This code was created with the purpose of assembling investment portfolios that are based on cryptocurrencies. In this code I added new unsupervised learning skills. I created a Jupyter notebook that clusters cryptocurrencies by their performance in different time periods. Then,I plotted the results so that I can visually show the performance.
pandas
hvplot.pandas
path
sklearn.cluster
sklearn.decomposition
sklearn.preprocessing
In order to open and run this program you have to follow these steps:
Go to my repository in GitHub and open the repository called crypto_investment_10
Copy the repository's link
Open Git Bash in your computer
Clone the repository by typing git clone and paste the link [email protected]:nestor39/crypto_investment_10.git
After cloning the repository, activate the Conda development environment, by typing conda activate dev and then run the following code:
pip install -U scikit-learn
conda install -c pyviz hvplot
After installing the tools above
Type jupyter lab" in your Git Bash(it will open a tab in your explorer)
Open the file crypto_investments.ipynb and you are going to see the code.
Save it
Push play
This code is going to show you many superpower tools with interactive characteristics:
2021-05-29.21-58-12.mp4
2021-05-29.22-02-06.mp4
2021-05-29.22-01-31.mp4
2021-05-29.22-00-41.mp4
2021-05-29.22-00-10.mp4
This project was made with helpful contribuitions from Berkeley Fintech Bootcamp members; Siege and Joel Gonzales. Joel Gonzales was of particular assistence when it came to improving the code.
I also utilized the AskBCS Learning Assistent in order to fine-tune my project.
This code was written by Nestor Ramirez.
email: [email protected]
Linkedin: (https://www.linkedin.com/in/nestor-ramirez-cuadro-375654209/)
MIT