Metadata-Version: 2.1
Name: om-quant-fin
Version: 0.1.0
Summary: A simple quantitative trading library for the OMNP Class
Home-page: UNKNOWN
Author: Outspoken Market
Author-email: info@outspokenmarket.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Requires-Dist: yfinance
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: numpy

# OM Quant Lib

OM Quant Lib is a simple Python library for quantitative trading analysis. It provides functionality for downloading stock data, calculating various indicators, and running a logistic regression model with AUC and Gini metrics for model evaluation.

## Project Structure

om_quant/                   # Diretório raiz do projeto
├── om_quant /              # Python package
│   ├── __init__.py         # Marca o diretório como um package
│   └── om_quant.py         # Contém as funções da sua lib
├── setup.py                # Fornece os metadados do package bem como suas dependências
├── .gitignore              # Lista os arquivos e pastas que não precisam ser registradas no git
└── README.md               # Arquivo Markdown com a descrição do projeto, exemplos e outras informações


## Features

- Download stock data from Yahoo Finance
- Calculate rolling Z-scores
- Calculate rolling ratio of adjusted close and its mean
- Calculate returns
- Split data into training and test sets
- Run a logistic regression model

## Installation

Install the library using pip:

pip install om_quant

## Usage

import my_quant_lib as mql
import my_quant_lib as mql

#Download stock data:
data = mql.download_data("AAPL", "2020-01-01", "2022-12-31")

#Calculate rolling Z-score:
z_score = mql.rolling_z_score(data["Adj Close"], window=20)

#Calculate rolling ratio:
ratio = mql.rolling_ratio(data["Adj Close"], window=20)

#Calculate returns:
returns = mql.calculate_returns(data["Adj Close"])

#Split data into training and test sets:
train, test = mql.split_data(data, "2020-01-01", "2021-12-31", "2022-01-01", "2022-12-31")

#Run a logistic regression model:
predictions = mql.run_logistic_regression(X_train, y_train, X_test)

## License

This project is licensed under the MIT License.

This markdown file provides an overview of your project, its structure, features, installation, and usage instructions. It's a good starting point for users who want to learn about your library and how to use it. You can also include additional information, such as code examples, detailed explanations of the functions, and any other relevant information that you'd like to share.


