Features
Data
Connect, store, update, transform, and combine local and remote market data
Bring market data into a research workflow, whether it comes from a provider such as Yahoo Finance, Binance, or TradingView, from a local file, or from a database. This section is for anyone who needs clean, aligned, and current data before testing a strategy. It covers data sources, storage and querying, pipelines that keep data up to date, combining timeframes by when their values become available, and synthetic prices for testing a strategy on paths that never happened.
Start here
Begin with Market data sources to pull your first dataset. Already have your data? Start with Data storage and databases for files and database queries, or Data pipelines for working with your own Pandas objects.
| What do you want to do? | Where to start |
|---|---|
| Find a provider for stocks, crypto, futures, or FX | Market data sources |
| Bring chart data into Python or compare Binance spot and futures | TradingView data and Binance data |
| Keep a local history and load only the data you need | Data storage and databases |
| Align symbols with different histories, add research features, and update the dataset | Data pipelines |
| Use a daily indicator with hourly entries | Multi-timeframe analysis |
| Test a strategy on generated prices and different market conditions | Synthetic data |
Data pages
Market data sources
Pull stock, crypto, futures, and FX data from many providers with one interface
TradingView data
Pull TradingView bars for stocks, futures, crypto, and FX, and search its symbols
Binance data
Download Binance spot and futures klines with trade counts and taker volume
Data storage and databases
Save and query market data in files, DuckDB, SQL databases, and ArcticDB
Data pipelines
Align, update, transform, and analyze multi-symbol market data in one object
Multi-timeframe analysis
Resample and realign data across timeframes by when each value became available
Synthetic data
Generate reproducible random walks, GBM paths, and synthetic OHLC bars
From data to a backtest
You can start with downloaded candles, saved files, or DataFrames from your own research. VBT's data object keeps symbols and features together, aligns their indexes, and lets you choose whether to retain each symbol's full history or work with their shared timestamps. Prices and custom features can then feed indicators and trading signals.
For example, pull hourly prices for several coins, save the history locally, and compare a strategy across the same period for each coin. Add a daily trend filter with the multi-timeframe tools, then try the strategy on synthetic paths to explore how it behaves outside that history. Each part has its own feature page above, so you can use the pieces your research needs.
Continue with Technical indicators to calculate signals, or Signal backtesting if your entry and exit conditions are ready. Use the Data documentation to learn how VBT represents, retrieves, updates, and manages market data.
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