Símbolo	Empresa	MAPE	R²	Acurácia Dir.	Avaliação
MSFT	Microsoft	3.47% ⭐	0.83	54.0%	Excelente
V	Visa	3.72% ⭐	-0.77	50.0%	Excelente
TSLA	Tesla	5.61%	0.88	46.6%	Muito Bom
GOOGL	Alphabet	7.36%	0.85	55.7%	Muito Bom
NVDA	NVIDIA	7.50%	0.81	46.0%	Muito Bom
META	Meta	7.60%	0.42	55.7%	Bom
AAPL	Apple	8.28%	0.04	52.3%	Bom
JPM	JPMorgan	10.42%	-0.28	49.4%	Aceitável
AMZN	Amazon	11.61%	-1.32	51.7%	Aceitável

Interpretação

MAPE < 5%: Excelente (MSFT, V)
MAPE 5-10%: Bom (TSLA, GOOGL, NVDA, META, AAPL)
R² > 0.8: Modelo captura bem a variância (TSLA, GOOGL, MSFT, NVDA)

🏗️ Arquitetura do Modelo

Features de Entrada (16)

Categoria	Features
Preços	open, high, low, close
Volume	volume, volume_ma_7
Médias Móveis	ma_7, ma_30, ma_90
Volatilidade	volatility_7, volatility_30
Momentum	momentum, roc_7, roc_30
Variação	price_change, pct_change

📁 Arquivos Disponíveis

Modelos (.pth)

Arquivo	Tamanho	Descrição
	~3.3 MB	Modelo Apple
	~3.3 MB	Modelo Alphabet
	~3.3 MB	Modelo Microsoft
	~3.3 MB	Modelo Amazon
	~3.3 MB	Modelo Meta
	~3.3 MB	Modelo NVIDIA
	~3.3 MB	Modelo Tesla
	~3.3 MB	Modelo JPMorgan
	~3.3 MB	Modelo Visa

Preprocessors (.pkl)

Arquivo	Descrição
	MinMaxScaler para cada símbolo

Metadata (.json)

Arquivo	Descrição
	Hiperparâmetros e métricas

🚀 Como Usar

Instalação

Requirement already satisfied: torch in ./venv/lib/python3.14/site-packages (2.9.1) Requirement already satisfied: huggingface_hub in ./venv/lib/python3.14/site-packages (1.1.7) Requirement already satisfied: scikit-learn in ./venv/lib/python3.14/site-packages (1.7.2) Requirement already satisfied: pandas in ./venv/lib/python3.14/site-packages (2.3.3) Requirement already satisfied: numpy in ./venv/lib/python3.14/site-packages (2.3.5) Requirement already satisfied: filelock in ./venv/lib/python3.14/site-packages (from torch) (3.20.0) Requirement already satisfied: typing-extensions>=4.10.0 in ./venv/lib/python3.14/site-packages (from torch) (4.15.0) Requirement already satisfied: setuptools in ./venv/lib/python3.14/site-packages (from torch) (80.9.0) Requirement already satisfied: sympy>=1.13.3 in ./venv/lib/python3.14/site-packages (from torch) (1.14.0) Requirement already satisfied: networkx>=2.5.1 in ./venv/lib/python3.14/site-packages (from torch) (3.6) Requirement already satisfied: jinja2 in ./venv/lib/python3.14/site-packages (from torch) (3.1.6) Requirement already satisfied: fsspec>=0.8.5 in ./venv/lib/python3.14/site-packages (from torch) (2025.10.0) Requirement already satisfied: hf-xet<2.0.0,>=1.2.0 in ./venv/lib/python3.14/site-packages (from huggingface_hub) (1.2.0) Requirement already satisfied: httpx<1,>=0.23.0 in ./venv/lib/python3.14/site-packages (from huggingface_hub) (0.28.1) Requirement already satisfied: packaging>=20.9 in ./venv/lib/python3.14/site-packages (from huggingface_hub) (25.0) Requirement already satisfied: pyyaml>=5.1 in ./venv/lib/python3.14/site-packages (from huggingface_hub) (6.0.3) Requirement already satisfied: shellingham in ./venv/lib/python3.14/site-packages (from huggingface_hub) (1.5.4) Requirement already satisfied: tqdm>=4.42.1 in ./venv/lib/python3.14/site-packages (from huggingface_hub) (4.67.1) Requirement already satisfied: typer-slim in ./venv/lib/python3.14/site-packages (from huggingface_hub) (0.20.0) Requirement already satisfied: anyio in ./venv/lib/python3.14/site-packages (from httpx<1,>=0.23.0->huggingface_hub) (4.12.0) Requirement already satisfied: certifi in ./venv/lib/python3.14/site-packages (from httpx<1,>=0.23.0->huggingface_hub) (2025.11.12) Requirement already satisfied: httpcore==1.* in ./venv/lib/python3.14/site-packages (from httpx<1,>=0.23.0->huggingface_hub) (1.0.9) Requirement already satisfied: idna in ./venv/lib/python3.14/site-packages (from httpx<1,>=0.23.0->huggingface_hub) (3.11) Requirement already satisfied: h11>=0.16 in ./venv/lib/python3.14/site-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface_hub) (0.16.0) Requirement already satisfied: scipy>=1.8.0 in ./venv/lib/python3.14/site-packages (from scikit-learn) (1.16.3) Requirement already satisfied: joblib>=1.2.0 in ./venv/lib/python3.14/site-packages (from scikit-learn) (1.5.2) Requirement already satisfied: threadpoolctl>=3.1.0 in ./venv/lib/python3.14/site-packages (from scikit-learn) (3.6.0) Requirement already satisfied: python-dateutil>=2.8.2 in ./venv/lib/python3.14/site-packages (from pandas) (2.9.0.post0) Requirement already satisfied: pytz>=2020.1 in ./venv/lib/python3.14/site-packages (from pandas) (2025.2) Requirement already satisfied: tzdata>=2022.7 in ./venv/lib/python3.14/site-packages (from pandas) (2025.2) Requirement already satisfied: six>=1.5 in ./venv/lib/python3.14/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0) Requirement already satisfied: mpmath<1.4,>=1.1.0 in ./venv/lib/python3.14/site-packages (from sympy>=1.13.3->torch) (1.3.0) Requirement already satisfied: MarkupSafe>=2.0 in ./venv/lib/python3.14/site-packages (from jinja2->torch) (3.0.3) Requirement already satisfied: click>=8.0.0 in ./venv/lib/python3.14/site-packages (from typer-slim->huggingface_hub) (8.3.1)

Download e Uso

Fazer Previsão

📊 Treinamento

Configuração

Por que dados pós-COVID?

O crash de Março de 2020 foi um evento atípico que criava vieses nos modelos. Treinar com dados a partir de 2021 resultou em:

MAPE: 17% → 7% (59% de melhoria)
R² positivo: 1/3 → 6/9
Acurácia direcional: Média de 52%

🔗 Links

Código Fonte: GitHub - previsao_acoes
API Demo: Railway (em produção)
Documentação: no repositório

📄 Licença

MIT License - Uso livre para fins educacionais e comerciais.

👥 Autores

Tech Challenge Fase 4 - FIAP Pós-Tech ML Engineering

📈 Métricas Visuais

Última atualização: Dezembro 2024

henriquebap/stock-predictor-lstm

📈 Stock Price Predictor - LSTM Models

🎯 Overview

Principais Características

📊 Performance dos Modelos

Tabela de Métricas