semvec/pypi-packages huggingface dataset:
basecmd 0.1.6
Logging boilerplate for the command line
Requires Python: >=3.7

# basecmd

Boilerplate for the command line.

Classes inheriting from `BaseCmd` have a `self.log` attribute
that is a standard Python logger. A basic logging configuration to `sys.stdout`
is provided.

For command line options controlling the logging verbosity
and output to a log file, call the command with `-h` or `--help`.

Defaults for logging options can be also provided as environment variables
or in a `.emv` file:

* `LOG_LEVEL`: the logging verbosity, one of `error`, `warn`, `info`, or `debug`; default: `info`.
' `LOG_FILE`: path to a log file, defaults to the standard output for easy redirection.
* `LOG_FORMAT`: a standard Python logging format, defaults to `%(asctime).19s  %(message)s` when logging to a file or a terminal and `%(message)s` otherwise.

When logging to a terminal, the output is colored by log level.

## Example usage

```python
from basecmd import BaseCmd

class MyCmd(BaseCmd):

    def add_arguments(self):
        self.parser.add_argument('--foo',
            help='Custom command line option')

    def __call__(self):
        self.log.debug(self.options.foo)

if __name__ == '__main__':
    cmd = MyCmd
    cmd()
```
quantfeat 0.1.0
A data analytics, feature engineering library for time series price data
Requires Python: >=3.8


# QuantFeat

QuantFeat is a Python package for quantitative financial analysis, including EDA, returns, volatility, and data conversion utilities for time series data.

## Installation

Install the latest release from PyPI:

```bash
pip install quantfeat
```

## Usage

Import the modules you need:

```python
from quantfeat import eda, returns, volatility, convert_data
```

### Data Resampling

Convert raw data to a desired frequency (e.g., hourly):

```python
import pandas as pd
data = pd.read_csv("your_data.csv")
df = convert_data.resample(data, "1H", start="2020-01-01", end="2025-08-01")
```

### Exploratory Data Analysis (EDA)

Profile your dataset and get price/volume/returns stats:

```python
eda.profile(df)
eda.price_stats(df)
eda.returns(df)
eda.volume_stats(df)
```

Generate plots and heatmaps:

```python
eda.plot_time_series(df)
eda.corr_heatmap(df)
```

Run a full EDA pipeline (with summary and plots):

```python
eda.perform_quantitative_eda(df)
```

### Returns Calculation

Compute simple/log/lagged/rolling returns:

```python
from quantfeat import returns
returns.simple_returns(df)
returns.log_returns(df)
returns.lagged_returns(df, n=5)
returns.rolling_returns(df, window=20)
```

### Volatility Estimation

Estimate volatility using various models:

```python
from quantfeat import volatility
volatility.realized_volatility(df, window=20)
volatility.parkinson_volatility(df, window=20)
volatility.garman_klass_volatility(df, window=20)
volatility.rogers_satchell_volatility(df, window=20)
volatility.yang_zhang_volatility(df, window=20)
```

### Data Validation

Check and standardize your data:

```python
from quantfeat import validate
validate.validate_and_standardize(df)
```

## Documentation

See function docstrings or source for detailed arguments and options.

## License

MIT
ifunnel 0.1.0
Backend for the investment-funnel
Requires Python: >=3.10

# [funnel](https://tschm.github.io/funnel)

[![PyPI version](https://badge.fury.io/py/funnel.svg)](https://badge.fury.io/py/funnel)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE.txt)
[![CI](https://github.com/tschm/funnel/actions/workflows/ci.yml/badge.svg)](https://github.com/tschm/funnel/actions/workflows/ci.yml)
[![Coverage Status](https://coveralls.io/repos/github/tschm/funnel/badge.svg?branch=main)](https://coveralls.io/github/tschm/funnel?branch=main)
[![Created with qCradle](https://img.shields.io/badge/Created%20with-qCradle-blue?style=flat-square)](https://github.com/tschm/package)
[![CodeFactor](https://www.codefactor.io/repository/github/tschm/funnel/badge)](https://www.codefactor.io/repository/github/tschm/funnel)

## Getting Started

For later...

### **Set Up Environment**

```bash
task build:install
```

This installs/updates [uv](https://github.com/astral-sh/uv),
creates your virtual environment and installs dependencies.

For adding or removing packages:

```bash
uv add/remove requests  # for main dependencies
uv add/remove requests --dev  # for dev dependencies
```

### **Configure Pre-commit Hooks**

```bash
task quality:fmt
```

Installs hooks to maintain code quality and formatting.

### **Update Project Info**

- Edit `pyproject.toml` to update authors and email addresses

## Development Commands

```bash
task docs:tests   # Run test suite
task docs:marimo  # Start Marimo notebooks
```

## Contributing

- Fork the repository
- Create your feature branch (git checkout -b feature/amazing-feature)
- Commit your changes (git commit -m 'Add some amazing feature')
- Push to the branch (git push origin feature/amazing-feature)
- Open a Pull Request