This is an unofficial implementation of the paper: Step on the Gas? A Better Approach for Recommending the Ethereum Gas Price (by Sam M. Werner, Paul J. Pritz, Daniel Perez)!
Table of Contents
This is a price gas recommender for the Ethereum network. The network is trained to
predict the next Sth prices (with a resampling over 5 minutes periods). As the minimum
gas price is very noisy, the idea is to predict the prices on the next minutes/hours.
From thoses predictions the algorithm will return a recommended price,
taking into account the slope of the predictions.
Few plots from this repository:
Note: We try to forecast the minimum eth gas price over the next 3 hours.
The loss of the GRU model:
Model first timestamp predictions (5min) on the test range:
Some predictions over the 3 hours:
Results of the simulation between the block 8965759 and the block 8995344:
DEEP GAS ORACLE :
GETH (my implementation):
As a comparison, here is the paper original results:
Note: I just tried a few different hyper-parameters but didn't have time to tune them yet. My results are not as good as the paper but close to it.
- Python
- Pytorch
- Pandas & Numpy
- Notebooks for visualisations
Follow the notebooks.
This run on python 3, you can find the requirements in : requirements.txt
Note: you need to have pip installed
To download the raw datasets, you need a google api key to use the BigQuery
service to be able to fetch historical eth blocks information.
Once you have the key, place the json file in the credentials/ folder.
For the eth price, you can download it here on Kaggle.
You should put the ETH price csv file in the datasets/ folder.
1 - Clone this repo
2 - Install the package with pip:
pip install .
You can run the notebooks to:
- 01 -> Explore the data and preprocess it
- 02 -> Modelise the minimum gas prices (5min avg) with a GRU neural-network
- 03 -> Evaluate the recommendation made by the deep oracle VS "Geth strategy"
Distributed under the MIT License. See LICENSE for more information.