Workflow

Paper trading market data review process

Paper trades are only as reviewable as the data behind them. Use this process to check sources, gaps, timeframes, liquidity assumptions, and simulated fill context before a paper entry becomes part of a serious sample.

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Data checks do not remove risk

Trading Boy does not execute live trades, hold funds, or provide financial advice. Market-data review improves simulated evidence quality, but it does not prove live execution, remove slippage, guarantee fills, or turn paper results into trading recommendations.

Market data review checklist

Run this checklist before a simulated entry is logged, before a missed trade is scored, and before a paper result is used in a benchmark. The goal is not perfect data. The goal is knowing when the evidence is clean enough to compare.

Check	Review question	Pass evidence	Conservative action when weak
Source	Where did the price, candle, or quote come from?	The source is named and consistent across the sample.	Tag the entry as source-mixed or exclude it.
Freshness	Was the data current at decision time?	The timestamp aligns with the paper decision cadence.	Skip the entry if the data is stale.
Gaps	Are candles, ticks, or quotes missing?	Missing periods are absent or explicitly explained.	Mark the sample incomplete and avoid promotion.
Timeframe	Does the timeframe match the written strategy?	The setup and review use the same primary timeframe.	Do not switch timeframes after the result is known.
Spread and liquidity	Could a live fill differ materially from the paper mark?	The review records spread, liquidity proxy, and caveats.	Route the trade to slippage review.
Outlier events	Did news, outage, wick, or venue issue distort the data?	The journal records the event and whether it affects the conclusion.	Separate the outlier from the clean sample.

Example data review

Setup: An AI paper-trading agent flags a simulated ETH breakout. The trader checks the source, candle timestamp, and spread proxy before adding the entry to the paper journal.

Problem: The setup candle exists on one data source but not another. The price also moved during a short exchange outage window, which makes the paper mark less reliable.

Decision: The trader does not count the entry as a clean sample trade. They record it as a data-quality skip, link it to the execution slippage review, and wait for the next setup that passes the same data checks.

Where this fits in the workflow

Use market-data review after watchlist rules and before pre-trade review. The watchlist decides what symbols are eligible. Market-data review decides whether the current information is clean enough to inspect. Pre-trade review decides whether the thesis, invalidation, and paper risk are complete.

This order matters because many paper samples look better when weak data is ignored. A market-data gate makes those weak records visible before they contaminate the sample.

What to record in the journal

Data source: Name the source used for the decision.
Timestamp: Record when the data was observed and when the paper entry was logged.
Timeframe: Match the setup timeframe to the written rule.
Known issue: Note gaps, outages, stale quotes, unusual wicks, or spread concerns.
Sample status: Label the record clean, caveated, skipped, or excluded.

Market data review outcomes

A data review should produce one clear outcome. A clean outcome means the paper entry can continue to pre-trade review. A caveated outcome means the entry can be logged, but the caveat must remain visible when results are summarized. A skipped outcome means the setup was not reviewable at decision time. An excluded outcome means the record can be discussed, but should not be counted in the clean performance sample.

Those labels are intentionally conservative. They prevent a user from treating every attractive candle as valid evidence. They also help a future reviewer understand why the paper sample changed. If many entries are caveated or excluded, the next improvement is usually data-source discipline or a narrower watchlist, not a more aggressive agent prompt.

Market data review FAQ

Why review market data before paper trading?

Market data quality affects whether a simulated entry, exit, or skipped trade can be reviewed fairly. Gaps, stale candles, bad spreads, or unclear sources can make paper evidence misleading.

Does data review make paper fills realistic?

No. Data review can identify weak assumptions, but it cannot prove live fills, latency, fees, or future execution quality.

What should happen when market data is unclear?

The conservative response is to tag the decision as unclear, skip the paper entry, or separate it from the clean sample until the data issue is understood.