Point-in-Time Backtesting / 时点回测

Definition

Point-in-time backtesting evaluates a strategy using only the data values, document versions, and publication timing that were available when each historical decision would actually have been made.

Current Synthesis

A historical test becomes invalid when it uses a later correction, revised database value, or document that had not yet been published. The source treats this as seeing the answer in advance, not as a minor implementation detail.

Point-in-time discipline requires preserving availability timestamps and revision history, aligning inputs to each simulated decision, and exposing irrecoverable gaps. It is one necessary defense against look-ahead bias, but it still needs realistic trading costs, execution assumptions, robustness tests, and protection against overfitting.

Key Claims

• Historical event time and data-availability time are not necessarily the same.
• Later revisions must not be substituted for the value known at the simulated decision point.
• Documents and alternative data need publication and access timestamps, not only period labels.
• Missing contemporaneous data should narrow or halt a test rather than be silently filled with hindsight.
• Point-in-time validity is necessary but insufficient for a tradable strategy.

Evidence

Future-data leakage

• AI Trading —— 决策便宜，行动很贵｜对谈超 3w Star Vibe-Trading 作者浩哲 says backtests must not use information unpublished at the time or values corrected only later.

Missing-data behavior

• AI Trading —— 决策便宜，行动很贵｜对谈超 3w Star Vibe-Trading 作者浩哲 requires the agent to pause and identify missing input rather than continue with an incomplete historical record.

Wider validation chain

• AI Trading —— 决策便宜，行动很贵｜对谈超 3w Star Vibe-Trading 作者浩哲 places point-in-time checks alongside contrary evidence and transaction costs before bounded execution.

Counterevidence & Qualifications

• A point-in-time database can still contain survivorship bias, bad identifiers, selection bias, incorrect corporate-action handling, or unrealistic fills.
• Publication timestamps do not prove that an investor actually received, parsed, and could trade on the information at that moment.
• The source states the principle but does not audit a specific Vibe Trading dataset or backtest implementation.

What Changed

• Added the historical-information boundary needed to distinguish a plausible backtest from future-data leakage.

Related Concepts

• Financial Data Alignment / 金融数据对齐 - broader semantic and temporal alignment layer.
• Quantitative Investing - strategy family that depends on valid historical testing.
• Quantitative Overfitting - separate failure in which valid history is fit too closely.
• Financial Model Risk - wider risk from assumptions, leverage, liquidity, and regime change.
• AI Trading - uses point-in-time checks before a generated hypothesis can support action.