semvec/pypi-packages huggingface dataset:
microcrypta 0.1.1
A micro package for cryptocurrency technical analysis
Requires Python: >=3.6

# microcrypta

`microcrypta` is a Python package for cryptocurrency technical analysis. It provides tools for fetching market data, performing technical analysis, and backtesting trading strategies.

## Installation

To install the package, run:

```sh
pip install microcrypta
```

## Usage

Here is an example of how to use the `microcrypta` package:

```python
# example_usage.py

from microcrypta.coin import Coin
from microcrypta.account import correlation_matrix, Account
from microcrypta.coinmarket import CoinMarket
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from microcrypta.bsh import BSH
from microcrypta.backtest import Backtest
import microcrypta.config as config

# Initialize coins
corr_coins = ['BTC', 'ETH','SHIB','ADA']
correlation_matrix(corr_coins,time_frame='4h')# Create a market object
market = CoinMarket(btc, usdt, "1h")

coin = Coin('REQ')
usd = Coin('BTC')
coin_usd = CoinMarket(coin,usd,time_frame = "1h")
bshs = BSH(coin_usd)

# Create an account object
account = Account(1000, 1)

# Generate buy/sell/hold signals using the moving average strategy
signal = bsh.moving_average()

# Backtest the strategy
backtest = Backtest(market, signal, "Moving Average")
position_size, coin_count, percent_gain = backtest.backtest(plot=True)

print(f"Position Size: {position_size}")
print(f"Coin Count: {coin_count}")
print(f"Percent Gain: {percent_gain:.2f}%")
```

## Features

- Fetch market data from various cryptocurrency exchanges using `ccxt`.
- Perform technical analysis with indicators like Moving Averages, RSI, MACD, Bollinger Bands, etc.
  ![Visualization of Aaron Oscillator](imgs/corr_mat.png)
- Backtest trading strategies based on technical indicators.
  ![Visualization of backtesting](imgs/signal.png)
- Visualize data and analysis results using `matplotlib`.
  ![Visualization of Aaron Oscillator](imgs/aaron.png)

## Dependencies

- `ccxt`
- `pandas`
- `numpy`
- `matplotlib`
- `mplfinance`
- `seaborn`
- `tqdm`

## License

This project is licensed under the MIT License.

## Acknowledgements

Special thanks to the developers of `ccxt`, `pandas`, `numpy`, `matplotlib`, `mplfinance`, `seaborn`, and `tqdm`.

## Project Structure

```console
microcrypta/
│
├── microcrypta/
│ ├── init.py
│ ├── config.py
│ ├── coin.py
│ ├── coinmarket.py
│ ├── account.py
│ ├── bsh.py
│ └── backtest.py
│
├── setup.py
├── README.md
└── LICENSE
```
ichthyop 1.1.0
Python package for the analysis of Ichthyop outputs
Keywords: ocean; grid model; transport; lagrangian; larval dispersion; ichthyoplankton

![License](https://img.shields.io/github/license/ichthyop/ichthyop-python)
[![DOI](https://zenodo.org/badge/286680975.svg)](https://zenodo.org/badge/latestdoi/286680975)
[![PyPI](https://img.shields.io/pypi/v/ichthyop)](https://pypi.org/project/ichthyop/)
[![Latest Release](https://img.shields.io/github/release/ichthyop/ichthyop-python)](https://github.com/ichthyop/ichthyop-python/releases)
[![GitHub Issues](https://img.shields.io/github/issues/ichthyop/ichthyop-python)](https://github.com/ichthyop/ichthyop-python/issues)

<div align="center">
    <img src="https://avatars1.githubusercontent.com/u/69504072?s=400&u=8766ef42c78a1fbf1930e756d7b9e530dec69d19&v=4" width=100>
</div>

# Ichthyop

Ichthyop (http://ichthyop.org/) is a free Java tool designed to study the effects of physical and biological factors on ichthyoplankton dynamics (code available [here](https://github.com/ichthyop/ichthyop)).

This python package is dedicated to the analysis of Ichthyop's outputs.
- Plots trajectories (as a function of time, depth, etc.)
- Make animations
- Connectivity calculations
dataguard 1
A package for automating data quality and integrity checks with optional GPU acceleration using cuDF
Requires Python: >=3.6

# Data Quality Checker

Data Quality Checker is a Python package that automates data quality and integrity checks for your dataset. It performs several checks including missing values, duplicate rows, outliers, data type validation, and range validation. The package uses cuDF for GPU acceleration if a compatible GPU is available, and falls back to Dask for parallel processing otherwise.

## Installation

You can install the package via pip:

```bash
pip install data_quality_checker