plot_bgcolor='rgba(255,255,255,0.9)')
fig.update_yaxes(showgrid=True, gridwidth=0.1, gridcolor = 'rgb(240,238,238)')
st.plotly_chart(fig,use_container_width=True)
# ------------------------------ INÍCIO Comparação P/L ---------------
with col2:
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( height=500, showlegend=False, paper_bgcolor='rgba(255,255,255,0.9)', plot_bgcolor='rgba(255,255,255,0.9)')
fig.update_yaxes(showgrid=True, gridwidth=0.1, gridcolor = 'rgb(240,238,238)')
st.plotly_chart(fig,use_container_width=True)
# ------------------------------ INÍCIO Comparação P/V---------------
with col1:
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( height=500, showlegend=False, paper_bgcolor='rgba(255,255,255,0.9)', plot_bgcolor='rgba(255,255,255,0.9)')
fig.update_yaxes(showgrid=True, gridwidth=0.1, gridcolor = 'rgb(240,238,238)')
st.plotly_chart(fig,use_container_width=True)
# ------------------------------ INÍCIO Comparação P/L * P/VP---------------
with col2:
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( height=500, showlegend=False, paper_bgcolor='rgba(255,255,255,0.9)', plot_bgcolor='rgba(255,255,255,0.9)')
fig.update_yaxes(showgrid=True, gridwidth=0.1, gridcolor = 'rgb(240,238,238)')
st.plotly_chart(fig,use_container_width=True)
# ------------------------------ 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( height=500, showlegend=False, paper_bgcolor='rgba(255,255,255,0.9)', plot_bgcolor='rgba(255,255,255,0.9)')
fig.update_yaxes(showgrid=True, gridwidth=0.1, gridcolor = 'rgb(240,238,238)')
st.plotly_chart(fig,use_container_width=True)
# ------------------------------ 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$"))
fig = go.Figure(layout = layout)
for i in range(len(rolling_mean_50['symbol'].unique())):
fig.add_trace(go.Scatter(x=rolling_mean_50.loc[rolling_mean_50['symbol']==rolling_mean_50['symbol'].unique()[i]]['date'], y=rolling_mean_50.loc[rolling_mean_50['symbol']==rolling_mean_50['symbol'].unique()[i]]['close'],mode='lines',name=time.reset_index()['symbol'].unique()[i]))
fig.update_layout( height=500, showlegend=False, paper_bgcolor='rgba(255,255,255,0.9)', plot_bgcolor='rgba(255,255,255,0.9)')
fig.update_yaxes(showgrid=True, gridwidth=0.1, gridcolor = 'rgb(240,238,238)')
st.plotly_chart(fig,use_container_width=True)
# ------------------------------ GRÁFICOS DE MÉDIAS MÓVEIS 20----------------------------
rolling_50 = time['close'].rolling(window=20)
rolling_mean_50 = rolling_50.mean()
rolling_mean_50 = pd.DataFrame(rolling_mean_50.reset_index())
# mm50 = time.reset_index()
layout = go.Layout(title="MÉDIAS MÓVEIS 20",xaxis=dict(title="Data"), yaxis=dict(title="Preço R$"))
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( height=500, showlegend=False, paper_bgcolor='rgba(255,255,255,0.9)', plot_bgcolor='rgba(255,255,255,0.9)')
fig.update_yaxes(showgrid=True, gridwidth=0.1, gridcolor = 'rgb(240,238,238)')
st.plotly_chart(fig,use_container_width=True)
# ------------------------------ GRÁFICOS DE volatilidade---------------------------
col1, col2 = st.columns([0.5,0.5])
with col1:
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( height=500, showlegend=False, paper_bgcolor='rgba(255,255,255,0.9)', plot_bgcolor='rgba(255,255,255,0.9)')
fig.update_yaxes(showgrid=True, gridwidth=0.1, gridcolor = 'rgb(240,238,238)')
st.plotly_chart(fig,use_container_width=True)
# ------------------------------ GRÁFICOS DE sharpe_ratio---------------------------
with col2:
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( height=500, showlegend=False, paper_bgcolor='rgba(255,255,255,0.9)', plot_bgcolor='rgba(255,255,255,0.9)')
fig.update_yaxes(showgrid=True, gridwidth=0.1, gridcolor = 'rgb(240,238,238)')
st.plotly_chart(fig,use_container_width=True)
# ------------------------------ GRÁFICOS DE correlação--------------------------
try:
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.set_option('deprecation.showPyplotGlobalUse', False)
#st.pyplot()
import plotly.express as px
df = px.data.medals_wide(indexed=True)
fig = px.imshow(retscomp.corr(),color_continuous_scale='YlOrRd',labels=dict(x="Correlação"))
#fig.update_layout(autosize=False,width=1200,height=800, paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
fig.update_layout( height=500, showlegend=False, paper_bgcolor='rgba(255,255,255,0.9)', plot_bgcolor='rgba(255,255,255,0.9)')
fig.update_yaxes(showgrid=True, gridwidth=0.1, gridcolor = 'rgb(240,238,238)')
fig.update_xaxes(side="top")
st.plotly_chart(fig,use_container_width=True)
except:
exit
# ------------------------------ 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, paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
fig.update_layout( height=500, showlegend=False, paper_bgcolor='rgba(255,255,255,0.9)', plot_bgcolor='rgba(255,255,255,0.9)')
fig.update_yaxes(showgrid=True, gridwidth=0.1, gridcolor = 'rgb(240,238,238)')
st.plotly_chart(fig,use_container_width=True)
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