README.md

metadata

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

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)