semvec/pypi-packages huggingface dataset:
doubo 1.1
Place a buy and a sell order in one go on MetaTrader5
License: MIT
Requires Python: >=3.8

# doubo
Place a buy and a sell order in one go on MetaTrader5.

## Pre-requisite

`doubo` interacts with MetaTrader5 to place orders.
It is required to have MetaTrader5 installed before using `doubo`.

## Installation

`doubo` can be installed using `pip`:

```shell
pip install doubo
```

Note that `doubo` depends on MetaTrader's official Python library, which is only available on Windows x86 platform.

## Usage

As of v1.0, only real time market orders are supported:

```shell
doubo place <SYMBOL> <VOLUME> [--sl <STOP_LOSS_PIPS> | --tp <TAKE_PROFIT_PIPS>]
```

An example command to place 0.01 double-end orders on EUR/USD forex with 50 pips stop loss and doubled take profit:

```shell
doubo place EURUSD 0.01 --sl 50 --tp 100
```

## License

[MIT license](LICENSE)
rfpl 1.0.0
Recursive Functional Programming Language
License: MIT
Requires Python: >=3.9

# RFPL: Recursive Functional Programming Language

RFPL is a recursive functional programming language designed primarily for educational purposes. It introduces the concept of primitive recursion—which you can read about on [Wikipedia](https://en.wikipedia.org/wiki/Primitive_recursive_function)—and can also be used for non-educational purposes, such as for fun or challenges.

## Usage

### Installation

#### Easy Way

Install ANTLR4. Clone the repository and use `make` to run RFPL:

```console
$ git clone https://github.com/AMBandariM/RFPL
$ cd RFPL
$ make          # Launch RFPL's interactive console
$ make journey  # Start "The RFPL Journey"
```

#### Easier Way

Alternatively, you can install RFPL using pip:

```console
$ pip install rfpl
```

Then run:

```console
$ rfpl      # Launch RFPL's interactive console
$ journey   # Start "The RFPL Journey"
$ # or run with `python -m rfpl` and `python -m journey` if there was PATH errors.
```

### VSCode Syntax Highlighter
To enable syntax highlighting for RFPL files in VSCode:

1. Open the Extensions panel.
2. Search for `RFPL`.
3. Install the **RFPL Syntax Highlighter** extension, which provides highlighting for `.rfpl` files.

## Development and Contribution
### Translating "The RFPL Journey"

You are welcome to translate the file `./journey/journey_en.json` into your native language. Submit your translation, if the translation meets our quality standards, we will include it in future releases with your name and contact information.

### Future Development

Although Version 1 of RFPL is complete and published, we have plans for future improvements:
- **Refactoring:** Extracting 'fundamental' functions from the interpreter.
- **User Experience:** Implementing a robust cache and function-guessing system.
- **Dependency Management:** Potentially removing ANTLR and other nonessential dependencies.

### Documentation
You have to experience the language and read the source code. There is no additional documentation at this time—sorry about that.

## Contact Information
- **Parsa Alizadeh** \[[parsa.alizadeh1@gmail.com](mailto:parsa.alizadeh1@gmail.com)\]

- **AmirMohammad Bandari Masoole** \[[ambandarim@gmail.com](mailto:ambandarim@gmail.com)\]
sisense-automl 0.1.7
A package for automating machine learning processes using Sisense and AutoML.
License: MIT
Requires Python: <3.10,>=3.9

# sisense-automl

`sisense-automl` is a Python package for automating machine learning processes using Sisense and AutoML. It provides easy-to-use interfaces for data preprocessing, model training, and evaluation.

## ⚠️ Compatibility Warning
This package requires **Python 3.9** to function correctly. Please ensure you are using the correct Python version.

**Why only Python 3.9?**
- **Auto-sklearn** does not support Python 3.10 or newer.
- **Scikit-learn 0.24.0** (required by Auto-sklearn) works best with Python 3.9.
- **NumPy 1.23+** removed `numpy.distutils`, which some dependencies rely on.

## Features

- Preprocesses data by handling duplicates, splitting numerical and categorical features, and encoding them appropriately.
- Splits data into training and testing sets.
- Uses auto-sklearn for training classification and regression models.
- Saves trained models and datasets for later use.

## Installation

You can install the package using pip:

```sh
pip install sisense-automl
```

## System-Level Dependencies

Before installing the package, make sure you have the following system-level dependencies:

```sh
sudo apt-get install swig -y
```

## Python-Level Dependencies

The package will automatically install the required Python dependencies as specified in `setup.py`.

## Usage

Here is a basic example of how to use the 'sisense-automl' package:

```python
import pandas as pd
from sisense_automl import AutoMl

# Load your dataset
data = pd.read_csv("your_dataset.csv")

# Define your target column and objective
target_column = "target"
objective = "classification"  # or "regression"

# Define the folder path to save models and data
folder_path = "your_folder_path"

# Create an AutoMl instance and run the process
automl = AutoMl(data, target_column, objective, folder_path)
```

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

## Author
Himanshu Negi - himanshu.negi.08@gmail.com

## Contributing

Pull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.

Please make sure to update tests as appropriate.