diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..c862ed8
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,21 @@
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env
+venv
+pip-log.txt
+pip-delete-this-directory.txt
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+.git
+.mypy_cache
+.pytest_cache
+.hypothesis
\ No newline at end of file
diff --git a/.envExemple b/.envExemple
new file mode 100644
index 0000000..95294e1
--- /dev/null
+++ b/.envExemple
@@ -0,0 +1,3 @@
+OPENAI_API_KEY='Your Open AI API key here'
+username_td='Your username for Trading view here'
+password_td='Your password for Trading view here'
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..480de20
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,114 @@
+### User defined ###
+
+# Access credentials
+credentials.py
+
+
+
+# Log files
+*.log
+*.out
+
+# Csv files
+# *.csv
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__
+components/__pycache__/
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+plots/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+.pytest_cache/
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule.*
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..db4a0a1
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,11 @@
+FROM python:3.11
+
+
+WORKDIR /app
+COPY . ./
+
+RUN pip install -r requirements.txt
+
+EXPOSE 8080
+
+CMD python index.py
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..e880877
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Felipe Roratto Muner
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e2721c9
--- /dev/null
+++ b/README.md
@@ -0,0 +1,71 @@
+# SmartInvest AI
+
+## Visão Geral
+
+O SmartInvest AI é um painel de análise de investimentos para o mercado de ações, projetado para ajudar investidores a monitorar e analisar o desempenho dos ativos em suas carteiras em tempo real. Com funcionalidades que incluem o registro de transações, visualizações gráficas detalhadas e a comparação com o índice IBOV, o SmartInvest AI auxilia na tomada de decisões de investimento mais informadas. A integração com a API do ChatGPT oferece um consultor financeiro inteligente, proporcionando análises aprofundadas e recomendações personalizadas.
+
+## Estrutura do Código
+
+O aplicativo é composto por diversos componentes principais, organizados da seguinte forma:
+
+- `app.py`: Inicializa o aplicativo, configura estilos, gerencia exceções de callback e define configurações do servidor.
+- `index.py`: Responsável por rodar o aplicativo e gerenciar as rotas ou páginas do aplicativo.
+- `utils/functions.py`: Funções utilitárias para manipulação de dados e interação com a API do TradingView.
+- `components/`: Diretório contendo os componentes do Dash, como a carteira (`wallet.py`), modal de cadastro de ativos (`modal.py`), página inicial (`home.py`), cabeçalho (`header.py`), integração ChatGPT (`chatgpt.py`), e outros.
+- `tvdatafeed/`: Módulos para interação com a API do TradingView, incluindo `consumer.py`, `datafeed.py`, `main.py` e `seis.py`.
+
+## Funcionalidades
+
+- Monitoramento em tempo real dos ativos
+- Registro e acompanhamento de transações de compra e venda
+- Visualizações gráficas para análise dos ativos
+- Comparação do desempenho da carteira com o IBOV
+- Consultoria financeira através da integração com o ChatGPT
+
+## Instalação
+
+Clone o repositório para obter a última versão do SmartInvest AI:
+
+```
+git clone https://github.com/takeonepilot/Smart_Invest_AI.git
+```
+
+## Docker
+
+Instalção via Docker, execute os seguintes comandos:
+
+```
+docker build -t smart_invest_ai .
+```
+
+```
+docker run -d --name mart_invest_ai -p 8080:8080 smart_invest_ai
+```
+
+## Local
+
+Instale as dependências necessárias para executar o aplicativo:
+
+```
+pip install -r requirements.txt
+```
+
+## Execução
+
+Para iniciar o aplicativo, execute:
+
+```
+python index.py
+```
+
+## Contribuição
+
+Contribuições são sempre bem-vindas! Se você tem alguma sugestão para melhorar o aplicativo, sinta-se à vontade para criar uma issue ou enviar um pull request.
+
+## Contato
+
+Para dúvidas, sugestões ou contribuições, sinta-se à vontade para abrir uma [Issue](https://github.com/takeonepilot/Smart_Invest_AI/issues) aqui no GitHub ou me seguir e entrar em contato através do [meu perfil no GitHub](https://github.com/takeonepilot).
+
+## Licença
+
+SmartInvest AI é distribuído sob a licença MIT.
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..18fa7f7
--- /dev/null
+++ b/app.py
@@ -0,0 +1,10 @@
+import dash
+import dash_bootstrap_components as dbc
+
+estilos = ["https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css", "https://fonts.googleapis.com/icon?family=Material+Icons"]
+
+app = dash.Dash(external_stylesheets= estilos + [dbc.themes.BOOTSTRAP])
+
+app.config['suppress_callback_exceptions'] = True
+app.scripts.config.serve_locally = True
+server = app.server
\ No newline at end of file
diff --git a/assets/logo_dark.png b/assets/logo_dark.png
new file mode 100644
index 0000000..bffa3b3
Binary files /dev/null and b/assets/logo_dark.png differ
diff --git a/assets/menu_styles.py b/assets/menu_styles.py
new file mode 100644
index 0000000..c92ef33
--- /dev/null
+++ b/assets/menu_styles.py
@@ -0,0 +1,67 @@
+COR_LEGENDA = 'rgba(255,255,255,0.4)'
+
+#menu radar graph
+INDICADORES = 'rgba(255,255,255,0.4)'
+BACKGROUND_RADAR = 'rgba(178, 45, 178, 1)'
+LINHA_X = 'white'
+LINHA_Y = 'white'
+LINHAS_CIRCULARES = '#0d0834'
+LINHA_CIRCULAR_EXTERNA = '#0d0834'
+LINHAS_PREENCHIMENTO_1 = 'white'
+LINHAS_PREENCHIMENTO_2 = '#0d0834'
+CAIXA_LEGENDA = 'rgba(255,255,255,0.1)'
+AXIS_X_VALUES_COLOR = 'rgba(0,0,0,0)'
+TAMANHO_INDICADORES = 15
+TAMANHO_RADAR = 190
+
+#menu line graph
+AXIS_FONT_SIZE = 20
+AXIS_VALUES_COLOR = 'white'
+LINHAS_DE_GRADE = 'rgba(255,255,255,0.1)'
+LINHA_ZERO_X = 'rgba(255,255,255,0.2)'
+LINHA_EVOLUCAO_PATRIMONIAL = 'rgba(178, 45, 178, 1)'
+LISTA_DE_CORES_LINHAS = ['rgba(178, 45, 178, 1)', 'rgba(76,0,176,0.4)', 'rgba(138,0,176,0.4)', 'rgba(103,0,103,0.4)', 'rgba(87,53,106,0.4)', 'rgba(177,0,205,0.4)', 'rgba(190,46,214,0.4)', 'white']
+HOVER_LINE_GRAPH = {
+ "bgcolor":"rgba(32, 36, 73, 0.7)",
+ "font" : {'color':COR_LEGENDA}
+}
+#PREENCHIMENTO_LINE_GRAPH = 'rgba(178, 45, 178, 0.1)'
+
+#cards fixed_row
+CARD_GRAPHS_LINE_COLOR = 'rgba(178, 45, 178, 0.5)'
+
+#radar graph
+MAIN_CONFIG = {
+ "hovermode": "x unified",
+ "legend": {"yanchor":"top",
+ "y":1.0,
+ "xanchor":"left",
+ "x":1.0,
+ "title": {"text": None},
+ "bgcolor": CAIXA_LEGENDA},
+ "font" : {'color': COR_LEGENDA},
+ "margin": {"l":0, "r":0, "t":10, "b":0},
+}
+
+#line graph
+MAIN_CONFIG_2 = {
+ "hovermode": "x unified",
+ "legend": {"yanchor":"top",
+ "y":0.25,
+ "xanchor":"left",
+ "x":0.05,
+ "bgcolor": CAIXA_LEGENDA},
+ "font" : {'color': COR_LEGENDA},
+ "margin": {"l":0, "r":0, "t":10, "b":0},
+}
+
+#cards graph
+MAIN_CONFIG_3 = {
+ # "hovermode": "x unified",
+ "legend": {"bgcolor": 'rgba(0,0,0,0)'},
+ "font" : None,
+ "margin": {"l":0, "r":0, "t":0, "b":0},
+}
+
+
+HEIGHT={'height': '100%'}
\ No newline at end of file
diff --git a/assets/nexa-extralight.ttf b/assets/nexa-extralight.ttf
new file mode 100644
index 0000000..7efb7dc
Binary files /dev/null and b/assets/nexa-extralight.ttf differ
diff --git a/assets/nexa-heavy.ttf b/assets/nexa-heavy.ttf
new file mode 100644
index 0000000..ea68d8c
Binary files /dev/null and b/assets/nexa-heavy.ttf differ
diff --git a/assets/nexa-regular.otf b/assets/nexa-regular.otf
new file mode 100644
index 0000000..6e2667f
Binary files /dev/null and b/assets/nexa-regular.otf differ
diff --git a/assets/style.css b/assets/style.css
new file mode 100644
index 0000000..4502647
--- /dev/null
+++ b/assets/style.css
@@ -0,0 +1,277 @@
+/*Imports das fontes de textos*/
+@font-face {
+ font-family: 'Nexa';
+ src: url('nexa-extralight.ttf') format('truetype'),
+ url('nexa-extralight.woff2') format('woff2'),
+ url('nexa-extralight.woff') format('woff');
+ font-weight: normal;
+ font-style: normal;
+}
+@font-face {
+ font-family: 'NexaRegular';
+ src: url('nexa-regular.otf') format('truetype'),
+ url('nexa-regular.woff2') format('woff2'),
+ url('nexa-regular.woff') format('woff');
+ font-weight: normal;
+ font-style: normal;
+}
+
+/*Estilização do background do dashboard*/
+body{
+ background: linear-gradient(#100731, #100731, #0E112D, #0E112D) fixed;
+}
+
+/*Estilização do header*/
+.header_icon{
+ background-color: transparent !important;
+ border-color : transparent !important;
+ color : rgba(255, 255, 255, 0.6) !important;
+ font-size: 16px;
+ padding: 0;
+ font-family: Nexa;
+}
+.header_icon:hover{
+ transition: 0.4s ease-out !important;
+ color: white !important;
+ background-color: transparent !important;
+ border-color: transparent !important;
+}
+
+/*Estilização dos cards da fixed_row*/
+.card1_linha1{
+ background-color: transparent;
+ color: rgba(255,255,255,1);;
+ border: transparent;
+}
+.card2_linha1{
+ background-color: transparent;
+ border-color: transparent;
+}
+.cards_linha2{
+ background-color: rgba(32, 36, 73);
+}
+.cardRadar{
+ background-color: transparent;
+ color: white;
+ border: transparent;
+}
+
+/*Estilização dos botões switchs e radio buttons*/
+.form-check-input{
+ background-color: #202449;
+ border-color: rgba(255,255,255,0.2);
+}
+.form-check-input:checked {
+ background-color: #b22db2;
+ border-color: #b22db2;
+}
+
+/* Estilização do menu dropdown do line graph.
+Até daria pra alterar algumas caracteristicas do dropdown diretamente no dcc.dropdown, mas algumas só consegui pelo css, entao preferi alterar tudo por aqui pra centralizar as edições aqui */
+.Select-control {
+ background-color: #202449 !important;
+ border-color: rgba(255,255,255,0.2);
+ color: white;
+}
+.is-open>.Select-control{
+ border-color: rgba(255,255,255,0.1);
+}
+.is-focused:not(.is-open)>.Select-control{
+ border-color: #b22db2;
+}
+.Select-menu-outer {
+ background-color: #202449;
+ border-color: rgba(255,255,255,0.1);
+ color: #b22db2;
+}
+.Select--multi .Select-value{
+ color: #b22db2;
+ border-color: #b22db2;
+}
+.Select-value-icon{
+ color: #b22db2 ;
+ border-color: #b22db2 !important;
+}
+.Select-value-icon:hover{
+ color: red !important;
+}
+
+/* Estilização das fontes: cores, tamanhos e famlias*/
+.textoPrincipal{
+ font-size: 3.8rem;
+ color: white;
+ font-family: NexaRegular;
+}
+.textoSecundario{
+ font-size: 1.8rem;
+ color: white;
+ font-family: NexaRegular;
+}
+.textoTerciario{
+ font-size: 1.4rem;
+ color: white;
+ font-family: NexaRegular;
+}
+.textoTerciarioSwitchLineGraph{
+ font-size: 1.4rem;
+ color: white;
+}
+.textoQuartenario{
+ font-size: 1.2rem;
+ color: rgba(255,255,255,0.4);
+ display: inline;
+ font-family: NexaRegular;
+}
+.textoQuartenarioBranco{
+ font-size: 1.2rem;
+ color: white;
+ display: inline;
+ font-family: NexaRegular;
+}
+.textoQuartenarioVerde{
+ font-size: 1.2rem;
+ color: rgba(8,180,164,1);
+ font-family: Nexa;
+}
+.textoQuartenarioVermelho{
+ font-size: 1.2rem;
+ color: rgba(255,0,0, 0.7);
+ font-family: Nexa;
+}
+h5 {
+ display: inline;
+}
+
+/* Estilização graph line*/
+.graph_line{
+ height: 530px;
+}
+.graph_cards{
+ height: 40px;
+}
+
+/* Estilização dos cards da wallet*/
+.card_compra{
+ border-left-width: 10px;
+ border-left-color: rgba(8,180,164,1);
+ background-color: rgba(255, 255, 255, 0.1);
+ color: white;
+}
+.card_venda{
+ border-left-width: 10px;
+ border-left-color: rgba(255,0,0, 0.7);
+ background-color: rgba(255, 255, 255, 0.1);
+ color: white;
+}
+.card_sem_registros{
+ background-color: transparent;
+ color: white;
+}
+
+/* estilização do scroll*/
+::-webkit-scrollbar {
+ width: 10px;
+}
+::-webkit-scrollbar-track {
+ box-shadow: inset 0 0 5px rgba(255,255,255, 0.5);
+ border-radius: 10px;
+}
+::-webkit-scrollbar-thumb {
+ border-radius: 10px;
+ height: 30px;
+ background: #0E112D;
+}
+
+/* estilização do modal*/
+.modal_header{
+ background: #110D31;
+ color: white;
+}
+.modal{
+ --bs-modal-header-border-color: #343b3e;
+ --bs-modal-footer-border-color: #343b3e;
+}
+.btn-close{
+ background-image:url(https://icon-library.com/images/x-icon-white/x-icon-white-7.jpg);
+}
+.modal_body{
+ background: #110D31;
+ color: white;
+}
+.modal_footer{
+ background: #110D31;
+}
+.modal-content{
+ background-color: transparent;
+ border-color: #343b3e;
+ border-width: 1px;
+}
+#submit_cadastro{
+ background-color: rgba(178, 45, 178, 1);
+ border-color: transparent;
+}
+#submit_cadastro:hover{
+ background-color: rgba(178, 45, 178, 0.8);
+}
+#nome_ativo{
+ background-color: rgba(255,255, 255, 0.1);
+ border-color: rgba(255,255, 255, 0.1);
+ color: white;
+}
+#nome_ativo::placeholder{
+ color: rgba(255,255, 255, 0.3);
+}
+#preco_ativo{
+ background-color: rgba(255,255, 255, 0.1);
+ border-color: rgba(255,255, 255, 0.1);
+ color: white;
+}
+#preco_ativo::placeholder{
+ color: rgba(255,255, 255, 0.3);
+}
+#quantidade_ativo{
+ background-color: rgba(255,255, 255, 0.1);
+ border-color: rgba(255,255, 255, 0.1);
+ color: white;
+}
+#quantidade_ativo::placeholder{
+ color: rgba(255,255, 255, 0.3);
+}
+.DateInput_input{
+ background-color: #292646;
+ color: rgba(255,255, 255, 1);
+}
+.SingleDatePickerInput{
+ border-color: #292646;
+}
+
+/* Estilização ícones lixeira dos cards da wallet */
+.fa-trash{
+ color : rgba(255, 255, 255, 0.6);
+}
+.fa-trash:hover{
+ transition: 0.2s ease-out;
+ color: white ;
+}
+
+/*###########################################################*/
+.btn{
+ background-color: rgba(178, 45, 178, 1);
+ border-color : rgba(255, 255, 255, 0.3);
+ color: rgba(255, 255, 255, 0.8);
+}
+
+.btn:hover{
+ background-color: rgba(178, 45, 178, 0.8);
+ color: rgba(255, 255, 255, 0.8);
+ border-color: rgba(255, 255, 255, 0.5);
+}
+.card_chatgpt_user{
+ background-color: rgba(255, 255, 255, 0.1);
+ color: white;
+}
+.card_chatgpt_gpt{
+ background-color: rgba(255, 255, 255, 0.3);
+ color: white;
+}
+/*###########################################################*/
\ No newline at end of file
diff --git a/components/chatgpt.py b/components/chatgpt.py
new file mode 100644
index 0000000..8b393c4
--- /dev/null
+++ b/components/chatgpt.py
@@ -0,0 +1,303 @@
+from app import *
+from assets.menu_styles import *
+from utils.functions import *
+
+from dash import html
+import dash_bootstrap_components as dbc
+from dash.dependencies import Input, Output, State
+import pandas as pd
+import os
+from dotenv import load_dotenv
+
+from openai import OpenAI
+
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+load_dotenv()
+
+# definicao da chave da API
+
+
+try:
+ df_historico_msgs = pd.read_csv("data/historical_msgs.csv", index_col=0)
+
+except:
+ df_historico_msgs = pd.DataFrame(columns=["user", "chatGPT"])
+
+try:
+ sectors_df = pd.read_csv("data/sectors.csv", index_col=0)
+ sector_map = dict(zip(sectors_df["Código"], sectors_df["Setor"]))
+ df_data_wallet = pd.read_csv("data/book_data.csv")
+ df_data_wallet.drop("exchange", axis=1, inplace=True)
+ df_data_wallet["date"] = df_data_wallet["date"].str.replace("T00:00:00", "")
+ df_data_wallet["Setor"] = df_data_wallet["ativo"].map(sector_map)
+
+except:
+ sectors_df = pd.DataFrame(columns=["setor", "ativo", "participacao %"])
+
+
+df_historico_msgs.to_csv("data/historical_msgs.csv")
+
+
+def generate_card_gpt(pesquisa):
+ cardNovo = dbc.Row(
+ [
+ dbc.Col(
+ [
+ dbc.Card(
+ [
+ dbc.CardBody(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ html.H5(
+ [
+ html.I(
+ className="fa fa-desktop",
+ style={
+ "font-size": "85%"
+ },
+ ),
+ " SmartInvestGPT: ",
+ ],
+ className="textoQuartenario",
+ ),
+ html.H5(
+ str(pesquisa),
+ className="textoQuartenarioBranco",
+ ),
+ ],
+ md=12,
+ style={
+ "text-align": "left"
+ },
+ ),
+ ]
+ ),
+ ],
+ md=11,
+ xs=6,
+ style={"text-align": "left"},
+ ),
+ ]
+ )
+ ]
+ )
+ ],
+ className="card_chatgpt_gpt",
+ )
+ ]
+ )
+ ],
+ className="g-2 my-auto",
+ )
+
+ return cardNovo
+
+
+def generate_card_user(pesquisa):
+ cardNovo = dbc.Row(
+ [
+ dbc.Col(
+ [
+ dbc.Card(
+ [
+ dbc.CardBody(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ html.H5(
+ [
+ html.I(
+ className="fa fa-user-circle",
+ style={
+ "font-size": "85%"
+ },
+ ),
+ " User: ",
+ ],
+ className="textoQuartenario",
+ ),
+ html.H5(
+ str(pesquisa),
+ className="textoQuartenarioBranco",
+ ),
+ ],
+ md=12,
+ style={
+ "text-align": "left"
+ },
+ ),
+ ]
+ ),
+ ],
+ md=11,
+ xs=6,
+ style={"text-align": "left"},
+ ),
+ ]
+ )
+ ]
+ )
+ ],
+ className="card_chatgpt_user",
+ )
+ ]
+ )
+ ],
+ className="g-2 my-auto",
+ )
+
+ return cardNovo
+
+
+def gerar_resposta(messages):
+ try:
+ response = client.chat.completions.create(
+ model="gpt-3.5-turbo", messages=messages, max_tokens=1024, temperature=1
+ )
+ retorno = response.choices[0].message.content
+ except:
+ retorno = "Não foi possível pesquisar. ChatGPT fora do ar"
+ return retorno
+
+
+def clusterCards(df_msgs_store):
+
+ df_historical_msgs = pd.DataFrame(df_msgs_store)
+ cardsList = []
+
+ for line in df_historical_msgs.iterrows():
+ card_pergunta = generate_card_user(line[1]["user"])
+ card_resposta = generate_card_gpt(line[1]["chatGPT"])
+
+ cardsList.append(card_pergunta)
+ cardsList.append(card_resposta)
+
+ return cardsList
+
+
+layout = (
+ dbc.Container(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ "SmartInvest Chat",
+ className="textoPrincipal",
+ style={"margin-top": "10px"},
+ md=12,
+ ),
+ ],
+ className="g-2 my-auto",
+ ),
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ dbc.Input(
+ id="msg_user_wallet",
+ type="text",
+ placeholder="Insira uma mensagem",
+ )
+ ],
+ md=10,
+ ),
+ dbc.Col(
+ [
+ dbc.Button(
+ "Pesquisa", id="botao_search_wallet"
+ )
+ ],
+ md=2,
+ ),
+ ],
+ className="g-2 my-auto",
+ ),
+ dbc.Row(
+ [
+ dbc.Col(
+ [],
+ md=12,
+ id="cards_respostas_wallet",
+ style={
+ "height": "100%",
+ "maxHeight": "25rem",
+ "overflow-y": "auto",
+ },
+ ),
+ ],
+ className="g-2 my-auto",
+ ),
+ ],
+ md=12,
+ ),
+ ],
+ className="g-2 my-auto",
+ )
+ ],
+ fluid=True,
+ ),
+)
+
+
+@app.callback(
+ Output("cards_respostas_wallet", "children"),
+ Input("botao_search_wallet", "n_clicks"),
+ State("msg_user_wallet", "value"),
+)
+def add_msg(n, msg_user):
+
+ df_historical_msgs = pd.read_csv("data/historical_msgs.csv", index_col=0)
+
+ if msg_user == None:
+ lista_cards = clusterCards(df_historical_msgs)
+ return lista_cards
+
+ if "ativo".lower() or "ativos".lower() in msg_user:
+ mensagem = (
+ f"{df_data_wallet}, considerando todos os dados existentes dentro do dataframe, desde a primeira linha até a última, qual é a resposta exata de um expert para a pergunta: "
+ + msg_user
+ )
+ else:
+ mensagem = f"qual é a resposta exata de um expert para a pergunta: " + msg_user
+
+ mensagens = []
+ mensagens.append({"role": "user", "content": str(mensagem)})
+
+ pergunta_user = mensagens[0]["content"]
+ resposta_chatgpt = gerar_resposta(mensagens)
+
+ if pergunta_user == "None" or pergunta_user == "":
+ lista_cards = clusterCards(df_historical_msgs)
+ return lista_cards
+
+ new_line = pd.DataFrame(
+ [[pergunta_user, resposta_chatgpt]], columns=["user", "chatGPT"]
+ )
+
+ new_line["user"] = new_line["user"].str.split(":")
+ new_line["user"] = new_line["user"][0][-1]
+ df_historical_msgs = pd.concat([new_line, df_historical_msgs], ignore_index=True)
+
+ df_historical_msgs.to_csv("data/historical_msgs.csv")
+
+ lista_cards = clusterCards(df_historical_msgs)
+
+ return lista_cards
diff --git a/components/fixed_row.py b/components/fixed_row.py
new file mode 100644
index 0000000..c340731
--- /dev/null
+++ b/components/fixed_row.py
@@ -0,0 +1,738 @@
+from dash import html, dcc, Input, Output, State, no_update
+import dash_bootstrap_components as dbc
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+from datetime import date
+
+from assets.menu_styles import *
+from utils.functions import *
+from app import *
+
+layout = dbc.Container(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ dbc.Card(
+ [
+ dbc.CardBody(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ html.Img(
+ src="assets/logo_dark.png",
+ height="120px",
+ ),
+ " SmartInvest AI",
+ ],
+ className="textoPrincipal",
+ ),
+ ]
+ )
+ ]
+ )
+ ],
+ className="card1_linha1",
+ )
+ ],
+ xs=12,
+ md=7,
+ ),
+ dbc.Col(
+ [
+ dbc.Card(
+ [
+ dbc.CardBody(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ html.Legend(
+ "CARTEIRA:",
+ className="textoSecundario",
+ )
+ ],
+ md=4,
+ xs=3,
+ style={
+ "text-align": "right"
+ },
+ ),
+ dbc.Col(
+ [],
+ md=5,
+ xs=6,
+ id="carteira_valor",
+ style={
+ "text-align": "left"
+ },
+ ),
+ dbc.Col(
+ [],
+ md=3,
+ xs=3,
+ id="carteira_percent",
+ style={
+ "text-align": "left"
+ },
+ ),
+ ]
+ ),
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ html.Legend(
+ "IBOV: ",
+ className="textoQuartenario",
+ )
+ ],
+ md=4,
+ xs=3,
+ style={
+ "text-align": "right"
+ },
+ ),
+ dbc.Col(
+ [],
+ md=5,
+ xs=5,
+ id="ibov_valor",
+ style={
+ "text-align": "left"
+ },
+ ),
+ dbc.Col(
+ [],
+ md=3,
+ xs=4,
+ id="ibov_percent",
+ style={
+ "text-align": "left"
+ },
+ ),
+ ]
+ ),
+ ]
+ )
+ ],
+ className="card2_linha1",
+ )
+ ],
+ md=5,
+ xs=12,
+ ),
+ ],
+ className="g-2 my-auto",
+ ),
+ dbc.Row(
+ [
+ dbc.Col([], md=12, id="cards_ativos"),
+ ],
+ className="g-2 my-auto",
+ ),
+ ],
+ xs=12,
+ md=9,
+ ),
+ dbc.Col(
+ [
+ dbc.Card(
+ [
+ dbc.CardBody(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ dbc.Switch(
+ id="radar_switch",
+ value=True,
+ label="Setores IBOV X Carteira",
+ className="textoTerciario",
+ ),
+ ]
+ )
+ ]
+ ),
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ dcc.Graph(
+ id="radar_graph",
+ config={
+ "displayModeBar": False,
+ "showTips": False,
+ },
+ )
+ ]
+ )
+ ]
+ ),
+ ]
+ )
+ ],
+ className="cardRadar",
+ )
+ ],
+ xs=12,
+ md=3,
+ ),
+ ],
+ className="g-2 my-auto",
+ ),
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ dcc.Dropdown(
+ id="dropdown_card1",
+ value=[],
+ multi=True,
+ options=[],
+ ),
+ ],
+ sm=12,
+ md=3,
+ ),
+ dbc.Col(
+ [
+ dbc.RadioItems(
+ options=[
+ {"label": x, "value": x}
+ for x in PERIOD_OPTIONS
+ ],
+ value="1y",
+ id="period_input",
+ inline=True,
+ className="textoTerciario",
+ ),
+ ],
+ sm=12,
+ md=7,
+ ),
+ dbc.Col(
+ [
+ html.Span(
+ [
+ dbc.Label(
+ className="fa fa-user-circle"
+ ),
+ dbc.Switch(
+ id="profit_switch",
+ value=True,
+ className="d-inline-block ms-1",
+ ),
+ dbc.Label(
+ className="fa fa-shopping-basket"
+ ),
+ ],
+ className="textoTerciarioSwitchLineGraph",
+ ),
+ ],
+ sm=12,
+ md=2,
+ style={"text-align": "end"},
+ ),
+ ],
+ className="g-2 my-auto",
+ ),
+ ],
+ xs=12,
+ md=12,
+ ),
+ ],
+ className="g-2 my-auto",
+ ),
+ ],
+ fluid=True,
+)
+
+
+# Callback radar graph
+@app.callback(
+ Output("radar_graph", "figure"),
+ Input("book_data_store", "data"),
+ Input("radar_switch", "value"),
+)
+def radar_graph(book_data, switch):
+ df_registros = pd.DataFrame(book_data)
+ df_registros["vol"] = abs(df_registros["vol"]) * df_registros["tipo"].replace(
+ {"Compra": 1, "Venda": -1}
+ )
+
+ #####################################################################################################################################
+ try:
+ df_sectors = pd.read_csv("data/sectors.csv", index_col=0)
+
+ except:
+ df_sectors = pd.DataFrame(columns=["setor", "ativo", "participacao %"])
+
+ df_sectors.to_csv("data/sectors.csv")
+ #####################################################################################################################################
+
+ if switch:
+ df_ibov_prop = df_ibov[
+ df_ibov["Código"].isin(df_registros["ativo"].unique())
+ ].copy()
+ df_ibov_prop["Proporcao"] = df_ibov_prop["Participação"].apply(
+ lambda x: x * 100 / df_ibov_prop["Participação"].sum()
+ )
+
+ #####################################################################################################################################
+ df_sectors = df_ibov_prop.drop(
+ [
+ "Ação",
+ "Tipo",
+ "Qtde. Teórica",
+ "Part. (%)Acum.",
+ "Participação",
+ "Proporcao",
+ ],
+ axis=1,
+ )
+ df_sectors.to_csv("data/sectors.csv")
+ #####################################################################################################################################
+
+ ibov_setor = df_ibov_prop.groupby("Setor")["Proporcao"].sum(numeric_only=True)
+
+ df_registros = df_registros[
+ df_registros["ativo"].isin(df_ibov["Código"].unique())
+ ]
+ df_registros.loc[:, "Participação"] = df_registros["vol"].apply(
+ lambda x: x * 100 / df_registros["vol"].sum()
+ )
+
+ df_registros = df_registros.groupby("ativo")["Participação"].sum(
+ numeric_only=True
+ )
+ df_registros = pd.DataFrame(df_registros).reset_index()
+
+ try:
+ df_registros["setores"] = np.concatenate(
+ [
+ df_ibov_prop[df_ibov_prop["Código"] == ativo]["Setor"].values
+ for ativo in df_registros["ativo"]
+ ]
+ )
+ except:
+ df_registros["setores"] = "Sem registros"
+
+ df_registros = df_registros.groupby("setores")["Participação"].sum(
+ numeric_only=True
+ )
+
+ fig = go.Figure()
+ fig.add_trace(
+ go.Scatterpolar(
+ r=ibov_setor,
+ theta=ibov_setor.index,
+ name="IBOV",
+ fill="toself",
+ hovertemplate="IBOV"
+ + "
Participação: %{r:.2f}%"
+ + "
Setor: %{theta}
",
+ line=dict(color=LINHAS_PREENCHIMENTO_1),
+ )
+ )
+ fig.add_trace(
+ go.Scatterpolar(
+ r=df_registros,
+ theta=df_registros.index,
+ name="Carteira",
+ fill="toself",
+ hovertemplate="CARTEIRA"
+ + "
Participação: %{r:.2f}%"
+ + "
Setor: %{theta}
",
+ line=dict(color=LINHAS_PREENCHIMENTO_2),
+ )
+ )
+ else:
+ df_total_ibov = (
+ df_ibov.groupby("Setor")["Participação"].sum(numeric_only=True) * 100
+ )
+ fig = go.Figure()
+ fig.add_trace(
+ go.Scatterpolar(
+ r=df_total_ibov,
+ theta=df_total_ibov.index,
+ name="Carteira",
+ fill="toself",
+ hovertemplate="IBOV"
+ + "
Participação: %{r:.2f}%"
+ + "
Setor: %{theta}
",
+ line=dict(color=LINHAS_PREENCHIMENTO_1),
+ )
+ )
+
+ fig.update_traces(line={"shape": "spline"})
+ fig.update_layout(
+ MAIN_CONFIG, # Mantém as configurações principais que você já definiu em MAIN_CONFIG
+ showlegend=True,
+ height=TAMANHO_RADAR,
+ paper_bgcolor="rgba(0,0,0,0)",
+ plot_bgcolor="rgba(0,0,0,0)",
+ autosize=True, # Adiciona a propriedade autosize para tornar o gráfico responsivo
+ margin=dict(l=20, r=20, t=20, b=20), # Ajusta as margens
+ polar=dict(
+ bgcolor=BACKGROUND_RADAR,
+ angularaxis=dict(
+ tickfont_size=TAMANHO_INDICADORES,
+ color=INDICADORES,
+ gridcolor=LINHA_Y,
+ linecolor=LINHA_CIRCULAR_EXTERNA,
+ ),
+ radialaxis=dict(
+ color=AXIS_X_VALUES_COLOR,
+ gridcolor=LINHAS_CIRCULARES,
+ linecolor=LINHA_X,
+ ),
+ ),
+ )
+ return fig
+
+
+# callback para atualizar os cards
+@app.callback(
+ Output("ibov_valor", "children"),
+ Output("ibov_percent", "children"),
+ Output("carteira_valor", "children"),
+ Output("carteira_percent", "children"),
+ Output("cards_ativos", "children"),
+ State("historical_data_store", "data"),
+ Input("period_input", "value"),
+ Input("dropdown_card1", "value"),
+ Input("book_data_store", "data"),
+)
+def update_cards_ativos(historical_data, period, dropdown, book_data):
+
+ if dropdown == None:
+ return no_update
+ if type(dropdown) != list:
+ dropdown = [dropdown]
+ dropdown = ["IBOV"] + dropdown
+
+ df_book = pd.DataFrame(book_data)
+ df_hist = pd.DataFrame(historical_data)
+
+ df_book["datetime"] = pd.to_datetime(df_book["date"], format="%Y-%m-%d %H:%M:%S")
+
+ df2 = df_book.groupby(by=["ativo", "tipo"])["vol"].sum(numeric_only=True)
+
+ diferenca_ativos = {}
+ # agrupa os dados por valores únicos na primeira coluna do índice (level = 0)
+ for ativo, new_df in df2.groupby(level=0):
+ compra, venda = 0, 0
+ try:
+ # salva os valores da linha dos ativos que tem 'compra'
+ compra = new_df.xs((ativo, "Compra"))
+ except:
+ pass
+ try:
+ # salva os valores da linha dos ativos que tem 'venda'
+ venda = new_df.xs((ativo, "Venda"))
+ except:
+ pass
+ diferenca_ativos[ativo] = compra - venda
+
+ # adiciona em um dicionario somente os ativos existentes na carteira
+ ativos_existentes = dict((k, v) for k, v in diferenca_ativos.items() if v >= 0)
+ ativos_existentes["IBOV"] = (
+ 1 # botei 1 pq era só pra adicionar um valor qualquer, o que importa é a chave 'IBOV'
+ )
+
+ if period == "ytd":
+ correct_timedelta = date.today().replace(month=1, day=1)
+ correct_timedelta = pd.Timestamp(correct_timedelta)
+ else:
+ correct_timedelta = date.today() - TIMEDELTAS[period]
+
+ dict_valores = {}
+
+ for key, value in ativos_existentes.items():
+ df_auxiliar = df_hist[df_hist.symbol.str.contains(key)].copy()
+ df_auxiliar["datetime"] = pd.to_datetime(
+ df_auxiliar["datetime"], format="%Y-%m-%d %H:%M:%S"
+ )
+ df_periodo = df_auxiliar[df_auxiliar["datetime"] > correct_timedelta]
+ # import pdb; pdb.set_trace()
+ valor_atual = df_periodo["close"].iloc[-1]
+ diferenca_periodo = valor_atual / df_periodo["close"].iloc[0]
+ dict_valores[key] = valor_atual, diferenca_periodo
+
+ dfativos = (
+ pd.DataFrame(dict_valores)
+ .T.rename_axis("ticker")
+ .add_prefix("Value")
+ .reset_index()
+ )
+ dfativos["Value1"] = dfativos["Value1"] * 100 - 100
+
+ seta_crescendo = [
+ "fa fa-angle-up",
+ "textoQuartenarioVerde",
+ ]
+ seta_caindo = ["fa fa-angle-down", "textoQuartenarioVermelho"]
+
+ lista_valores_ativos = []
+ lista_tags = []
+ for ativo in range(len(dfativos)):
+ tag_ativo = dfativos.iloc[ativo][0]
+ lista_tags.append(tag_ativo)
+ valor_ativo = dfativos.iloc[ativo][1]
+ variacao_ativo = dfativos.iloc[ativo][2]
+ if variacao_ativo < 0:
+ lista_valores_ativos.append(
+ [tag_ativo, valor_ativo, variacao_ativo, seta_caindo[0], seta_caindo[1]]
+ )
+ else:
+ lista_valores_ativos.append(
+ [
+ tag_ativo,
+ valor_ativo,
+ variacao_ativo,
+ seta_crescendo[0],
+ seta_crescendo[1],
+ ]
+ )
+
+ # Graficos
+ df_hist = pd.DataFrame(historical_data)
+ df_hist["datetime"] = pd.to_datetime(
+ df_hist["datetime"], format="%Y-%m-%d %H:%M:%S"
+ )
+ df_hist = slice_df_timedeltas(df_hist, period)
+ df_hist = df_hist[df_hist["symbol"].str.contains("|".join(lista_tags))]
+
+ lista_graficos = []
+ for n, ticker in enumerate(lista_tags):
+ fig = go.Figure()
+ df_aux = df_hist[df_hist.symbol.str.contains(ticker)]
+ df_aux = df_aux.dropna() # Removido o inplace=True
+ df_aux["close"] = (
+ df_aux["close"] / df_aux["close"].iloc[0] - 1
+ ) # Modificado para evitar operação in-place
+
+ fig.add_trace(
+ go.Scatter(
+ x=df_aux.datetime,
+ y=df_aux.close * 100,
+ mode="lines",
+ name=ticker,
+ line=dict(color=CARD_GRAPHS_LINE_COLOR),
+ hoverinfo="skip",
+ )
+ )
+ fig.update_layout(
+ MAIN_CONFIG_3,
+ showlegend=False,
+ paper_bgcolor="rgba(0,0,0,0)",
+ plot_bgcolor="rgba(0,0,0,0)",
+ )
+ fig.update_xaxes(visible=False)
+ fig.update_yaxes(visible=False)
+
+ lista_graficos.append(fig)
+
+ lista_colunas = []
+ for n, ativo in enumerate(lista_valores_ativos):
+ if ativo[0] != "IBOV" and n < 4:
+ col = dbc.Col(
+ [
+ dbc.Card(
+ [
+ dbc.CardBody(
+ [
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ html.Legend(
+ ativo[0],
+ className="textoQuartenario",
+ ),
+ ],
+ md=4,
+ ),
+ dbc.Col(
+ [
+ dcc.Graph(
+ figure=lista_graficos[n],
+ config={
+ "displayModeBar": False,
+ "showTips": False,
+ },
+ className="graph_cards",
+ ),
+ ],
+ md=8,
+ ),
+ ]
+ ),
+ dbc.Row(
+ [
+ dbc.Col(
+ [
+ html.H5(
+ [
+ "R$",
+ "{:,.2f}".format(ativo[1]),
+ " ",
+ ],
+ className="textoTerciario",
+ ),
+ html.H5(
+ [
+ html.I(className=ativo[3]),
+ " ",
+ "{:,.2f}".format(ativo[2]),
+ "%",
+ ],
+ className=ativo[4],
+ ),
+ ]
+ )
+ ]
+ ),
+ ]
+ )
+ ],
+ className="cards_linha2",
+ ),
+ ],
+ md=3,
+ xs=12,
+ )
+
+ lista_colunas.append(col)
+
+ card_ativos = dbc.Row([*lista_colunas])
+
+ valor_ibov = (
+ html.Legend(
+ ["", "{:,.2f}".format(dfativos["Value0"].iloc[-1]), " "],
+ className="textoQuartenario",
+ ),
+ )
+
+ ibov_percentual = dfativos["Value1"].iloc[-1]
+ if ibov_percentual < 0:
+ percent_ibov = html.Legend(
+ [
+ html.I(className=seta_caindo[0]),
+ " ",
+ "{:,.2f}".format(ibov_percentual),
+ "%",
+ ],
+ className=seta_caindo[1],
+ )
+ else:
+ percent_ibov = html.Legend(
+ [
+ html.I(className=seta_crescendo[0]),
+ " ",
+ "{:,.2f}".format(ibov_percentual),
+ "%",
+ ],
+ className=seta_crescendo[1],
+ )
+
+ compra_e_venda = df_book.groupby("tipo")
+
+ df_compra_e_venda = compra_e_venda.sum(numeric_only=True)
+ if "Venda" in compra_e_venda.groups and "Compra" in compra_e_venda.groups:
+ valor_carteira_original = (
+ df_compra_e_venda["valor_total"]["Compra"]
+ - df_compra_e_venda["valor_total"]["Venda"]
+ )
+ elif "Venda" in compra_e_venda.groups and "Compra" not in compra_e_venda.groups:
+ valor_carteira_original = -df_compra_e_venda["valor_total"]["Venda"]
+ elif "Venda" not in compra_e_venda.groups and "Compra" in compra_e_venda.groups:
+ valor_carteira_original = df_compra_e_venda["valor_total"]["Compra"]
+ else:
+ valor_carteira_original = "0.00"
+
+ df_tipo_e_ativo = df_book.groupby(["tipo", "ativo"])
+ df_tipo_e_ativo_soma = df_tipo_e_ativo.sum(numeric_only=True)
+
+ # verificando a quantidade de cada ativo que existe comprada na wallet
+ lista_ativos = df_book["ativo"].unique()
+ resultado = {}
+ for ativo in lista_ativos:
+ try:
+ compra = df_tipo_e_ativo_soma["vol"]["Compra"][ativo]
+ venda = df_tipo_e_ativo_soma["vol"]["Venda"][ativo]
+ if compra > venda:
+ qtd = compra - venda
+ resultado[ativo] = qtd
+ except:
+ try:
+ qtd = df_tipo_e_ativo_soma["vol"]["Compra"][ativo]
+ resultado[ativo] = qtd
+ except:
+ pass
+
+ valor_total = 0
+ for ativo, qtd in resultado.items():
+ for id, ativos in enumerate(lista_valores_ativos):
+ if ativo == ativos[0]:
+ total = qtd * ativos[1]
+ valor_total += total
+
+ if valor_carteira_original == "0.00":
+ valor_carteira_original = 0
+ varicao_carteira = 0
+ varicao_carteira_configurado = 0
+ else:
+ varicao_carteira = valor_total / float(valor_carteira_original)
+ varicao_carteira_configurado = (
+ valor_total / float(valor_carteira_original) * 100 - 100
+ )
+
+ valor_carteira_atual = html.Legend(
+ "R$" + "{:,.2f}".format(valor_carteira_original * varicao_carteira),
+ className="textoSecundario",
+ )
+
+ if varicao_carteira <