Build Your Own Historical Dataset — CSV, JSON or Parquet
Choose a market, timeframe and date range and preview the rows. Full CSV, JSON and Parquet downloads are paid. More than 600 symbol-timeframe combinations across forex, gold, indices, energy and crypto.
Build a Custom Dataset
Select a symbol and timeframe. The date fields update to the available range. Preview matching rows; full CSV, JSON and Parquet downloads are paid.
Choose a dataset to see its available range.
| Timestamp | Open | High | Low | Close | TickVolume | Spread |
|---|
CSV, JSON or Parquet — Which Should You Use?
CSV
Best for MT4/MT5, Excel, pandas and quick inspection. Supports any custom date range.
JSON
Best for JavaScript, web apps and APIs. Nested metadata and row objects. Supports custom dates.
Parquet
Best for Python, DuckDB, Polars and ML. Columnar, compressed and fast. Full-history format available in paid packages.
What You Can Build With This Data
- Backtests: export only the date range used by your strategy.
- Machine learning: combine with the indicator-labeled datasets and ML-ready datasets.
- Session studies: filter a date range, convert server time to UTC, and measure Asian, London and New York behaviour.
- Data quality checks: inspect gaps, duplicates and spread outliers before training.
- Custom dashboards: use JSON for browser apps and Parquet for Python services.
Custom Data Builder FAQ
Can I preview only a specific date range?
Yes. Choose a symbol, timeframe, start date and end date. The preview shows matching rows; full downloads are paid.
What formats are available?
CSV, JSON and Parquet are available in paid packages. The preview is for format checking only.
How many datasets are available?
The catalog contains more than 600 symbol-timeframe combinations across forex, gold, indices, energy and crypto.
Are the timestamps in broker server time?
Yes. Timestamps are broker server time. Convert to UTC or New York time before building session-based strategies.
Can I use the data for machine learning?
Yes. CSV and Parquet are ready for pandas, Polars, DuckDB, scikit-learn and PyTorch pipelines.
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