semvec/pypi-packages huggingface dataset:
PypptRoadSign 0.1.1
QXWZ Power Point Road Sign Project
License: MIT
Requires Python: >=3.6
Keywords: Roadsign,PowerPoint,Genertor

# PypptRoadSign

 QXWZ Power Point Road Sign Project

# Very Basic Usage
from PypptRoadSign.sign import Sign

test_sign = Sign(background_style='default',width=16,height=9)

test_sign.background.draw()

test_sign.output('output.pptx')
tanji 1.0.3
Tanji is a Python encryption library that provides a secure and flexible solution for encrypting and decrypting messages. It leverages both symmetric and asymmetric encryption techniques to ensure the confidentiality and integrity of your data.
License: MIT
Requires Python: >=3.11,<4.0

# Tanji Encryption Library 👩‍💻🔐

Tanji is a Python encryption library that provides a secure and flexible solution for encrypting and decrypting messages. It leverages both symmetric and asymmetric encryption techniques to ensure the confidentiality and integrity of your data.

## Features 🚀

- **Symmetric Encryption:** Utilizes the Advanced Encryption Standard (AES) algorithm for symmetric key encryption.
- **Asymmetric Encryption:** Employs RSA algorithm for secure asymmetric key encryption.
- **Base64 Encoding:** Efficiently encodes encrypted data using Base64 for safe transmission.
- **Key Pair Generation:** Automatically generates RSA key pairs for secure communication.
- **Random Initialization Vectors (IV):** Uses random IVs to enhance security.
- **Ease of Use:** Simple and straightforward interface for encrypting and decrypting messages.

## How Tanji Differs 🤔

- **Robust Security:** Tanji prioritizes the security of your data by combining symmetric and asymmetric encryption methods.
- **Key Pair Generation:** Automatically generates and manages RSA key pairs, simplifying the encryption process.
- **Dynamic Initialization Vectors:** Randomly generated IVs for each encryption enhance the resistance against cryptographic attacks.
- **Base64 Encoding:** Encoded data ensures compatibility and safe transmission across different systems.
- **Developer-Friendly:** Designed to be user-friendly and easily integrated into various Python projects.

## Getting Started 🏁

1. Install Tanji:

    ```bash
    pip install tanji
    ```

2. Use Tanji in your Python project:

    ```python
    from tanji.tanji import Tanji

    # Example Usage
    tanji = Tanji()
    encrypted_message, ciphertext = tanji.encrypt_message("Hello, Tanji!")
    decrypted_message = tanji.decrypt_message(encrypted_message, ciphertext)

    print("Original Message:", "Hello, Tanji!")
    print("Encrypted Message:", encrypted_message)
    print("Decrypted Message:", decrypted_message)
    ```

## Contributing 🤝

Contributions are welcome! Feel free to submit issues or pull requests.

## License 📄

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
cryptofeed-experiments 0.1.7
WebSocket subclasses, and experiments, for data from cryptocurrency exchanges
License: MIT
Requires Python: >=3.8,<4.0
Keywords: cryptocurrency,trading

# What?

This is the basis of a pipeline for live data from cryptocurrency exchanges. It normalizes [cryptofeed](https://github.com/bmoscon/cryptofeed) WebSocket messages, aggregates, and optionally publishes them to GCP Pub/Sub.

# How?

Sequences of trades that have equal symbol, timestamp, and tick rule are aggregated. Aggregating trades in this way can increase information, as they are either orders of size or stop loss cascades.

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

By filtering aggregated messages, for example only emitting a mesage when an aggregated trade is greater than or equal to a `significant_trade_filter`, the number of messages can be reduced more.

Example
-------
The following are two sequential aggregated trades by timestamp, nanoseconds, and tick rule.

As it was aggregated from 4 raw trades, the second trade has ticks 4.

```python
[
    {
        "timestamp": 1620000915.31424,
        "price": "57064.01",
        "volume": "566.6479018604",
        "notional": "0.00993004",
        "tickRule": -1,
        "ticks": 1
    },
    {
        "timestamp": 1620000915.885381,
        "price": "57071.2",
        "volume": "9376.6869202914",
        "notional": "0.16429813",
        "tickRule": 1,
        "ticks": 4
    }
]
```

An example filtered message, emitted because the second aggregated trade exceeds `significant_trade_filter >= 1000`

Information related to the first trade is aggregated with the second.

```python
[
    {
        "timestamp": 1620000915.885381,
        "price": "57071.2",
        "volume": "9376.6869202914",
        "notional": "0.16429813",
        "tickRule": 1,
        "ticks": 4,
        "high": '57071.2',
        "low": '57064.01',
        "totalBuyVolume": "9376.6869202914",
        "totalVolume": "9943.3348221518",
        "totalBuyNotional": "0.16429813",
        "totalNotional": "0.17422817",
        "totalBuyTicks": 4,
        "totalTicks": 5
    }
]
```

For 1m, 5m, 15m candles, there is an optional parameter `window_seconds`.  

For settings, see the [examples](https://github.com/globophobe/cryptofeed-experiments/blob/main/examples/)

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

:white_check_mark: Binance

:white_check_mark: Bitfinex

:white_check_mark: Bitflyer

:white_check_mark: BitMEX

:white_check_mark: Bybit

:white_check_mark: Coinbase Pro

:white_check_mark: Upbit

Contributing
------------

Install dependencies with `poetry install`. The docker example is built with [invoke tasks](https://github.com/globophobe/cryptofeed-experiments/blob/master/tasks.py). For example, `invoke build`

Future plans
------------
Order book aggregation, in the manner of [crypto-whale-watching-app](https://github.com/pmaji/crypto-whale-watching-app)