Moon Radar measures the popularity of altcoins on social media platforms Twitter, Reddit and Google Trends. If it detects a spike in popularity for a given altcoin, it notifies it’s user by means of a push notification. The user can then decide to act on this information by buying or selling shares of the respective cryptocurrency. Furthermore, Moon Radar retrieves current exchange rates for altcoins from CoinGecko and reports them alongside the corresponding popularity index.
Make sure you have the following resources at your disposal.
- PostgreSQL Database
- Jupyter Notebook
- Docker
Start by executing ‘API_…’ scripts in folder ‘1_Historical_data_to_RDS’ in a Jupyter Notebook. This will pull historical data from Google Trend’s, Reddit’s and CoinGecko’s API and subsequently loads the data obtained into your PostreSQL Data Lake.
Next, create a new docker image using the docker-compose.yaml and Dockerfile within the folder ‘2_Continuous_data_to_RDS’. Proceed by adding the ‘API_to_RDS.py’ script within the ‘DAG’ folder as a DAG in your Apache Airflow instance.
This procedure will fetch API data at a quarter-hourly rate and uploads it, supplementing historical data in your PostgreSQL Data Lake with current, up-to-date, information. That is already it, you established your own Data Lake which comprises of tweets, posts, queries, etc. regarding altcoins as well as their currency excange rates!