| --- |
| language: |
| - en |
| license: |
| - cc-by-4.0 |
| task_categories: |
| - time-series-forecasting |
| - tabular-classification |
| tags: |
| - finance |
| - gold |
| - forex |
| - XAUUSD |
| - time-series |
| pretty_name: Cleaned XAUUSD Dataset |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # Cleaned XAUUSD Dataset |
|
|
| ## Dataset Description |
|
|
| This dataset contains cleaned and preprocessed minute-level historical price data for the XAU/USD (Gold vs. US Dollar) pair. The data spans from **November 1, 2011**, to **January 3, 2024**, and includes the following columns: |
|
|
| - `open`: The opening price of the minute. |
| - `high`: The highest price during the minute. |
| - `low`: The lowest price during the minute. |
| - `close`: The closing price of the minute. |
| - `tickvol`: The number of price changes (ticks) during the minute. |
|
|
| The dataset is cleaned, with missing values removed and unnecessary columns (e.g., `VOLUME`, `SPREAD`) dropped. The data is indexed by a `datetime` column, making it suitable for time-series analysis. |
|
|
| ### Dataset Structure |
|
|
| - **Rows**: Minute-level price data. |
| - **Columns**: |
| - `datetime`: The timestamp for each minute (used as the index). |
| - `open`: Opening price. |
| - `high`: Highest price. |
| - `low`: Lowest price. |
| - `close`: Closing price. |
| - `tickvol`: Number of price changes (ticks). |
|
|
| ### Example Usage |
|
|
| Here’s how you can load and use the dataset in Python: |
|
|
| ```python |
| import pandas as pd |
| |
| # Load the dataset from Hugging Face |
| file_path = "hf://datasets/Pcitycrypto/xauusd/XAUUSD_1m.csv" |
| df = pd.read_csv(file_path, parse_dates=['datetime'], index_col='datetime') |
| |
| # Display the first few rows |
| print(df.head()) |
