An Ositrustees research note on timestamps, market sessions, historical adjustments and source context — and why chart differences should be investigated before they are interpreted.
Two charts can show the same market and still disagree.
Sometimes the difference is obvious. One chart includes extended-hours trading while the other shows only the regular session. Sometimes it is much harder to spot. The ticker appears to match, the date range looks the same, and both screens seem current, yet the prices, candles or volume still do not line up.
The immediate reaction is often to ask which chart is wrong.
That is usually the wrong place to start.
A better question is:
Were both charts built from comparable inputs?
At Ositrustees, this is a useful distinction because chart comparison is not only a visual task. It is also a data-alignment task. Before trying to explain a difference, the researcher first needs to establish what exactly is being compared.
That means looking beyond the picture on the screen.
A chart is the output, not the source
A chart is a visual representation of a set of underlying choices.
Those choices may include the instrument identifier, venue, currency, timestamp convention, aggregation interval, session definition, adjustment methodology and source treatment.
Two interfaces can therefore display charts that look like they refer to the same thing while actually using slightly different assumptions.
Consider a simple example.
One chart shows an equity during regular market hours only. Another includes pre-market and after-hours trading. Both charts may legitimately carry the same ticker. Both may be based on valid market observations. But their highs, lows, volume profile and even some candle structures can differ.
The visual difference is real.
That does not automatically make either chart incorrect.
The first research task is therefore not to decide which chart to trust. It is to determine whether the charts are truly comparable.
Start with instrument identity, not appearance
The name displayed above a chart can be misleadingly simple.
A familiar ticker may exist across multiple venues, currencies or instrument types. A company can have more than one listing. A derivative may track an underlying asset but not behave identically to it. Even symbol mappings can change over time.
This is why a reproducible comparison should begin with instrument identity.
If two charts are supposed to represent the same market, the researcher should be able to answer a few basic questions without ambiguity: what is the instrument, where is it listed, what currency is being quoted, and is the underlying instrument type actually the same?
If that foundation is unclear, every later comparison becomes weaker.
This sounds basic, but many disagreements that initially look like data-quality problems begin here.
Timestamps are more complicated than they look
Time is another frequent source of confusion.
A screen may show a current time. A feed may display a last-update time. A candle may have an opening timestamp or a closing timestamp. A filing may contain a publication time, an acceptance time and a later processing time.
These are different events.
That distinction matters because researchers often treat a visible timestamp as if it described the exact moment represented by every number on the page.
It may not.
A chart refreshed at 14:32 can still contain a value based on a trade from 14:31. A filing displayed by one provider may use the original publication time, while another may record the moment it entered that provider's system. A daily candle can also depend on which time zone or session boundary the source uses.
This is one reason why writing down only “14:30” is rarely enough.
A more useful research note records the time together with the convention behind it.
For example:
19 October 2026, 14:30 UTC — candle close time
is much more useful than:
around 2:30 PM
The difference is not cosmetic. It determines whether another researcher can reproduce the comparison.
Interval changes the story
Charts are also shaped by aggregation.
The same stream of observations can produce very different visual structures depending on whether the data is grouped into one-minute, five-minute, hourly or daily intervals.
A price move that looks dramatic on a five-minute chart may barely register inside a daily candle. Intraday volume may reveal concentration around a specific event while the daily chart compresses that activity into one bar.
Neither representation is necessarily better.
They simply answer different questions.
Problems begin when two charts are compared visually without first confirming that their interval and date range match.
This is especially important when screenshots are used in research notes, online discussions or reviews. A screenshot can make two charts appear comparable while hiding the settings that produced them.
For that reason, the interval should be treated as part of the data context, not merely as a display preference.
Session settings can change more than people expect
One of the most common sources of disagreement is session coverage.
For many markets, a chart may include only regular trading hours, or it may include activity before and after the main session.
That difference can affect far more than the shape of the line.
It can change the opening price, closing context, high and low, volume distribution and apparent gaps between periods.
Imagine that a stock makes a significant move before the regular session opens.
A regular-hours chart may begin after that movement has already taken place. An extended-hours chart may show the move directly.
The two charts now tell visibly different stories, even though both are describing the same instrument.
This is exactly why a chart comparison should not begin with the sentence:
“One of these is wrong.”
It should begin with:
“Do these charts use the same session?”
Historical adjustments create another layer
Historical charts are often transformed after corporate or structural events.
Stock splits are the obvious example, but they are not the only one. Dividend treatment, contract rolls, symbol changes and later data corrections can all affect how a historical series is presented.
This can create surprisingly large differences.
A split-adjusted chart may preserve continuity by revising earlier prices. An unadjusted chart may display the historical prices as originally quoted. A total-return series may incorporate dividends, while a simple price series does not.
All three can be internally consistent.
They are simply measuring different things.
This point became especially relevant in the technical discussion around our earlier Ositrustees market-data article. A useful observation from that discussion was that the adjustment mode itself should be recorded explicitly rather than inferred from the values on the chart.
That is an important research habit.
If a comparison depends on whether a series is raw, split-adjusted, dividend-adjusted or otherwise transformed, that setting belongs next to the timestamp and source in the research record.
Otherwise, two people may believe they are comparing the same historical series when they are not.
Source context matters because data is processed
Researchers often talk about a “data source” as though it were a single, uniform object.
In practice, the path from an original observation to the number displayed on a screen can contain multiple steps.
A provider may receive data, normalize it, map symbols, apply session rules, adjust historical values, calculate derived fields and then display the result through a charting interface.
That means two sources can begin with similar underlying information and still produce different outputs.
This is why source context matters.
The important question is not only:
Where did this number come from?
It is also:
How was this number processed before it reached the chart?
Documentation can help here. Methodology notes, feed descriptions, filing metadata and historical-adjustment policies often explain differences that would otherwise look mysterious.
The goal is not to assume that every discrepancy has an innocent explanation.
The goal is to avoid jumping to an explanation before checking the mechanics first.
Observation should come before explanation
This is one of the most useful habits in analytical work.
Suppose Chart A shows higher volume than Chart B.
The first research note should be simple:
Observation: Chart A displays higher volume for the same date.
Only after that should the researcher begin testing explanations.
Perhaps one chart includes extended hours. Perhaps the sources use different aggregation rules. Perhaps the timestamps are not aligned. Perhaps one source revised the historical series.
Those are hypotheses.
Keeping the observation separate from the explanation reduces the risk of forcing the data into a story too early.
It also makes research easier to review later.
Someone else can look at the same note and understand what was actually observed before seeing how the researcher interpreted it.
Reproducibility is more useful than certainty
A good comparison does not have to end with complete certainty.
Sometimes two sources will still disagree after the obvious settings are aligned.
That is fine.
The more important question is whether another person could reproduce the comparison.
If the instrument, source, timestamp, time zone, interval, session and adjustment method are recorded, then the disagreement becomes a research object rather than just an opinion.
For example, a useful note might read:
AAPL, NASDAQ, USD.
Source A and Source B compared at 14:30 UTC.
Daily interval. Regular session only.
Source A uses split-adjusted history. Source B adjustment method not documented.
Observed difference: historical closing prices diverge before the split date.
That note is far more valuable than:
“The charts don’t match.”
The first can be investigated.
The second cannot.
A practical comparison workflow
The Ositrustees research approach can therefore be reduced to a simple sequence.
First, identify exactly what is being compared.
Then align the basic inputs: instrument, time, interval and session.
After that, check how the source treats historical adjustments and whether its methodology is documented.
Only then record the visible difference.
Interpretation comes last.
This sequence matters because it prevents the explanation from shaping the observation too early.
In shorthand:
Identify → Align → Check → Observe → Interpret
The point is not to make research more complicated.
It is to make it easier to repeat.
When two charts still disagree
Even after careful alignment, differences may remain.
That is not necessarily a failure of the workflow.
It may simply mean that the disagreement exists deeper in the source methodology.
At that stage, useful questions become more specific.
Does one source calculate a derived field differently? Does one use a different closing convention? Has one source revised the historical data? Are the providers using different symbol mappings or corporate-action treatments?
Notice how different these questions are from simply asking which chart is “right.”
The comparison has moved from a visual disagreement to a traceable technical investigation.
That is the real value of structured research.
Final thought
Market-data discrepancies are easy to overinterpret.
A different candle, timestamp or volume figure can look like evidence of a major problem when the underlying cause is something as ordinary as session coverage, interval alignment or historical adjustment.
That does not mean every difference should be dismissed.
It means the difference should be investigated in the correct order.
Start with the instrument.
Align the time.
Match the interval and session.
Understand the adjustment method.
Record the source context.
Then interpret what remains.
For Ositrustees Research Notes, the principle is deliberately simple:
Research. Compare. Verify.
This article is part of the Ositrustees Research Notes series on market data, analytical workflows and reproducible research. It is provided for educational and informational purposes and does not constitute investment advice.
Top comments (1)
You list publication time, acceptance time and processing time as different events, which is the part I would put in the checklist as a field of its own. I hit a concrete case in SEC EDGAR: the acceptanceDateTime in the submissions JSON is labelled UTC, but when I compared 450 filings from 25 companies' files, some files were correct for every filing in them and others were shifted by an extra Eastern offset (+4 h in summer, +5 h in winter). JPMorgan's 06:30 ET filing read 14:30Z instead of 10:30Z. The same filing even read differently in two different files (a company's and its director's).
So "same instrument, same date range, same timestamp label" still isn't enough to call two sources comparable. A cheap addition to your Align step is to test one known event with an independently documented time (a filing with a press-release time, an exchange auction) in each source before trusting any intraday alignment.
Do you record the timestamp convention per source in the note itself, or keep it in a separate config? I built canli-mcp (free, MIT, no key) and it applies the corrected times:
npx -y canli-mcp, github.com/arhancanli/canlicapital.