Demo: https://pintoza-real-time-indicators-home-slo8fp.streamlit.app/
- Average Weekly Hours of All Employees, Manufacturing
- Average Weekly Initial Jobless Claims
- Manufacturers' New Orders: Consumer Goods
- Manufacturers' New Orders: Non-defense Capital Goods
- Building Permits: New Privately Owned Housing Units
- S&P 500 Stock Price Index
- Interest Rate Spread: 10-Year Treasury Constant Maturity vs. Federal Funds Rate
- Average Consumer Expectations for Business Conditions
- Average Weeks Unemployment
- Commercial and Industrial Loans, All Commercial Banks
- Consumer Price Index for All Urban Consumers: All Items
- Unit Labor Costs for All Workers
- Manufacturers' Inventories to Sales Ratio
- Consumer Credit as a Percentage of Disposable Personal Income
- Bank Prime Loan Rate
- Total Employees on Non-farm Payrolls
- Personal Income excluding current Transfer Receipts
- Industrial Production Index
- Manufacturing and Trade Industries Sales
- This project creates an analytics engineering dashboard for visualizing key economic indicators. It aims to provide up-to-date information on leading, lagging, and coincident economic indicators, reflecting the latest economic trends and data releases.
- The initiative involves the aggregation of data from various authoritative economic sources, focusing on the real-time update and visualization of these indicators.
- Develop a platform that showcases updated economic indicators, making complex data accessible and interpretable.
- Empower users with the ability to analyze trends in economic data through interactive visualizations.
- The data is sourced from several economic sources, (see Inidicators list above), although the data is pulled from two main sources:
- Federal Reserve Economic Data (FRED) API via the fredapi
- Survey of Consumers, University of Michigan via the Survey of Consumers
- This project employs a robust data pipeline that extracts, processes, and loads data into a streamlit web app and uses cron jobs to ensure updated and clean data is always available.
- The updates are set to run daily at 11 AM EST, ensuring that the data is always up-to-date.
- Data Extraction and Loading:
- Python scripts for automated data extraction and loading.
- Cron jobs for scheduling daily updates.
- Data Visualization:
- Plotly for creating interactive data visualizations.
- Web Application:
- Streamlit for building the interactive web application.
- Python 3.x
- Streamlit
- FRED API Key
- Other Python packages (see requirements.txt)
- Instructions on setting up the database, environment variables, and running the Python scripts.
- Steps to start the Streamlit server and access the web application.
- Details on how others can contribute to the project.
- Acknowledgements for any third-party resources or contributors.
- This project is licensed under the MIT License - see the LICENSE.md file for details.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ │
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io