I am a student blockchain developer, I am hoping to graduate next spring. My plan is to make this Code Louisville graduation project the first step towards a much bigger work in progress project of extensive analysis and prediction of the preformance of different cryptocurrencies. I chose to make the project include more than jupyter notebook file, each file is relatively small and covers one feature of the future comprehensive project
The project will include the following features hoping to meet or exeed the requirements of a passing graduation project:
Read 2 dataset with an API. The first API is forex-python (https://pypi.org/project/forex-python/) where I get bitcoin data, the second API is yfinance API (https://pypi.org/project/yfinance/) Cleaning data and perform a pandas merge Make 3 matplotlib, pandas and seaborn visualizations to display data I used the following code to create and use a virutal environment:#create environment named venv
python -m venv venv
#activate environment
venv\scripts\activate.bat
#install project packages and dependencies
#create a requirements.txt file
pip freeze > requirements.txt
#deactivate environment each time after working on the project
deactivate
The contents of requirements.txt file
bokeh==2.4.3
certifi==2022.6.15
charset-normalizer==2.1.0
cycler==0.11.0
DateTime==4.5
fonttools==4.34.4
forex-python==1.8
idna==3.3
Jinja2==3.1.2
kiwisolver==1.4.4
lxml==4.9.1
MarkupSafe==2.1
.1
matplotlib==3.5.2
multitasking==0.0.11
numpy==1.23.1
packaging==21.3
pandas==1.4.3
Pillow==9.2.0
pyparsing==3.0.9
python-dateutil==2.8.2
pytz==2022.1
PyYAML==6.0
requests==2.28.1
scipy==1.8.1
seaborn==0.11.2
simplejson==3.17.6
six==1.16.0
stockstats==0.4.1
tornado==6.2
typing_extensions==4.3.0
urllib3==1.26.11
yfinance==0.1.74
zope.interface==5.4.0
I would like to thankmy mentors for their great effort they made to help us. I liked their new innovative methods they used to present the material. I am also grateful to every person in Code Louisville staff
- Examining and adding different trading technical analysis indicators so that the project becomes a smart and reliable crypto trading helper tool
- Adding automation features so that the project can be used as a trading bot
- Adding machine learning capability so it can be used to test different trading strategies
- The project will stay open to any future ideas, suggestions and upgrades