semvec/pypi-packages huggingface dataset:
cwutils 1.1.1a0
Utils by Chaos and Whoami.

### Модуль time
*** 
```python
from cwutils import time

time.time_to_string(second)
```
> Конвертирует секунды(second) в недели, дни, часы, секунды (в зависимости сколько секунд вы написали в second.).

### Модуль memory
***
```python
from cwutils import memory

memory.memory_from_bytes('{{your number}} to {}KB, MB, GB, TB, PB, EB, ZB, UB}}'
```
> Конвентирует байты в KB, MB, GB, TB, PB, EB, ZB, UB (в зависимости от того что вы напишите после "to".).
```python
from cwutils import memory

memory.get_size_unit_in_bytes('{{{KB, MB, GB, TB, PB, EB, ZB, UB}}}')
```
> Возвращает размер 1 памятной единицы которую вы передали.
```python from cwutils import memory

memory.memory_from_string('(your number) KB, MB, GB, TB, PB, EB, ZB, UB')
```
> Тоже самое что и в 1. В "your number" напишите число, которые хотите конвертировать в KB, MB, GB, TB, PB, EB, ZB, UB.
 
```python 
from cwutils import memory
 
memory.get_size_unit(bytes)
```
> Возвращает макс.тип размерности кол-ва переданных байтов.

### Модуль nums
***
```python
from cwutils import nums

nums.num_to_string(your number)
```
> Конвертирует цифры в строчные цифры.
MLTA 0.0.5
Technical Analysis enhanced through Machine Learning

# MLTA
Machine Learning Driven Technical Analysis Library in Python.

## Motivation
Technical Analysis has long been a stable for generating insights for trading. While the predictive power of each individual indicator is up for a debate, it is no denying that technical indicators are useful tools to create summary statistics for time-series data. This usefulness is not limited to finance, but rather any domain with necessity to deal with time series.

However, most indicators are interpreted in a too simplistic way. For example, candlestick patterns classify each candle into one of a couple classes. However, we would get more useful information if we can also measure how close a candle matches each class.

MLTA enables this possibility. Instead of returning classes, MLTA generates the likelihood of a candle belonging to a given class. The result is more granula information which leads to an improvement in accuracy.

## Installation

MLTA is not yet on pypi. You can install the package through the following command:

	pip install git+https://github.com/palmbook/MLTA.git

## Usage

#### Candlestick Patterns

	from MLTA import Candlestick

	candle_model = Candlestick()

	# df must have columns open, high, low, and close (in lower letters)
	# class_prob contains columns of probabilities for each possible class
	class_prob = candle_model.CDL3INSIDE(df)

Available methods are

* CDL3INSIDE
* CDL3LINESTRIKE
* CDL3OUTSIDE
* CDL3WHITESOLDIERS
* CDLCLOSINGMARUBOZU
* CDLCOUNTERATTACK
* CDLDOJI
* CDLDRAGONFLYDOJI
* CDLGAPSIDESIDEWHITE
* CDLGRAVESTONEDOJI
* CDLHAMMER
* CDLHARAMI
* CDLHOMINGPIGEON
* CDLINVERTEDHAMMER
* CDLLADDERBOTTOM
* CDLLONGLEGGEDDOJI
* CDLLONGLINE
* CDLMARUBOZU
* CDLMATCHINGLOW
* CDLMORNINGDOJISTAR
* CDLMORNINGSTAR
* CDLRICKSHAWMAN
* CDLRISEFALL3METHODS
* CDLSEPARATINGLINES
* CDLSHORTLINE
* CDLSTICKSANDWICH
* CDLTAKURI
* CDLTASUKIGAP
* CDLUNIQUE3RIVER
* bullishPin
* bearishPin