Data Guide

Most people do not only want a number. They want to know whether they can trust that number. This guide exists to answer exactly that question.

When you compare scenarios in a calculator, small differences in data or assumptions can create large differences in long-term outcomes. Understanding the data layer helps you avoid false confidence and catch the moments when your input settings are doing more work than the data itself.

We separate historical information from user assumptions so you can clearly see which part of your result comes from real past market behavior and which part comes from your own future settings. Those two layers behave very differently under uncertainty.

This page is written for practical decision support. You do not need to read code or technical files to understand how the platform works.

If you are planning monthly investing, comparing metals versus crypto, backtesting the Magnificent 7 stocks, running S&P 500 scenarios, or translating money across inflation periods, this guide helps you read outputs in the right context and with the right skepticism.

For crypto, metals, Magnificent 7 stocks, and S&P 500 mode, we use monthly market close prices from Yahoo Finance as the source for historical price levels. Yahoo Finance provides widely-used free market data and is the standard reference for consumer-grade historical price research.

For inflation, we use official public statistics instead of market data: US CPI-U from the U.S. Bureau of Labor Statistics (BLS) and UK CPI from the UK Office for National Statistics (ONS). BLS has published CPI-U monthly since January 1947, giving approximately 78 years of US inflation history. ONS publishes UK CPI monthly from January 1988, covering roughly 37 years.

BLS describes CPI-U as a monthly measure of average price change for a market basket paid by urban consumers. The basket covers food, housing, apparel, transportation, medical care, and recreation. ONS describes CPI as a consumer basket that is reviewed regularly to stay representative of actual spending patterns.

S&P 500 mode is based on index history for ^GSPC, Yahoo Finance's ticker for the S&P 500 index. The index in its modern 500-stock form dates to March 1957, with predecessor series extending back to the 1920s. Monthly close data in the calculator is sourced from 1950 onwards for practical scenario lengths.

No single data source is perfect in every context, so we focus on transparent handling, consistent monthly processing, and clear limits rather than pretending uncertainty does not exist.

The end goal is simple: give you a stable and understandable base for scenario planning, not investment hype or black-box outputs.

Each month, we run an automatic synchronization cycle to refresh supported crypto, metals, S&P 500, and inflation datasets. The cycle collects the newest monthly close or index value from each source and appends it to the existing series.

After new monthly data is collected, we validate structure and continuity before publication. This includes checking that the new data point connects cleanly to the existing series and that no unexpected gaps or format changes appeared in the source.

If a source is late, unstable, or temporarily inconsistent, we prefer to hold publication instead of pushing uncertain updates. A one-month delay in refresh is far less harmful than a corrupt update that silently shifts thousands of scenario results.

In practice, you may see small result shifts from one month to the next because one extra historical month can slightly change compounding paths. For a 20-year scenario, the addition of one month of real history typically shifts final values by less than 0.5% in either direction.

Your own assumptions still matter most for long horizons, but regular monthly refreshes keep the historical part current and relevant.

For crypto, metals, and S&P 500 mode, the calculator moves through time month by month. It applies the actual monthly percentage change from the source series to your running balance and adds contributions according to your settings.

Contribution frequency is normalized so weekly, monthly, or yearly plans can still be compared on one timeline in a fair way. A plan of $100 per week becomes a monthly equivalent of $433 for calculation purposes.

When a selected horizon goes beyond the last available real market month, the projection continues with your own assumed annual return for the forward part. The transition point is shown in the chart so you can always distinguish history from projection.

Inflation mode is intentionally different. It uses CPI index ratios between years to translate purchasing power, rather than simulating market returns. If the CPI index stood at 172 in 2005 and 315 in 2025, then $1,000 in 2005 corresponds to $1,831 in 2025 purchasing power.

Date ranges are bounded to available official CPI years. This avoids inventing synthetic future inflation values that are not present in the source data.

So the platform combines two layers: historical data replay where data exists, and transparent user assumptions where history ends.

A monthly update is not only a data import. It also includes checks to confirm that data is still usable for stable scenario calculations.

We check for issues such as malformed values, timeline inconsistencies, missing segments, and suspicious jumps that could signal a source problem rather than a real market event.

We also monitor whether calculator outputs still behave as expected around edge cases, such as boundaries near the last historical datapoint, contribution normalization for unusual frequencies, and transitions between historical and assumption-driven phases.

If checks fail, publication is paused and reviewed. We only publish after the dataset passes validation.

This process is designed to protect end users from silent regressions that could otherwise produce misleading projections.

In other words, we prefer delayed accuracy over fast but unreliable updates.

Automatic checks: format integrity, ordering, and continuity of historical ranges

Behavior checks: edge cases around range limits and transition to assumption-based projection

Release rule: publish only when validation passes

Historical performance is informative, but it is never a guarantee of future outcomes. The S&P 500 delivered roughly 10% nominal per year over the past century, but individual decades ranged from negative returns to over 18% annualized. Treat results as scenario guidance, not certainty.

Forward projections are highly sensitive to your assumptions. A plan of $500 per month for 20 years at 7% assumed return produces approximately $261,000. The same plan at 5% produces approximately $205,000. That $56,000 gap comes entirely from a 2 percentage point difference in one input field.

Real-life costs are personal and platform-specific. Taxes, custody costs, product spreads, slippage, and behavioral timing decisions are not universally modeled in one single default output. Use the optional cost fields to get closer to your actual situation.

The S&P 500 series used here is an index-level price history input. It does not include dividend reinvestment by default and should not be interpreted as a complete personalized portfolio return forecast.

Inflation comparison shows purchasing power translation based on official CPI series, which is useful context but still not a full personal cost-of-living model for every household. Housing costs, healthcare, and education often diverge significantly from the broad CPI basket.

For better decisions, compare conservative, base, and optimistic scenarios instead of trusting one headline number.

Use ranges: always compare multiple assumptions

Read phases separately: historical replay and assumption-driven extension are different phases

Stay realistic: include costs and uncertainty in your interpretation

Most errors in using financial calculators are not math errors. They are interpretation errors: reading the output as something it was never designed to be. These five pitfalls appear repeatedly across different asset classes and time frames.

Treating the forward projection as a plan. The number shown for year 15 or year 25 is the mechanical result of applying your assumed return forward. A 1% difference in that assumption on a $300 per month plan over 25 years shifts the final value by approximately $65,000. The projection is only as reliable as the assumption that drives it.

Leaving cost fields at zero. All S&P 500, crypto, and metals modes let you include transaction cost and TER. Leaving them at zero means projecting into a frictionless world. A 0.5% annual TER drag reduces a 20-year outcome by approximately 9% compared to the gross return line. Small persistent costs compound just like returns.

Comparing metal start dates as if they cover the same regime. Before August 1971, gold was fixed at USD 35 per ounce under Bretton Woods. The floating gold market began only after that agreement ended. Post-1971 monthly price swings reflect a genuine market. Using pre-1971 gold data alongside post-1971 data blends two structurally different regimes and can distort long-run average return numbers.

Reading nominal S&P 500 return as real purchasing power. The S&P 500 has averaged roughly 10% nominal per year over long periods. Inflation has historically averaged around 3%, making real returns closer to 7%. If you plan for future spending power, a nominal projection overstates what the money actually buys by approximately 75% over 25 years at 3% inflation.

Comparing coins with very different history lengths as equal. Bitcoin has monthly data from 2010, Ethereum from 2015, and many altcoins only from 2017 or later. A 12-year scenario starting in 2012 uses real data for Bitcoin but relies entirely on your assumed return for a coin that did not exist yet. The shorter the real history, the more the output is assumption-driven rather than data-driven.

The crypto calculator uses monthly historical close prices for each supported coin from Yahoo Finance and derives month-to-month returns from those levels. Bitcoin has monthly data from 2010, giving over 14 years of real history. Ethereum data starts in 2015. Most major altcoins have data from 2017 onwards.

Your contributions are converted into a monthly equivalent so different contribution rhythms can be compared consistently. A plan of $50 per week becomes a monthly equivalent input for the calculation engine.

Each coin has its own earliest available year. Older assets provide longer historical windows, while newer assets naturally start later. The available history length matters because a scenario running through actual market cycles is much more informative than one that relies mostly on your return assumption.

If a month is missing inside historical coverage, the model avoids inventing a synthetic jump for that month and keeps the process stable until the next valid datapoint.

When your selected timeline extends beyond the last real month available, the projection continues using your own annual return assumption.

Display currency helps readability in the interface. It does not rewrite the underlying historical path for the asset itself.

This setup is useful for comparing recurring investment scenarios in volatile markets while keeping data handling transparent.

Supported assets: major crypto assets available in the calculator selection

Core logic: monthly historical replay plus recurring contribution compounding

After data end: user-defined return assumption drives forward projection

The metals calculator follows the same monthly replay concept as crypto mode, but applies it to major metal market series from Yahoo Finance. Gold and silver monthly price data is available from the late 1960s, reflecting the modern floating-price era that began after the end of the Bretton Woods fixed-exchange system in 1971.

Monthly changes are applied over time and your contribution plan is normalized so that contribution frequency remains comparable.

As with other investment modes, if your period runs past the latest historical datapoint, your assumption is used for the forward segment.

This data is a market proxy and is useful for scenario comparison. It is not a full physical ownership model. Practical real-world factors such as storage costs of around 0.1 to 0.5% per year, insurance, dealer premiums of 2 to 10% over spot, and specific product spreads are not universally embedded in the default output.

History depth can vary by metal, so available start years and scenario length flexibility may differ between gold, silver, copper, platinum, palladium, and aluminum.

Used correctly, this tool helps you compare contribution discipline across different metals in a consistent framework.

Typical assets: gold, silver, copper, platinum, palladium, aluminum

Main use case: long-term recurring strategy comparison between metals

Important context: market benchmark behavior, not full physical holding cost simulation

The S&P 500 mode on the homepage uses monthly index close data for ^GSPC from Yahoo Finance and applies those month-to-month changes to your contribution plan. The S&P 500 in its current 500-stock form dates to March 1957, but monthly data available in the calculator extends back to 1950 using predecessor series.

S&P 500 is widely used as a broad benchmark for large US equities, delivering an average gross return of roughly 10% per year over the past 70 years. This makes it a useful reference scenario for long-term investing discussions, though individual decades varied from strongly negative to over 18% annualized.

To keep timeline calculations stable, missing months in the raw sequence are handled in a consistent way so that scenario outputs remain comparable.

You can optionally include transaction cost per contribution and TER drag, which helps approximate investing friction instead of assuming a cost-free world. A typical low-cost index fund carries a TER of 0.03 to 0.20% per year.

When selected years move beyond historical coverage, your own assumed annual return is used for the forward period.

Interpret this as an index-based planning model. It supports comparison and expectation setting, but it is not a promise of your personal future portfolio path.

Best for: testing disciplined recurring investment scenarios against broad index history

Optional realism: transaction costs and TER impact can be included

Forward extension: driven by your own assumptions after historical range

The Magnificent 7 calculator uses monthly closing prices for seven large-cap US technology stocks from Yahoo Finance: Apple (AAPL), Microsoft (MSFT), Alphabet (GOOGL), Amazon (AMZN), NVIDIA (NVDA), Meta (META), and Tesla (TSLA). These are the same kind of split-adjusted monthly close series used for crypto and metals, applied here to individual equities.

Coverage dates differ significantly across the seven stocks. Apple data is available from 1984 and Microsoft from 1986. Amazon has monthly data from 1997 and NVIDIA from 1999. Alphabet entered public markets in 2004, Tesla in 2010, and Meta in 2012. For fair cross-stock comparisons, a common start year of 2013 or later covers all seven stocks with a full calendar year of data for each.

The calculator stores per-share dividend amounts alongside the monthly price series. Dividend data is sourced from Yahoo Finance dividend event records and stored as the declared cash amount per share for each payment month. Apple has paid a cash dividend since 2012 at roughly $0.25 per share per quarter. Microsoft has paid dividends since 2003 at roughly $0.75 per share per quarter. Alphabet, Amazon, NVIDIA, Tesla, and Meta currently pay no regular cash dividend. When the "Reinvest dividends" toggle is enabled in More options, the per-share dividend amount is divided by the previous month's price to produce a yield fraction, which is then added to that month's return and compounded into the running balance. The yearly table shows a dividend column with the cumulative reinvested amount per year when this feature is active.

By default, dividend reinvestment is off and results reflect price returns only. This matches how most backtesting tools present individual stock results. Enabling reinvestment for Apple or Microsoft adds a small but compounding layer on top of price returns — over a 20-year backtest the cumulative effect can reach several thousand dollars depending on the starting balance and share price at each payment date. For Alphabet, Amazon, NVIDIA, Tesla, and Meta the toggle has no effect since no dividend data exists for those tickers.

Data is refreshed on the same monthly cycle as the crypto and metals series. After each refresh, the new month is appended to the existing series and validated before publication. The same quality checks that apply to crypto and metals apply here: format integrity, ordering, and continuity checks before the update is released.

The calculation engine is identical to the crypto and metals backtest: month-by-month compounding of real price changes, with your assumed annual return applied after the last available data month if the end year extends beyond available history.

Dividend reinvestment: toggle under More options to include per-share dividend data for Apple and Microsoft; off by default

Split-adjusted: historical prices are restated to reflect all stock splits, so older prices are comparable to today

Coverage starts: AAPL 1984, MSFT 1986, AMZN 1997, NVDA 1999, GOOGL 2004, TSLA 2010, META 2012

Inflation mode uses official CPI series from public institutions: BLS for the United States and ONS for the United Kingdom. BLS has published CPI-U monthly since January 1947, making it one of the longest-running official consumer price series in the world. ONS publishes UK CPI monthly from January 1988.

This is intentionally different from market return tools. The inflation calculator is focused on purchasing power translation between years.

It compares CPI index levels between your selected dates and shows what the same amount of money represents in the target year. For example, if US CPI stood at 172 in 2005 and 315 in 2025, then $10,000 in 2005 had the same purchasing power as approximately $18,310 in 2025.

For usability and transparency, dates are bounded to available CPI years rather than extended with guessed future inflation values.

This approach gives a conservative result grounded in official published series, which is especially useful when you want to compare nominal money versus real buying power.

If you use this together with investment scenarios, you can better understand the difference between portfolio growth and what that growth may actually buy later. A nominal portfolio that doubled may have only grown 40% in real purchasing power if inflation was high during the period.

Source type: official public inflation statistics (CPI)

Main purpose: translate purchasing power across years

Range rule: no extrapolation beyond available official CPI years