Datasets:
File size: 1,648 Bytes
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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)
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