How We Reconstructed 10 Years of BTC Data Without Losing Accuracy

Everyone loves a "10-year BTC return" chart.

Very few people ask whether the math behind it is actually correct.

When we started building Clarity, one of the first things we needed was long-term Bitcoin performance data. Not simulated. Not approximated. Actual historical performance that could be used for portfolio aggregation and DCA math.

What we learned very quickly is this: reconstructing 10 years of BTC data sounds trivial, but it is full of traps. If you take shortcuts, your numbers will look clean and still be wrong.

Here is how we approached it.

The Illusion of "Easy" Bitcoin History

Bitcoin is the most liquid and well-documented crypto asset. So it is tempting to assume that:

• Historical price data is complete
• Daily prices are enough
• You can just multiply returns and call it a day

That assumption breaks down fast.

When you zoom out to 10 years, you run into several problems at once:

• Missing or inconsistent early data
• Exchange fragmentation
• Timezone and timestamp drift
• Implicit survivorship assumptions
• Subtle math errors in return aggregation

Cryptocurrency return series exhibit high intricacy and non-linear behavior, making simple aggregation assumptions unreliable. Empirical research shows cryptocurrency returns are structurally more complex than traditional assets.

Most dashboards hide these problems behind smooth charts.

We decided not to.

The First Question We Asked

Before writing any code, we asked a simple question:

What does "10-year BTC performance" actually mean?

Is it:

• Price change from one arbitrary timestamp to another?
• Daily close-to-close return series?
• Capital-weighted aggregation across exchanges?
• A DCA strategy with real execution dates?

If you do not answer this explicitly, you end up mixing definitions. That is how backtests quietly lie.

For Clarity, we defined Bitcoin performance as:

A continuous, timestamp-aligned return series built from real historical price observations, suitable for portfolio aggregation and DCA math.

That definition drove every engineering decision after.

Handling Missing and Messy Early Data

Early Bitcoin data is not clean.

Some days have sparse exchange coverage. Some prices come from venues that no longer exist. Some timestamps drift depending on the source.

Prices in the early era of Bitcoin varied dramatically and are documented in granular historical records such as Bitcoin's long term price history , highlighting extreme moves and gaps in early price coverage.

A naive approach would be to forward-fill gaps or interpolate missing prices.

We did not do that.

Instead, we treated missing data as a first-class signal, not something to paper over.

Our approach looked like this conceptually:

For each historical period:

• Validate price source availability
• Normalize timestamps to a single reference
• Reject periods with insufficient market coverage
• Only construct returns where continuity is provable

This means we sometimes preferred less data over fake precision. The result is a dataset that may look slightly rough at the edges, but does not invent history.

Why Daily Prices Are Not Enough

Many platforms reconstruct long-term performance using daily closing price series without accounting for intraday or higher-frequency dynamics . That seems reasonable until you use the data for DCA or portfolio aggregation.

Here is the problem:

• DCA depends on when capital is deployed
• Portfolio math depends on path consistency, not just endpoints

If you collapse everything into daily closes without aligning execution logic, you distort results. Over 10 years, small distortions compound into large errors.

We reconstructed Bitcoin's history as a time-ordered return series, not just a price chart. That allowed us to:

• Apply DCA logic using real intervals
• Aggregate BTC alongside other assets correctly
• Avoid return smoothing that flatters performance

The Quiet Math Errors Most Dashboards Make

One of the most common mistakes we saw was this:

Calculating long-term returns by chaining percentage changes without respecting capital flow.

This problem is linked to broader challenges in simulation and backtesting, where historical data is imperfect or aggregated incorrectly. Backtesting is the practice of using historical data to estimate how a strategy would have performed.

This shows up in two places:

1. DCA calculations that assume uniform exposure
2. Portfolio backtests that rebalance without real constraints

On Clarity, we model returns as capital-aware transformations, not just price deltas.

For each contribution or rebalance:

• Track capital deployed at that time
• Apply returns forward from that point only
• Aggregate value, not percentages

This avoids overstating gains and makes drawdowns visible instead of hidden.

Avoiding Survivorship Bias by Design

Bitcoin did not always look inevitable.

Early periods include long drawdowns, thin liquidity, and violent regime shifts. Many reconstructions implicitly assume survival by anchoring everything to today's BTC.

We avoided that by never normalizing early data to future outcomes. Every return is calculated forward in time, using only information available at that moment.

No hindsight smoothing. No narrative shortcuts.

What the Final Dataset Enables

By taking this approach, we ended up with a Bitcoin history that can safely power:

• True 10-year performance comparisons
• Accurate DCA vs lump sum analysis
• Portfolio aggregation alongside other assets
• Benchmarks that do not flatter outcomes

Most importantly, it lets us say this honestly:

If Bitcoin's history looks good in Clarity, it is because it earned it.

Why This Matters Beyond Bitcoin

Bitcoin is the easy case.

If your system cannot reconstruct BTC accurately over 10 years, it will absolutely fail when you introduce:

• Assets with shorter histories
• Assets that died
• Assets with structural changes
• Indices and composite benchmarks

BTC forced us to build the right foundations early. Everything else builds on that.

Closing Thought

Long-term crypto analytics fail quietly.

They do not crash. They do not throw errors. They simply tell a cleaner story than reality deserves.

Reconstructing 10 years of BTC data forced us to choose between convenience and correctness. We chose correctness.

If you are building analytics, dashboards, or indices, the uncomfortable question is not "can I show a 10-year chart?"

It is whether that chart deserves to be trusted.