| Team | date |
|---|---|
Digital Gold Digger |
04/01/2022 |
Digital Gold Digger
By Estrella Moreira, Dawa Lama, Zhi Li, Ali Salem
We are here making trading strategies of top 5 cryptos they are Bitcoin, Ethereum, USDT Tether, USD Coin and Binance Coin with relationship with macroeconomic variable they are Federal fund interest rate, dallor exchnage rate, and S&P 500 index. Here our main aim is to see the effect of macroeconomic variables on trading of top 5 cryptos and we have calculate risk factor related to large cap cryptos trading.
Instruments use for trading(Top 5 large-cap Cryptos) have used here as our main instruments.
| S.N. | Symbol (Ticker) | Crypto Name |
|---|---|---|
| 1 | BTC | Bitcoin |
| 2 | ETH | Ethereum |
| 3 | ADA-ETH | Cardano |
| 4 | BNB | Binance Coin |
| 5 | USDC | USD Coin |
Data reported time : 04/05/2022, 14:20 Data source: https://coinmarketcap.com/
Macroeconomic variables use for project
| s.n. | Name | Data type |
|---|---|---|
| 1 | Federl fund Interest rate | Daily |
| 2 | Gold Price | daily/Per ounce |
| 3 | S&P 500 data | Daily |
| 4 | Euro Exchange rate | Daily |
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── 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
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── 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.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── 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
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io