cvmpy 0.1.0

Package to read and process data from the CVM website.

CVM Data Reader

Overview

The CVM Data Reader is a powerful tool designed to read and process datasets from the Comissão de Valores Mobiliários (CVM). It simplifies access to financial information, making it easier for users to conduct research and financial analysis. The project supports the following data categories:

• Companhias: ITR (Quarterly Information), DFP (Financial Statements), and more.
• Fundos de Investimento (FI): Informe Diário, Composição e Diversificação de Aplicações.
• Fundos de Investimento (FII): Demonstrações Financeiras, Informe Mensal, and more.

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Installation

To install the package via pip, run the following command:

pip install cvm

Usage

Example: Fetching FI Static and Historical Data

To fetch data, you can use the following methods:

• fetch_historical_data(dataset_name, start_date, end_date) for panel datasets.
• fetch_static_data(dataset_name) for static datasets.

Available Datasets

• cadastro (registration)
• extrato (statement)
• registro_fundo_classe (fund class registration)
• informe_diario (daily report)
• composicao_diversificacao (composition and diversification)
• balancete (trial balance)
• perfil_mensal (monthly profile)

import cvm

# Create an instance of FI datasets
fi = cvm.FI()

# Fetch static data (e.g., cadastro)
fi.fetch_static_data("cadastro")

# Fetch historical data (e.g., informe_diario)
fi.fetch_historical_data("informe_diario", "2024-11-05", "2024-12-23")

The fetched datasets become attributes of the FI instance. For example:

# Display the first few rows of Informe Diário data
print(fi.informe_diario.inf_diario_fi.head())

Handling Large Data with Parsers

For long historical data, memory issues may arise. In such cases, you can apply a parser function to filter data as it is being read. Here’s an example of filtering by specific CNPJs:

list_cnpjs = [
    "37.916.879/0001-26",  # DYNAMO COUGAR MASTER FIA
    "11.188.572/0001-62",  # ATMOS MASTER FIA
    "06.964.937/0001-63",  # OPPORTUNITY SELECTION MASTER FIA
]

# Fetch data with a parser function to filter by CNPJ
fi.fetch_historical_data(
    "composicao_diversificacao",
    "2024-01-31",
    "2024-03-31",
    parser=lambda df: (
        df[df["CNPJ_FUNDO_CLASSE"].isin(list_cnpjs)]
        if "CNPJ_FUNDO_CLASSE" in df.columns
        else df
    ),
)

In this example, only the specified CNPJs are retained in the dataset.

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Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a new branch: git checkout -b feature/your-feature-name.
3. Commit changes: git commit -m "Add your feature".
4. Push to your branch: git push origin feature/your-feature-name.
5. Open a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contact

If you encounter issues or have feedback, feel free to open an issue or reach out via the repository’s discussions tab.