semvec/pypi-packages huggingface dataset:
awt-ti 0.1.0
A comprehensive library for technical analysis indicators in financial markets
License: MIT
Requires Python: >=3.11
Keywords: finance, technical-analysis, trading, indicators, stocks

# Technical Indicators

A comprehensive Python library for technical analysis indicators in financial markets.

## Features

- Support and Resistance Indicators
  - Pivot Points (Floor Trader's Method)
  - Fibonacci Retracements
  - Support/Resistance Level Detection
  - Maximum Drawdown Analysis
- Moving Averages
- Volume Indicators
- Momentum Indicators
- Trend Indicators

## Installation

Using pip:
```bash
pip install technical-indicators
```

Using Poetry:
```bash
poetry add technical-indicators
```

## Quick Start

```python
import pandas as pd
from Technical_Analysis.Indicators.supportResistance import calculate_pivot_points

# Create or load your price data
df = pd.DataFrame({
    'High': [105, 104, 106],
    'Low': [98, 97, 99],
    'Close': [102, 101, 103]
})

# Calculate pivot points
pivots = calculate_pivot_points(df)
print(pivots)
```

## Documentation

Full documentation is available at [https://technical-indicators.readthedocs.io](https://technical-indicators.readthedocs.io)

### Support and Resistance Indicators

The library provides various methods to identify potential support and resistance levels:

```python
from Technical_Analysis.Indicators.supportResistance import (
    find_support_resistance,
    fibonacci_retracement,
    calculate_pivot_points
)

# Find support and resistance levels
levels = find_support_resistance(df)

# Calculate Fibonacci retracements
fib_levels = fibonacci_retracement(df)

# Calculate pivot points
pivots = calculate_pivot_points(df)
```

## Development

This project uses Poetry for dependency management. To set up the development environment:

1. Install Poetry:
```bash
curl -sSL https://install.python-poetry.org | python3 -
```

2. Clone the repository:
```bash
git clone https://github.com/yourusername/TechnicalIndicators.git
cd TechnicalIndicators
```

3. Install dependencies:
```bash
poetry install
```

4. Run tests:
```bash
poetry run pytest
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
eagle-exporter 0.1.4
CLI tool and library for exporting metadata from Eagle app JSON into parquet or huggingface dataset.
License: MIT
Requires Python: <4.0,>=3.10

# Eagle Exporter

**Eagle Exporter** is a command-line utility (and Python library) to parse image metadata JSON files from an [Eagle](https://en.eagle.cool/) library directory and export them to either a local Parquet file or a Hugging Face Dataset repository.

## Features

- Recursively scans an Eagle library's `images/` folder for JSON files.
- Extracts tags, star ratings, palette info, etc.
- Optionally merges with [s5cmd](https://github.com/peak/s5cmd) logs to provide S3 URIs for each file.
- **NEW:** Can include the actual image data when exporting to Hugging Face datasets.
- Exports to either:
  - A local `.parquet` file  
  - A Hugging Face dataset (can be public or private).

## Installation

```bash
pip install eagle-exporter
```

## Usage

Example command to export an Eagle library to Hugging Face:

```bash
eagle-exporter path/to/my_eagle.library --to myuser/my_hf_dataset --hf-public
```

Export to Hugging Face with image data included:

```bash
eagle-exporter path/to/my_eagle.library --to myuser/my_hf_dataset --hf-public --include-images
```

Or export to a local `.parquet` file:

```bash
eagle-exporter path/to/my_eagle.library --to /tmp/output.parquet
```

Optionally include an s5cmd file to attach S3 URIs:

```bash
eagle-exporter path/to/my_eagle.library --s5cmd /path/to/s5cmd.txt --to /tmp/output.parquet
```

### Command-Line Arguments

- `EAGLE_DIR` (positional): The path to your Eagle library directory (the folder that has `images/` inside).

- `--s5cmd <FILE>` (optional): Path to the s5cmd log file (with lines like `cp localfile s3://bucket/...`)

- `--to <DEST>` (required):
  - If `<DEST>` ends with `.parquet`, will export to Parquet.
  - Otherwise, treats `<DEST>` as a Hugging Face dataset name (e.g. `username/datasetname`).
  
- `--hf-public` (optional): If exporting to Hugging Face, mark it as public.

- `--include-images` (optional): Include the actual image files in the export. This is particularly useful for Hugging Face exports. Note that binary image data will be excluded from Parquet exports.

- `--help`: Show the help message.

## Developer Notes

- The core functionality resides in `src/eagle_exporter/core.py`.
- The CLI is in `src/eagle_exporter/cli.py`.
- The library uses `click` for the command line, `pandas` for data manipulation, and `datasets` for pushing to Hugging Face.
- Image processing is handled using Pillow (PIL).

### Building and publishing

To build and publish the repo, run the following commands:

```bash
python -m pip install build twine
python -m build
twine check dist/*
twine upload dist/*
```