backtesting data · historical data for backtesting · trading backtest data · quant backtesting data
Backtesting data
Backtesting data is the historical dataset used to test a trading or investment idea before live deployment. Useful backtesting requires more than price history. It needs correct timestamps, corporate actions, delisted instruments, point-in-time fields, realistic costs and a clear understanding of what would have been known at the time.
What this means
Backtesting data is not just historical prices. It is the full information set used to simulate historical decisions: prices, volume, universe membership, fundamentals, corporate actions, costs, constraints and timing.
The goal is not to make a backtest look good. It is to make the test realistic enough that a weak idea fails before capital, time or operational attention is spent on it.
Main data and source types
Different strategies need different data. A slow equity screen may need daily prices, fundamentals and corporate actions. An intraday strategy may need quotes, trades, spreads, fees and execution assumptions.
- Historical prices and volume.
- Corporate actions and adjusted fields.
- Delisted instruments and point-in-time universes.
- Transaction costs, spreads, slippage and borrow constraints.
Free or public sources
Free data can be useful for learning and early experiments, but it often lacks delisted instruments, point-in-time fields, robust corporate-action history or clear provenance.
Use public data carefully and label results as exploratory when the dataset cannot support realistic historical decision simulation.
Paid or vendor sources
Paid backtesting datasets may include delisted securities, point-in-time fundamentals, survivorship-bias controls, corporate actions and bulk delivery. These features can matter more than raw breadth.
Licensing should be checked before storing derived research datasets, sharing results or embedding data in internal tools.
API and infrastructure considerations
Backtesting infrastructure should capture dataset version, retrieval date, cleaning rules, universe construction, parameter sets, transaction assumptions and code commit. Without these, results are hard to audit.
Separate raw data, cleaned data, features and simulation outputs. This makes it easier to detect whether performance comes from the idea or the data transformation pipeline.
Common use cases
Backtesting data supports strategy evaluation, factor research, portfolio construction, execution modelling, risk review, transaction-cost studies and out-of-sample validation.
Limitations and risks
Common risks include survivorship bias, lookahead bias, data snooping, stale data, incorrect adjustments, missing delistings, unrealistic costs and overfitting. Good backtests are sceptical by design.
Selection checklist
Check point-in-time correctness, universe construction, delisted coverage, corporate actions, calendars, costs, slippage, liquidity, benchmark choice, validation windows and whether results survive out-of-sample testing.
FAQ
What data do I need for backtesting?
It depends on the strategy. At minimum, you may need historical prices and volume; stronger backtests often require corporate actions, delisted instruments, point-in-time data and realistic costs.
What is lookahead bias?
Lookahead bias occurs when a backtest uses information that would not have been available at the time of the simulated decision.
Why is point-in-time data important?
Point-in-time data helps ensure the backtest only uses information available at that historical moment.
Should backtests include transaction costs?
Yes for any strategy where trading, turnover or execution quality affects results. Costs can change a promising result into an unusable one.
Ledgerstone is an independent financial-data research guide. It does not provide investment advice, trading advice, brokerage services, data vendor services or financial promotion.