This insight was synthesized by AI from public community discussions. We do not display original user posts or comments verbatim—all content has been rewritten and aggregated. Verify before acting on it.
Point-in-time backtesting platform for value investors
A SaaS platform that provides individual investors with survivorship-bias-free point-in-time fundamental data, automated DCF backtesting, and proper statistical testing. Users can define valuation rules before running backtests, test multiple holding periods, and validate strategies against historical data.
Why this matters
You spend hours building a DCF model to value stocks, but when you try to backtest it, you realize you need 13 years of point-in-time SEC filings, exact treasury yields on each date, and delisted companies that went to zero. Your data only covers companies still listed today, making your backtest look better than reality. You manually code statistical adjustments for overlapping return windows and discover your trailing-growth input systematically selects value traps. Without affordable access to clean historical data and proper testing frameworks, you cannot trust your own model enough to invest real money based on it.
- · Built for Individual value investors and quantitative researchers who build their own DCF models and want to validate them against historical data without paying for Bloomberg terminals..
- · Most likely monetization: SaaS subscription.
The Pain · Narrative
You spend hours building a DCF model to value stocks, but when you try to backtest it, you realize you need 13 years of point-in-time SEC filings, exact treasury yields on each date, and delisted companies that went to zero. Your data only covers companies still listed today, making your backtest look better than reality. You manually code statistical adjustments for overlapping return windows and discover your trailing-growth input systematically selects value traps. Without affordable access to clean historical data and proper testing frameworks, you cannot trust your own model enough to invest real money based on it.
Score Breakdown
Market Signal
Go-to-Market
Individual value investors who actively build DCF models in spreadsheets and want to validate them against historical data.
~200K-500K globally based on investing community sizes and active fundamental analysis practitioners
Investing community organic posts on Reddit and Hacker News with detailed backtest methodology
$49/month for individual tier with point-in-time data access
30 paying users within 60 days of launch from organic community posts
MVP Scope · 1–2 weeks
- Build SEC EDGAR API integration to fetch historical 10-K/10-Q filings with correct filing dates
- Create stock universe builder that includes delisted companies using free CRSP alternatives
- Implement basic DCF calculation engine with customizable growth inputs
- Set up PostgreSQL schema for point-in-time fundamental data storage
- Create simple CLI tool to run a single backtest with defined rules
- Add treasury yield integration from FRED API for point-in-time discount rates
- Implement quarterly rebalancing logic with 1y, 3y, and 5y holding periods
- Add Newey-West standard error calculation for statistical significance
- Build web UI for defining backtest rules and viewing results
- Create comparison dashboard showing cheap quintile vs equal-weight universe vs SPY
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1Data licensing costs for survivorship-bias-free historical data may exceed what individual investors are willing to pay, making the unit economics unsustainable.
- 2The target audience of serious individual value investors may be too small and too price-sensitive to sustain a SaaS business, especially if they can approximate results with free tools.
- 3Regulatory scrutiny could increase if the platform is perceived as providing investment recommendations rather than research tools, requiring costly compliance measures.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
The original poster spent significant effort manually building a point-in-time backtest over 13 years, noting delisted names as a key weak spot. Multiple commenters identified survivorship bias and overlapping return windows as critical statistical issues. Several users expressed interest in the published research and shared their own DIY backtesting platforms, indicating unmet demand for an accessible solution. The discussion revealed that proper point-in-time data handling and statistical robustness are beyond what most individual investors can build themselves.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Build
Strong demand signals detected. Real pain, real willingness to pay — start building an MVP.
Landing Page Copy Kit
Ready-to-paste copy based on real Reddit community language — no editing required
Headline
Point-in-time backtesting platform for value investors
Sub-headline
A SaaS platform that provides individual investors with survivorship-bias-free point-in-time fundamental data, automated DCF backtesting, and proper statistical testing. Users can define valuation rules before running backtests, test multiple holding periods, and validate strategies against historical data.
Who It's For
For Individual value investors and quantitative researchers who build their own DCF models and want to validate them against historical data without paying for Bloomberg terminals.
Feature List
✓ Survivorship-bias-free historical stock universe including delisted companies ✓ Point-in-time SEC filing data aligned by actual filing dates ✓ Automated DCF and reverse DCF engine with customizable inputs ✓ Statistical testing with Newey-West standard errors for overlapping periods ✓ Multi-period backtesting (1y, 3y, 5y holding periods) with quarterly rebalancing
Where to Validate
Share your landing page in r/r/ValueInvesting — that's exactly where these pain points were discovered.
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