Financial Modeling Prep vs Sharadar: Fundamental Data APIs for Quant Backtests

I spent two weeks rebuilding the same equity backtest twice — once on Financial Modeling Prep, once on Sharadar’s SF1 fundamentals via Nasdaq Data Link — and the two versions disagreed by a margin large enough to flip a “promising” strategy into a losing one. That gap is the whole story of this comparison. Both services hand you company financials over an API. Both will let you compute a price-to-earnings ratio for Apple in 2014. But only one of them reliably tells you the P/E that an investor could have known on a given day in 2014, and that distinction is the difference between a backtest that means something and a backtest that quietly lies to you. I went into this expecting the comparison to be about endpoints and pricing. It turned out to be about a single concept — point-in-time correctness — that most people discover only after they have wasted months chasing a backtested edge that never existed.

This is a developer’s comparison, not a trader’s. I care about how the data arrives, how clean it is, what it costs, and whether the historical record is honest. If you have read our earlier piece on why backtests lie, this is the practical follow-up: here are two concrete tools sitting on opposite sides of that exact problem.

What Each Service Actually Gives You

Financial Modeling Prep (FMP) is a broad, developer-friendly REST API. You sign up, get an API key, and immediately have access to income statements, balance sheets, cash flow statements, financial ratios, key metrics, historical and real-time prices, company profiles, analyst estimates, earnings calendars, and a long tail of other endpoints — SEC filings, insider trades, institutional holdings, ETF constituents. Everything is JSON over HTTPS, one resource per endpoint, keyed by ticker. You can be pulling Microsoft’s last forty quarters of revenue in about five minutes from a cold start. The pricing is approachable: there is a free tier with tight rate limits, and paid plans that run from roughly a couple hundred to several hundred dollars per year as of mid-2026, depending on call volume and how much history you need. For a solo developer building a screener, a dashboard, or a learning project, this is close to ideal — the surface area is huge and the cost is low.

Sharadar is a different animal. It is a data publisher, and its flagship product for equity research is the SF1 table — Core US Fundamentals — distributed through Nasdaq Data Link (the platform formerly known as Quandl). Where FMP gives you a sprawling menu of live endpoints, Sharadar gives you a small number of carefully constructed, downloadable tables: SF1 for fundamentals, SEP for prices, plus companion tables for tickers, actions, and metadata. The defining feature of SF1 is that it is built explicitly for backtesting. It is point-in-time and as-reported, it covers delisted companies, and it is engineered so that when you ask “what did the data look like on date X,” the answer reflects only what was actually filed and public by date X. That focus is the entire value proposition, and it comes at a meaningfully higher price — a research subscription to the Sharadar bundle runs in the low hundreds of dollars per month range as of mid-2026, an order of magnitude above what a hobbyist pays FMP.

So at a glance: FMP is wide and cheap and live; Sharadar is narrow and expensive and historically rigorous. To understand why the price gap is justified for serious work, you have to understand the one thing Sharadar is selling that FMP largely is not.

The Thing That Decides Everything: Point-in-Time Data

A backtest simulates running a strategy through history. For that simulation to be honest, on every historical date your code must see only information that was genuinely available on that date — no peeking at the future. The most common way this breaks with fundamental data is look-ahead bias through restatements and reporting lags.

Here is the concrete failure. A company reports Q1 earnings, and the figure gets entered into a database. Months later, the company restates that quarter — an accounting correction, a reclassification, a merger adjustment. A typical “current” financial database overwrites the original number with the restated one. Now, when your backtest asks for that company’s Q1 earnings, it gets the corrected value, even though no investor on the original report date could have known it. Worse, many databases stamp financials by the period they cover (the quarter ended March 31) rather than by the date they were actually filed (often six to eight weeks later). If your backtest reads March 31 fundamentals on March 31, it is trading on a 10-Q that did not exist yet. Both effects nudge backtest returns upward, and both are invisible unless you go looking.

Sharadar’s SF1 is built to defeat exactly this. It distinguishes between dimensions — as-reported (ARQ/ARY), most-recent-reported (MRQ/MRY), and trailing-twelve-month variants — and critically, every record carries the actual filing date (datekey) alongside the reporting period. When you query “as of” a date, you get the data as it stood on that date, restatements and all, with the reporting lag respected. That is the gold-standard behavior for a backtest, and it is the reason quant practitioners reach for Sharadar despite the cost.

FMP, by contrast, is primarily a current-view service. Its statement endpoints give you the company’s financials, and historically they have leaned toward the latest available figures rather than a reconstructable point-in-time snapshot. FMP does expose filing dates and an “as-reported” family of endpoints, so you are not entirely blind — a careful developer can use the filing date to enforce a reporting lag and avoid the most egregious look-ahead. But you are doing the point-in-time discipline yourself, by hand, on data that was not primarily designed to make that easy, and you have less protection against the restatement-overwrite problem. With Sharadar, point-in-time correctness is the product. With FMP, it is something you bolt on and hope you got right.

Coverage, History, and Survivorship Bias

The second way backtests lie is survivorship bias: testing your strategy only on companies that still exist today, silently excluding the ones that went bankrupt, got delisted, or were acquired. Since failures are exactly the outcomes a strategy needs to avoid, a survivorship-biased universe flatters almost any approach.

Sharadar is explicit and serious about including delisted companies. SF1 and the ticker tables retain securities that have left the market, with metadata about their fate, so you can construct a historical universe that contains the losers as well as the survivors. For backtesting, this is not a nice-to-have; it is mandatory. Coverage spans US equities with roughly two decades of fundamental history, which is enough to span multiple market regimes including the 2008 crisis and the 2020 shock.

FMP’s coverage is impressively broad in breadth — it reaches well beyond US equities into international markets, ETFs, crypto, forex, and commodities — and it offers many years of historical statements. Where it is weaker, for backtesting purposes specifically, is in giving you a clean, ready-made delisted-inclusive universe with reliable point-in-time membership. You can get a lot of history out of FMP, but assembling a survivorship-bias-free historical universe from it is more assembly-required than with Sharadar’s purpose-built tables.

Delivery and Developer Experience

This is where FMP wins comfortably, and where it earns its place as the default for side projects.

FMP is a pleasure to integrate. It is plain REST, one ticker at a time (with batch options on higher tiers), JSON responses with intuitive field names, and documentation you can skim and immediately use. There is essentially zero conceptual overhead: request, parse, done. For interactive work, building a web app, or pulling a few hundred tickers, this model is exactly right.

Sharadar’s delivery model reflects its bulk-research orientation. Through Nasdaq Data Link you can hit a table API, but the intended workflow for backtesting is to download entire tables — the full SF1 fundamentals file — and work against a local copy, often refreshed on a schedule. There are well-supported Python paths via the nasdaqdatalink (formerly quandl) client, and the data drops neatly into pandas. But you are working with multi-dimensional tables and datekey semantics, not point-and-shoot endpoints. The learning curve is real: you have to understand the dimension codes and the as-of query model before the data does what you want. That friction is the cost of rigor — the same structure that makes the integration heavier is what makes point-in-time queries trustworthy.

Who Should Use Which

Use Financial Modeling Prep if you are building something that consumes current or near-current fundamentals: a stock screener, a portfolio dashboard, a research tool that shows users today’s ratios, or a learning project where you are figuring out how the mechanics of pulling and computing financial data work. The breadth is enormous, the API is frictionless, and the cost is low enough that it disappears into a side-project budget. You can also do light backtesting on FMP if you are disciplined — pull filing dates, enforce a conservative reporting lag, and treat your results as directional rather than precise. Just know that you are compensating for a data model that was not built for the job.

Use Sharadar if you are backtesting a strategy with the intent to allocate real capital to it, or if you are doing academic-grade equity research where reproducibility and historical honesty are non-negotiable. The point-in-time fundamentals and delisted-inclusive universe remove the two largest sources of self-deception — look-ahead and survivorship bias — at the data layer, so your simulation is testing your idea rather than testing your data’s accidental knowledge of the future. The higher price and steeper learning curve are the entry fee for getting an answer you can trust.

The honest middle path, and the one I would actually recommend to most developers moving from hobby toward seriousness: prototype on FMP, and the moment a backtested strategy looks good enough that you are tempted to fund it, re-run it on Sharadar before you believe a single number. If the edge survives the switch to point-in-time data, you have something. If it does not — and it often will not — Sharadar just saved you from trading a mirage.

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