semvec/pypi-packages huggingface dataset:
aws-recommendation-a16 0.1.0
Provides AWS recommendations
Requires Python: >=3.7

# aws_recommendations

aws_recommendations package gives recommendation for you aws account

Release Notes:
# v0.0.28
1. Bug fixes and stability improvements

# v0.0.10
1. Added methods for following recommendations:
   - Low Utilization Amazon EC2 Instances
   - Underutilized Amazon Redshift Clusters
   - idle ebs volumes
   - Idle elb
   - enable s3 bucket keys
   - rds_general_purpose_ssd
   - ebs_general_purpose_ssd
   - unused_ami

# v0.0.7
1. Added methods for new recommendations
2. Added following methods:
   1. reserved_instance_lease_expiration()
   2. unassociated_elastic_ip_addresses()
   3. rds_idle_db_instances()

# v0.0.3
1. Minor bug fixes

# v0.0.2
1. Improved code quality
2. Multi region now supported

# v0.0.1 - Initial Release
kingback 1.0.2
Python Backtesting farmework for everyone

# Kingback

Kingback is a Python library for doing backtesting in python. It helps you organize your code and modularize different common component encounter during backtesting.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.

```bash
pip install kingback
```

## Usage

```python
from kingback import (
    PriceData,
    FeatureData,
    Strategy,
    Env,
    Backtest,
    Params,
    Optimizer
)
import pandas as pd
import yfinance as yf

btc = yf.Ticker("BTC-USD")
btchist = btc.history(interval="1d", start="2021-01-01", end="2023-03-01")
btchist.reset_index(inplace=True)
btchist["Date"] = pd.to_datetime(btchist["Date"], utc=True)
btchist = btchist.set_index("Date")

eth = yf.Ticker("ETH-USD")
ethhist = eth.history(interval="1d", start="2021-01-01", end="2023-03-01")
ethhist.reset_index(inplace=True)
ethhist["Date"] = pd.to_datetime(ethhist["Date"], utc=True)
ethhist = ethhist.set_index("Date")


class ComplexStrategy(Strategy):
    def __init__(self):
        super().__init__()

    def onData(self, i: int, env: Env, df: pd.DataFrame):
        now_date = df.loc[i, "Date"]
        now_open = df.loc[i, "open_BTC"]
        now_high = df.loc[i, "high_BTC"]
        now_low = df.loc[i, "low_BTC"]
        now_close = df.loc[i, "close_BTC"]

        env.unrealized_pnl = (now_close - env.pos_open_price) * env.pos
        env.equity_value_list.append(env.last_realized_capital + env.unrealized_pnl)

        # trade logic
        trade_logic = True

        # Open Position
        if env.pos == 0 and trade_logic:
            env.pos = env.last_realized_capital // now_close
            env.pos_open_price = now_close

        # Close Long Position
        elif env.pos > 0:
            env.pos_close_price = now_close
            env.pnl = (
                env.pos_close_price - env.pos_open_price
            ) * env.pos  # if Short, reverse
            env.pnl_list.append(env.pnl)
            env.last_realized_capital = env.last_realized_capital + env.pnl
            env.pos = 0
        return env


params_combs = Params({"tp": [1, 3, 5], "sl": [2, 4]})
p = PriceData("BTC")
p.set_price_data(btchist)

e = FeatureData("ETH")
e.set_feature_data(ethhist)

s = ComplexStrategy()
s.useData(p())
s.useFeature(e())

opt = Optimizer(params_combs=params_combs, strategy=s, default_env=Env(100000))

opt.run()

print(opt.result_sets)







```

## 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.

## License

[MIT](https://choosealicense.com/licenses/mit/)
aws-recommendation-a15 0.1.0
Provides AWS recommendations
Requires Python: >=3.7

# aws_recommendations

aws_recommendations package gives recommendation for you aws account

Release Notes:
# v0.0.28
1. Bug fixes and stability improvements

# v0.0.10
1. Added methods for following recommendations:
   - Low Utilization Amazon EC2 Instances
   - Underutilized Amazon Redshift Clusters
   - idle ebs volumes
   - Idle elb
   - enable s3 bucket keys
   - rds_general_purpose_ssd
   - ebs_general_purpose_ssd
   - unused_ami

# v0.0.7
1. Added methods for new recommendations
2. Added following methods:
   1. reserved_instance_lease_expiration()
   2. unassociated_elastic_ip_addresses()
   3. rds_idle_db_instances()

# v0.0.3
1. Minor bug fixes

# v0.0.2
1. Improved code quality
2. Multi region now supported

# v0.0.1 - Initial Release
xm2xl 0.3.0
Flatten Xmind file into xlsx sheet
Requires Python: >=3.9.16

# xm2xl
Flatten Xmind file into xlsx sheet

## Installation

pip install xm2xl

## Usage

     Usage : xm2xl <Xmind File> <flags>
     
     Flags
        -s, --sheet=SHEET : Sheet to read data from
            Default: ''
        -o, --outputfile=OUTPUTFILE : Name of the output file
            Default: ''
scrape_files 0.2.0
`scrape_files` is a tool to help scrape things online to your local machine.
Requires Python: >=3.7
Keywords: python, scraping, html, markdown, image, downloader

`scrape_files` is a tool to help scrape things online to your local machine.
Currently, it supports scraping and converting htmls to well-formatted markdowns for easy reading as well as scraping and downloading images of various formats in a web page. 

### Scraping htmls to your local machine
The html parsing logic is similar to a browser's easyread extension's, which trims off all the unnecessary decorations from a web page, only keeping the title and the article content. The main difference is that the file is downloaded and converted as a pretty formatted markdown.

Also support scraping links under the `<p>` tag in the current page concurrently.

Terminal usage:
```bash
scrape html <url>     # specify a url for scraping
scrape html <url> -d  # specify a directory name for saving files in current folder
scrape html <url> -l  # specify a level: 1 by default for the current page; 2 for links in the current page
```

### Scraping images to your local machine
Images are scraped and downloaded concurrently. Supported formats: jpg, png, gif, svg, jpeg, webp; defaults to all supported formats.

Terminal usage:
```bash
scrape image <url>     # specify a url for scraping
scrape image <url> -d  # specify a diretory name for saving files in current folder 
scrape image <url> -f  # specify image formats separated with space 
```

# Installation
```python
pip install scrape_files
```
ostats 2023.3.23
online algorithm for mean, variance, and covariance
Requires Python: >=3.6
Keywords: online algorithm,online mean,online variance,online covariance,online statistics

ostats
======

Online algorithm for the mean, variance, and covariance.

The `ostats` package contains a single function `ostats.add_sample()` which
updates a sample mean given a new sample, as well as optionally the sample
variance and the sample covariance matrix.

The package has no dependencies, as the `ostats.add_sample()` function works
with any input data type that supports in-place addition and fancy slicing.

Usage
-----

```py
import numpy as np

# the package
import ostats

# online algorithm for the mean
# start from zero
mu = np.zeros(4)

# generate samples and compute their mean
for i in range(1000):
    x = np.random.normal([0.1, 0.3, 0.5, 0.7])
    ostats.add_sample(i, x, mu)

# the mean is computed in place
print(mu)  # [0.08804402 0.25896929 0.44891264 0.73418769]

# compute the variance
mu = np.zeros(4)
var = np.zeros(4)
for i in range(1000):
    x = np.random.normal([0.1, 0.3, 0.5, 0.7], [0.2, 0.4, 0.6, 0.8])
    ostats.add_sample(i, x, mu, var=var)

print(mu)  # [0.09854301 0.29509305 0.4777673  0.70008311]
print(var**0.5)  # [0.19900518 0.4012857  0.59267129 0.81856542]

# compute the covariance matrix
mu = np.zeros(4)
cov = np.zeros((4, 4))
for i in range(100_000):
    x = np.random.multivariate_normal([0.1, 0.3, 0.5, 0.7],
            [[0.2, 0.02, 0.04, 0.06],
             [0.02, 0.4, 0.06, 0.08],
             [0.04, 0.06, 0.6, 0.10],
             [0.06, 0.08, 0.10, 0.8]])
    ostats.add_sample(i, x, mu, cov=cov)

print(mu)  # [0.10095607 0.30486108 0.50113141 0.69912377]
print(cov)
# [[0.20101406 0.02105503 0.0382198  0.06220174]
#  [0.02105503 0.39909545 0.06192678 0.0791239 ]
#  [0.0382198  0.06192678 0.59960537 0.1082596 ]
#  [0.06220174 0.0791239  0.1082596  0.80071002]]

```
junyu 1.0.11
君禹科技
Requires Python: >=3

# 这是README
君禹科技人工智能神经网络

## 字体
*这是斜体*
_这是斜体_
**这是粗体**
__这是粗体__
***这是粗斜体***
___这是粗斜体___

## 引用
>这是一个引用
>> 这是一个引用的引用

## 链接
[百度](https://www.baidu.com)
或者
[百度]
<https://www.baidu.com>

## 图片

<img alt="图片描述" height="300" src="C:\Users\Administrator\Pictures\gravator.jpg" width="300"/>

## 列表
* 无序列表 
  * 无序列表 1
+ 无序列表
- 无序列表

1. 有序列表 
   1. 有序列表
      1. 有序列表
   2. 有序列表
2. 有序列表
3. 有序列表

## 代码块
`hello` world

```python
def func():
    print('python')
```