Developers, quantitative researchers, and fintech startups face a persistent dilemma when building financial tools: high costs and unreliable data. Legacy data vendors charge prohibitive fees for commercial access, while affordable alternatives often lack basic transparency. Without clean point-in-time data and clear line-of-sight to source filings, backtests suffer from lookahead bias and financial models run the risk of relying on unverified numbers.
Solving this data integrity challenge is Second Dot LLC with the launch of StockFit API. Designed to streamline market data access directly from SEC EDGAR, StockFit provides over 90 endpoints backed by full audit trails and point-in-time accuracy. In this Executive Q&A, Andy Reimann, founder of Second Dot LLC, explains how his personal frustration with expensive market data inspired him to build an audit-grade, accessible API platform for developers and institutional workflows.
Q: What specific data challenges and cost barriers did you encounter in your own work that inspired you to build StockFit API?
Andy Reimann: When I built a long-term investor app 5 years ago, I had to subscribe to 5 different vendors. No single vendor gave me everything I needed, and the ones that came close had data quality issues. One example: an endpoint for a company’s executives with their tenure. A few vendors offered it, none actually had good coverage. That mix of APIs added up fast, and the experience stuck with me. It was the biggest inspiration to go and change the landscape with StockFit API. I basically now have the API I wish existed 5 years ago.
Q: Legacy data providers often deliver normalized numbers without context. How does StockFit API ensure audit-grade accuracy and full traceability back to original SEC EDGAR filings?
Andy Reimann: Think of it as one huge database table holding every single raw fact, each linked to the filing it came from. The base structure everything else is built on already contains what you need, so when I derive a normalized view, that provenance isn’t lost. It’s right there for everyone to see. On top of that, the as-reported endpoint lets you query all raw data broken down per period, with the sources. If a number looks off, everything is exposed for you to track it down precisely.
Q: Quantitative researchers frequently struggle with lookahead bias during backtesting. How does StockFit’s Point-In-Time (PIT) Temporal Rollback Ledger solve this problem for developers?
Andy Reimann: An example explains this best. A company reports revenue X, then later files something that retroactively restates or amends that same figure to Y. That information usually stays hidden with other vendors: they hand you the consolidated Y as if it had always been Y. If your algorithm ignores that, you are working with numbers that logically didn’t exist at the original report time. StockFit gives you Y as well, but in the same response you get the full audit trail broken down by number, so you know when revenue moved from X to Y and in which filing it happened. That works even if the figure was revised multiple times. StockFit also serves fundamentals for delisted companies, which removes survivorship bias for EDGAR filers. Together, that covers two of the hardest data problems in quantitative research. You can also get statements in any currency you like, automatically converted with the exchange rates from the period of the filing – another unique and useful feature.
Q: StockFit API launches with over 90 endpoints covering everything from insider transactions to native MCP server integrations for AI agents. How did you choose these specific features for modern fintech workflows?
Andy Reimann: Mostly my own needs from 5 years ago, plus early user feedback. Beyond the obvious ones like the income statement, StockFit ships a lot of more exotic endpoints: business segmentation, revenue segmentation, sector and industry specific metrics. Those are the result of multi-year research and development. And since AI will only play a bigger role, shipping with MCP on day one was the logical step, not just for customers but for my own development too. I can point an AI at my own MCP server and run all sorts of checks in no time.
Q: High pricing often locks early-stage startups and independent developers out of commercial-grade data. What is your strategy for keeping StockFit API affordable without sacrificing data depth?
Andy Reimann: My stance on pricing is simple: SEC data is fully public. All I do is change its structure. That is at times incredibly complex, but it isn’t expensive, so there’s no reason to artificially inflate the price. A higher price point would only be justifiable if I were paying high licensing fees. Many API vendors buy pre-cleaned data from feeds like FactSet and redistribute it. I skipped that part entirely, because with AI we have all we need to solve these once hard problems. It means I always have to go the extra mile, and I’m happy to do that when it lets me price lower. Daily fund holdings are one example: the only primary source is the fund’s own website, so I built my own scraping infrastructure to collect them at scale.
Q: Looking ahead, how do you see transparent, point-in-time financial data shaping the next generation of AI investment tools and automated trading systems?
Andy Reimann: I see StockFit as a real opportunity for any small startup in the situation I was in 5 years ago. Feedback from many customer meetings tells me I am definitely not alone with that experience. But the bigger opportunity is auditability. AI is increasingly used as a financial advisor, which is dangerous by itself. The moment you equip an AI with auditable data, the picture changes materially. Transparency and auditability are what move agent workflows from a playground tool to real infrastructure.
Access to reliable, verifiable financial data is fundamental to building successful investment applications and robust quantitative strategies. StockFit API addresses a long-standing industry pain point by uniting complete SEC filing traceability, point-in-time accuracy, and developer-friendly pricing into a single platform.
As financial technology moves toward automated execution engines and AI-driven analysis, data provenance and auditability will only become more critical. By removing traditional cost bottlenecks and delivering true source transparency, platforms like StockFit API are empowering startups and independent researchers to innovate with confidence.
To learn more, visit https://developer.stockfit.io/
