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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.
为什么这很重要
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.
- · 专为 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. 打造。
- · 最可能的变现方式：SaaS subscription。
痛点叙事
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.
得分构成
市场信号
Go-to-Market 启动方案
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 方案 · 1-2 周
- 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
差异化
为什么这件事可能失败
自我反驳——最重要的信任度信号
- 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.
证据综述
AI 如何合成此洞察——无原话引用
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.
行动计划
在写代码之前，先验证这个商机
推荐下一步
直接做
需求信号强烈。痛点真实、付费意愿明确——启动 MVP 开发。
落地页文案包
基于真实 Reddit 评论整理的即用文案，可直接粘贴到落地页
主标题
Point-in-time backtesting platform for value investors
副标题
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.
目标用户
适合：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.
功能列表
✓ 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
去哪里验证
把落地页链接发布到 r/r/ValueInvesting——这里就是这些痛点被发现的地方。
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