semvec/pypi-packages huggingface dataset:
spotify-confidence 4.0.0
Package for calculating and visualising confidence intervals, e.g. for A/B test analysis.
Requires Python: >=3.9

Spotify Confidence
========

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Python library for AB test analysis.

Why use Spotify Confidence?
-----------------

Spotify Confidence provides convenience wrappers around statsmodel's various functions for computing p-values and confidence intervalls. 
With Spotify Confidence it's easy to compute several p-values and confidence bounds in one go, e.g. one for each country or for each date. 
Each function comes in two versions: 
 - one that return a pandas dataframe,
 - one that returns a [Chartify](https://github.com/spotify/chartify) chart.

Spotify Confidence has support calculating p-values and confidence intervals using Z-statistics, Student's T-statistics 
(or more exactly [Welch's T-test](https://en.wikipedia.org/wiki/Welch%27s_t-test)), as well as Chi-squared statistics. 
It also supports a variance reduction technique based on using pre-exposure data to fit a linear model.  

There is also a Bayesian alternative in the BetaBinomial class.

Basic Example
--------
```
import spotify_confidence as confidence
import pandas as pd

data = pd.DataFrame(
    {'variation_name': ['treatment1', 'control', 'treatment2', 'treatment3'],
     'success': [50, 40, 10, 20],
     'total': [100, 100, 50, 60]
    }
)

test = confidence.ZTest(
    data,
    numerator_column='success',
    numerator_sum_squares_column=None,
    denominator_column='total',
    categorical_group_columns='variation_name',
    correction_method='bonferroni')
    
test.summary()
test.difference(level_1='control', level_2='treatment1')
test.multiple_difference(level='control', level_as_reference=True)

test.summary_plot().show()
test.difference_plot(level_1='control', level_2='treatment1').show()
test.multiple_difference_plot(level='control', level_as_reference=True).show()
```

There's a lot more you can do:
- Segment results by one or more dimensions
- Use non-inferiority margins
- Group sequential tests
- Sample size and power calculations
- etc

See jupyter notebooks in `examples` folder for more complete examples.

Installation
------------
Spotify Confidence can be installed via pip:

```pip install spotify-confidence```

[Find the latest release version here](https://github.com/spotify/confidence/releases)

### Code of Conduct

This project adheres to the [Open Code of Conduct](https://github.com/spotify/code-of-conduct/blob/master/code-of-conduct.md) By participating, you are expected to honor this code.
atj-algotrading 0.0.16
ATJ Trader's Package for Algo Trading & Backtesting
Requires Python: >=3.9

# ATJ Traders - Algo Trading Lib
This is ATJ Trader's Algo Trading Package in Python. It connects to the MetaTrader5 platform and comes with classes
and functions to make Backtesting and Coding of Trading Bots easier. 

## Installation
> pip install atj-algotrading

## Backtesting Tutorial
Please follow this link to access tutorials on Backtesting

## Trading Bot Development Tutorial
Please follow this link to access tutorials on Trading Bot Development