semvec/pypi-packages huggingface dataset:
www-robot 1.0.2
A WWW Robot library for Python
License: MIT
Keywords: www,robot,python,scraping

# www_robot
A WWW Robot library, made for Python.

## Installation
```sh
pip install www_robot
```

## Usage
```python
import www_robot
```

## Contributing
Please feel free to fork and contribute!

## License
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
CabFareByKD 0.0.2
Cab Fare Calculator
License: MIT
Keywords: calculator

This is a cab fare calculator that takes four arguments and return the prospective fare.

The four arguments are as follows:
- City name (string e.g., 'San Francisco')
- Journey starting time (string, e.g. '13:40:07')
- Journey starting date (string, e.g. '2017-11-14')
- Journey duration in hours (int/float e.g., 4/4.5 )

Change Log
==========

0.0.1 (14-08-2022)
------------------
- First Release
carnot 0.1.1
Module for defining reversible transactions.
License: MIT
Requires Python: >=3.8,<4.0

# carnot

`carnot` is a module that enables reversible transactions for general purposes. Reversible transactions may make your codes simpler and have better readability inspite of complicated logics.

## Installing

- Python >= 3.8

```
python -m pip install carnot
```

## Usage

```python
from carnot import reversible_function, transaction

count = 0

@reverse_function
def add(num: int) -> None:
    global count
    count += num
    add.set_args(num)

@add.backward
def _add(num: int) -> None:
    global count
    count -= num

@transaction
def add_and_emit_error() -> None:
    add(2)
    raise Exception

if __name__ == "__main__":
    try:
        add_and_emit_error()
    except:
        pass
    finally:
        print(count)    # 0
```
rstcheck-sphinx 1.0.0
Builder for the sphinx documentation generator to check the source with rstcheck.
License: MIT
Requires Python: >=3.7,<4.0
Keywords: restructuredtext,rst,linter,static-analysis

===============
rstcheck-sphinx
===============

Builder extension for the sphinx documentation generator to check the source with rstcheck.

**This project is under heavy development and not yet ready for use!**

**The package on PyPI is empty.**
django-quant-werks 0.1.0
Django Quant Werks collects and aggregates tick data from financial exchanges
License: MIT
Requires Python: >=3.8,<4.0
Keywords: django,cryptocurrency,candles,trading

# What?

Django Quant Werks collects and aggregates tick data from financial exchanges. 

Raw ticks are aggregated if equal symbol, timestamp, nanoseconds and tick rule. As described in the accompanying project [cryptofeed-werks](https://github.com/globophobe/cryptofeed-werks), aggregating trades in this way can increase information, as they are either orders of size or stop loss cascades.

As well, the number of rows can be reduced by 30-50%

By filtering aggregated rows, for example only writing a row when an aggregated trade is greater than `min_volume >= 1000`, the number of rows can be reduced more.


# Why?

Candlesticks aggregated by `django-quant-werks` are informationally dense. Such data can be useful for analyzing financial markets. As an example, refer to ["Low-Frequency Traders in a High-Frequency World: A Survival Guide"](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2150876) and ["The Volume Clock: Insights into the High Frequency Paradigm"](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2034858). Lopez de Prado recommends volume bars, however they are are computationally expensive to generate.

By filtering and aggregating trades, they can be computed faster, with little loss in precision.

# How?

Whenever possible, data is downloaded from the exchange's AWS S3 repositories. Otherwise, it is downloaded using their REST APIs. 

A database, preferably PostgreSQL, is required. Data is saved to the database after aggregation and filtering. 

Candles are aggregated at 1 minute intervals, and validated with the exchange's historical candle API.

[Notes](https://github.com/globophobe/django-quant-werks/blob/main/NOTES.md).


Supported exchanges
-------------------

:white_medium_square: Binance REST API (requires API key)

:white_check_mark: Bitfinex REST API

:white_check_mark: BitMEX REST API, and [S3](https://public.bitmex.com/) repository

:white_check_mark: Bybit [S3](https://public.bybit.com/) repository

:white_check_mark: Coinbase Pro REST API

:white_check_mark: FTX REST API

Note: Exchanges without paginated REST APIs or an S3 repository, will never be supported.

For deployment, there are Dockerfiles. As well there are invoke tasks for rapid deployment to Google Cloud Run.


Installation
------------

For convenience, `django-quant-werks` can be installed from PyPI:

```
pip install django-quant-werks
```

Environment
-----------

To use the scripts or deploy to GCP, rename `.env.sample` to `.env`, and add the required settings.


Future Plans
------------

Allow use as an aggr historical data source as decribed by [aggr](https://github.com/Tucsky/aggr#implement-historical-data).