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Evidence-Based Factor Screener
Build a SaaS stock screener that ranks indicators by empirical strength, then lets users screen equities using value, quality, and momentum factors with transparent evidence scores. The product should emphasize historical robustness, transaction-cost awareness, and sector-specific behavior rather than hype around any single indicator.
Why this matters
You want to select stocks with methods that have more than a good story behind them, but every indicator seems to have defenders, critics, and conflicting backtests. You can find academic papers, blog posts, and charting tools, yet none of them make it easy to answer a practical question: which signals still look credible after costs, across sectors, and over changing market conditions? If you are not already running your own research stack, you end up stitching together books, spreadsheets, and partial backtests. That creates uncertainty right where confidence matters most: before you commit capital.
- · Built for Self-directed investors and serious retail traders who want academically grounded stock screens without building their own quant pipeline..
- · Most likely monetization: SaaS subscription.
The Pain · Narrative
You want to select stocks with methods that have more than a good story behind them, but every indicator seems to have defenders, critics, and conflicting backtests. You can find academic papers, blog posts, and charting tools, yet none of them make it easy to answer a practical question: which signals still look credible after costs, across sectors, and over changing market conditions? If you are not already running your own research stack, you end up stitching together books, spreadsheets, and partial backtests. That creates uncertainty right where confidence matters most: before you commit capital.
Score Breakdown
Market Signal
Go-to-Market
Independent investors who already use stock screeners and want more evidence-driven factor selection without writing code.
~100K-300K active globally
SEO long-tail
$29/month
25 paying users from search traffic and finance-community outreach within 30 days
MVP Scope · 1–2 weeks
- Define 10 core factors with formulas and plain-English explanations
- Connect one market data source and one fundamentals data source
- Build a simple database schema for prices, fundamentals, and factor scores
- Create a factor evidence page with research summary, caveats, and cost notes
- Ship a basic stock screener UI with filters for value and cash-flow metrics
- Add sector-relative comparisons for each factor
- Build historical factor performance charts by decile
- Add simple transaction-cost assumptions to reported results
- Implement watchlists and saved screens
- Launch a landing page with one free evidence report to collect emails
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1The product may be perceived as another generic stock screener unless the evidence layer is clearly differentiated and trusted.
- 2Users may not convert if they can replicate core screens using free finance sites and public factor articles.
- 3Data licensing costs could compress margins before subscriber volume is high enough.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
The discussion repeatedly favors value and cash-flow-oriented metrics over common chart indicators when the goal is stock selection. Several participants point to long-horizon factor research, while others warn that technical indicators often degrade after costs or regime changes. There is also repeated interest in combining signals rather than trusting one metric alone, which supports a screener that surfaces evidence, caveats, and implementation context.
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
Evidence-Based Factor Screener
Sub-headline
Build a SaaS stock screener that ranks indicators by empirical strength, then lets users screen equities using value, quality, and momentum factors with transparent evidence scores. The product should emphasize historical robustness, transaction-cost awareness, and sector-specific behavior rather than hype around any single indicator.
Who It's For
For Self-directed investors and serious retail traders who want academically grounded stock screens without building their own quant pipeline.
Feature List
✓ Prebuilt factor library with evidence ratings ✓ Stock screening by value, cash flow, earnings yield, and quality metrics ✓ Sector-relative factor views and historical robustness dashboards
Where to Validate
Share your landing page in r/r/algotrading — that's exactly where these pain points were discovered.
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