---
license: cc0-1.0
task_categories:
- time-series-forecasting
language:
- en
tags:
- forex
- oanda
- trading
- financial-data
pretty_name: OANDA Trading Data - 10 Year Backfill
---

# OANDA Trading Data - 10 Year Backfill

Historical forex (FX) candle data collected from OANDA v3 API for machine learning model training.

## Dataset Summary

- **Period**: 10 years of historical data
- **Instruments**: EUR_USD, GBP_USD, USD_JPY, AUD_USD, USD_CHF
- **Granularities**: H1 (1-hour candles) - optimized for model training
- **Total Records**: ~310,000 rows (62k rows × 5 instruments)
- **Format**: CSV with OHLCV columns

## Data Format

Each CSV file contains:
- `instrument`: Currency pair (e.g., EUR_USD)
- `granularity`: Timeframe (H1 = 1-hour)
- `time`: Candle timestamp (ISO 8601)
- `open`: Opening price
- `high`: Highest price in period
- `low`: Lowest price in period
- `close`: Closing price
- `volume`: Volume (pip-based for OANDA)

## Files

- `EUR_USD_H1_10y_*.csv` - Euro/US Dollar hourly data
- `GBP_USD_H1_10y_*.csv` - British Pound/US Dollar hourly data
- `USD_JPY_H1_10y_*.csv` - US Dollar/Japanese Yen hourly data
- `AUD_USD_H1_10y_*.csv` - Australian Dollar/US Dollar hourly data
- `USD_CHF_H1_10y_*.csv` - US Dollar/Swiss Franc hourly data

## Usage

```python
from datasets import load_dataset

# Load specific instrument
df = load_dataset('keeprich/oanda-trading-data', data_files='EUR_USD_H1_10y_*.csv')

# Or load all instruments
df = load_dataset('keeprich/oanda-trading-data', split='train')
```

## Citation

OANDA trading data for ML model training. Collected July 2026.

## License

CC0 1.0 Universal (Public Domain)