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Quality Drawdown Screener for Value Investors
A SaaS platform that screens for high-quality companies (strong fundamentals, competitive moats, healthy balance sheets) currently experiencing significant price drawdowns, combining automated fundamental scoring with intrinsic value estimation and real-time alerts when quality stocks enter deep discount territory.
Why this matters
You are an active value investor who spends hours manually scanning stock screeners, reading earnings reports, and browsing community forums to find quality companies trading at a discount. When you find a stock down 30% from its highs, you have no quick way to determine whether this is a temporary dip in a great business or the beginning of a structural decline. You end up relying on anecdotal observations and scattered forum discussions to make decisions about thousands of dollars. Existing screeners show you price declines but do not combine that with fundamental quality scoring or intrinsic value estimation, leaving you to do the cross-referencing manually in spreadsheets.
- · Built for Active retail value investors who research individual stocks and deploy capital into conviction plays, typically managing $10K-$500K portfolios with 5-20 individual stock positions.
- · Most likely monetization: SaaS subscription with freemium tier.
The Pain · Narrative
You are an active value investor who spends hours manually scanning stock screeners, reading earnings reports, and browsing community forums to find quality companies trading at a discount. When you find a stock down 30% from its highs, you have no quick way to determine whether this is a temporary dip in a great business or the beginning of a structural decline. You end up relying on anecdotal observations and scattered forum discussions to make decisions about thousands of dollars. Existing screeners show you price declines but do not combine that with fundamental quality scoring or intrinsic value estimation, leaving you to do the cross-referencing manually in spreadsheets.
Score Breakdown
Market Signal
Go-to-Market
Individual retail value investors managing $25K-$250K portfolios who actively research and buy individual beaten-down stocks at least monthly
~300K-500K active in the US, potentially 1M+ globally
Organic posts in investing communities (r/ValueInvesting, r/stocks, r/investing) with a free screener tool that naturally showcases value
$29/month for full access, free tier with 5 stock reports per month
500 sign-ups and 25 paying users within 30 days of launch via organic community posts
MVP Scope · 1–2 weeks
- Set up Financial Modeling Prep API integration for fundamental data and price history
- Build quality score algorithm using ROIC, debt-to-equity, free cash flow consistency, and gross margin stability
- Create drawdown calculation logic (% from 52-week high, % from all-time high, decline duration)
- Build simple web UI with a sortable table of stocks matching quality + drawdown criteria
- Deploy to production with at least 100 pre-screened stocks in the database
- Add intrinsic value estimator with DCF model and configurable assumptions for turnaround scenarios
- Implement email alert system for when quality stocks cross user-defined drawdown thresholds
- Add stock detail page showing quality score breakdown, drawdown history, and valuation estimate
- Create free tier with daily report limit and subscription payment integration via Stripe
- Write and post organic content in investing communities showcasing 3-5 real screen results
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1Financial data APIs are expensive (potentially $500-2000/month for decent coverage), and at $29/month you need 50-70 paying users just to cover data costs before any other expenses
- 2During sustained bull markets the number of quality stocks in deep drawdown shrinks dramatically, making the tool feel empty and causing mass churn
- 3Users may sign up for the free tier, run their screens, find their picks, and never convert to paid because the core value is a one-time discovery rather than ongoing monitoring
Evidence Summary
How AI synthesized this insight — no verbatim quotes
Approximately 15 commenters in the discussion manually listed stock tickers they consider beaten-down, with most providing only one-sentence rationales. Several users debated brand health anecdotally without data. One user explicitly asked what a stock is worth per share, and another mentioned passing valuation screens without referencing a specific tool. The scatter of manual picks and lack of systematic screening suggests a clear gap that a purpose-built tool could fill.
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
Quality Drawdown Screener for Value Investors
Sub-headline
A SaaS platform that screens for high-quality companies (strong fundamentals, competitive moats, healthy balance sheets) currently experiencing significant price drawdowns, combining automated fundamental scoring with intrinsic value estimation and real-time alerts when quality stocks enter deep discount territory.
Who It's For
For Active retail value investors who research individual stocks and deploy capital into conviction plays, typically managing $10K-$500K portfolios with 5-20 individual stock positions
Feature List
✓ Quality score composite using profitability, balance sheet strength, and competitive moat metrics ✓ Drawdown tracker showing % decline from 52-week and all-time highs with decline duration analysis ✓ Intrinsic value estimator with bull/base/bear scenarios for turnaround situations ✓ Alert system notifying when quality stocks cross configurable drawdown thresholds ✓ Side-by-side comparison of fundamental quality vs. price decline to distinguish temporary dips from structural decline
Where to Validate
Share your landing page in r/r/ValueInvesting — that's exactly where these pain points were discovered.
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