Duplicate from Pcitycrypto/xauusd

Co-authored-by: Michael LaPointe <[email protected]>

.gitattributes

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+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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+*.jpg filter=lfs diff=lfs merge=lfs -text
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+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
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+XAUUSDx_M1_201111010100_202401032223.csv filter=lfs diff=lfs merge=lfs -text
+XAUUSD_1m.csv filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+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())

XAUUSD_1m.csv

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