task_categories:time-series-forecasting task_categories:tabular-classification language:en license:cc-by-4.0 size_categories:1M<n<10M format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us finance gold forex XAUUSD time-series
---
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())