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https://github.com/fergm/fattails
Code and Notes for fat-tailed statistics.
https://github.com/fergm/fattails
fat-tails ipynb notebooks power-laws probability statistical statistics-toolbox tails
Last synced: 6 days ago
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Code and Notes for fat-tailed statistics.
- Host: GitHub
- URL: https://github.com/fergm/fattails
- Owner: FergM
- License: mit
- Created: 2020-10-20T20:47:02.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-12-28T23:56:55.000Z (9 months ago)
- Last Synced: 2024-09-24T21:33:54.577Z (9 days ago)
- Topics: fat-tails, ipynb, notebooks, power-laws, probability, statistical, statistics-toolbox, tails
- Language: Jupyter Notebook
- Homepage:
- Size: 3.43 MB
- Stars: 66
- Watchers: 8
- Forks: 14
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: docs/CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# Fat Tails
Notes and Code for fat-tailed statistics. Inspired by Nassim Taleb's Technical Incerto.
### Table of Contents
* 1. [Blog](./blog)
* 2. [Python Notebooks](#notebooks)
* 3. [External Resources](#external-resources)
* [Other](#other-notes)
### Blog
See [fattails/blog/](./blog)
* 2023-10: I am drafting some articles about Entropy as used in information theory.
### Notebooks
See the [notebooks/README.md](https://github.com/FergM/fattails/blob/main/notebooks/README.md) for more detail.
My favourite notebooks so far:
* [Central Limit Theorem: How the sum of Uniform values is Gaussian](https://github.com/FergM/fattails/blob/main/notebooks/NB-22%20-%20Visual%20Central%20Limit%20Theorem.ipynb)
* [S&P500: How geometric average return is impossible](https://github.com/FergM/fattails/blob/main/notebooks/Notebook-11%20-%20Ergodicity%20and%20S%26P500.ipynb)
* [GameStop: January 2021 was not an outlier if you assume Power Law tails.](https://github.com/FergM/fattails/blob/main/notebooks/NB-25%20-%20Survival%20Plot%20-%20Gamestop.ipynb)
* [Tail Alpha: How to estimate the mean of a power law](https://github.com/FergM/fattails/blob/main/notebooks/NB34%20-%20Tail%20Alpha%20Distribution.ipynb)
### External Resources
Technical Incerto Book One:
* Free PDF: [researchers.one](https://researchers.one/articles/20.01.00018), [arxiv.org](https://arxiv.org/abs/2001.10488)
* Errata: [fooledbyrandomness.com](https://www.fooledbyrandomness.com/Errata2020FirstEdition.pdf)
More Links:
* **Uncertainty Reading Club**: [Website](http://www.techincertoreadingclub.com/)
* Code and Links collected by Marcos Carreira: [`StatisticalConsequencesOfFatTails`](https://github.com/MarcosCarreira/StatisticalConsequencesOfFatTails)
* Anthony Voto: Diagnosing the SP500 [Twitter thread](https://twitter.com/votoaj/status/1427587274670329857?s=20), [GitHub Code](https://github.com/votoaj/Statistical_Consequences_of_Fat_Tails)
# Other Notes
### Contact ✉
How to reach me:
* `@MFergal` on [Twitter](https://twitter.com/MFergal)
### Contributors
Special thanks to:
* Daniel Reti: [github.com/drettt](https://github.com/drettt)
### Python Package
I started this project as a Python Package. Since then I shifted focus to python notebooks instead. Below are some notes about the packaged code and functions.
Quick Access:
* [`fattails.mad()`](https://github.com/FergM/fattails/blob/main/fattails/metrics.py#L7): Calculates mean absolute deviation.
* [`fattails.plot_MS_moments()`](https://github.com/FergM/fattails/blob/main/fattails/express.py#L7): Plots the cumulative max/sum ratio of moments 1 to 4.
Other:
* [`fattails.metrics.get_survival_probability()`](https://github.com/FergM/fattails/blob/main/fattails/metrics.py#L35): Calculate survival probabilities for a given dataset.
* [`fattails.metrics.calculate_moments()`](https://github.com/FergM/fattails/blob/main/fattails/metrics.py#L94): Generate dataframe with the chosen moments for each datapoint
* [`fattails.metrics.max_over_sum()`](https://github.com/FergM/fattails/blob/main/fattails/metrics.py#L132): Calculate the cumulative max/sum ratio
Example:
```
$ pip install fattails
$ python
>>> import fattails
>>>
>>>
>>> fattails.mad([1,2,3]) # Calculate Mean Absolute Deviation of [1,2,3]
0.6666666666666666
>>>
>>>
>>> fattails.metrics.get_survival_probability([1,2,3]) # Get survival probability for each value in your data
0 0.75
1 0.50
2 0.25
Name: survival_probability, dtype: float64
```
### Derivations
Roughwork:
* [How much data do I need?](https://github.com/FergM/fattails/blob/main/docs/Notes-02%20-%20Derivation%20-%20How%20much%20data%20do%20I%20need.pdf)