Bitcoin real time prediction using SNARIMA by
riverml
andBinance API
![bitcoin_example_01](https://private-user-images.githubusercontent.com/118663358/291033322-b8d7c7c6-3aef-46a6-a75b-31297bea63b6.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3Mzg4ODM5MjEsIm5iZiI6MTczODg4MzYyMSwicGF0aCI6Ii8xMTg2NjMzNTgvMjkxMDMzMzIyLWI4ZDdjN2M2LTNhZWYtNDZhNi1hNzViLTMxMjk3YmVhNjNiNi5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjUwMjA2JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI1MDIwNlQyMzEzNDFaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1mZTFlNjc0MmU0NTIzZjk2M2Y3ZTE0ZDE5Mzc1NGI2YTZjZjA2OGM2YWZlODUyMjdjMjcyNWFlYmYzMGRmOGZlJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.BOofANjkPMCQM2JcH5DY-f_GR-ou7-UjB98ABg-zSEo)
![bitcoin_example_02](https://private-user-images.githubusercontent.com/118663358/291033332-e0804908-0125-449f-a758-0dcad9fee95d.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3Mzg4ODM5MjEsIm5iZiI6MTczODg4MzYyMSwicGF0aCI6Ii8xMTg2NjMzNTgvMjkxMDMzMzMyLWUwODA0OTA4LTAxMjUtNDQ5Zi1hNzU4LTBkY2FkOWZlZTk1ZC5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjUwMjA2JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI1MDIwNlQyMzEzNDFaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1lZjU4ZDcyZGFlYjEyMTcxMDRmNDVmNDNkNjEyNDViNjMxYjYxNTU1YzgxN2Q4ZjE1Y2UyZTZmZDc4MzIwN2I0JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCJ9.StxJ29S8oV0oepWLSycRJ8sW6igq4aK34TWbhzuyumw)
- clone this repo
- set current directory to
this_repo/predict_app
- run
docker build -t bitcoin:0 .
- run
docker run --name bitcoin -d -p 8080:8080 bitcoin:0
- open your browser and type
localhost:8080
- to close, run
docker stop bitcoin
- to delete all images & containers, run
docker system prune -a
code :
Algorithm : SNARIMA
data type : time series
evaluation : Root Mean Squared Error (RMSE) score
recommendation feature details : The recommendation feature works best when the RMSE score is at its lowest. (e.g. 0.001)