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Bias & Tradability Checker for Backtests
Build a web app that audits strategy tests for expectancy, transaction-cost realism, leakage, persistence effects, and invalid target construction. The core value is preventing traders from putting capital behind patterns that are descriptive but not tradable.
Why this matters
You test a market idea, see an attractive success rate, and feel tempted to treat it as an edge. Then a more experienced reviewer points out that the result may collapse once you include costs, compare against drift, or measure what happens after the decision point instead of before it. If you trade your own capital or a funded account, this gap is expensive. Existing notebooks let you compute metrics, but they do not reliably warn you when your target definition, shared reference point, or path persistence is making a weak idea look stronger than it is. You need software that challenges your result before the market does.
- · Built for Independent traders, aspiring quants, and funded retail traders who test ideas in Python or spreadsheets but lack institutional-grade validation discipline..
- · Most likely monetization: SaaS subscription.
The Pain · Narrative
You test a market idea, see an attractive success rate, and feel tempted to treat it as an edge. Then a more experienced reviewer points out that the result may collapse once you include costs, compare against drift, or measure what happens after the decision point instead of before it. If you trade your own capital or a funded account, this gap is expensive. Existing notebooks let you compute metrics, but they do not reliably warn you when your target definition, shared reference point, or path persistence is making a weak idea look stronger than it is. You need software that challenges your result before the market does.
Score Breakdown
Market Signal
Go-to-Market
Retail traders and solo quants who already run backtests in Python or export OHLC data into CSV files and want a second layer of validation before trading live.
~50K active globally in the initial reachable niche
SEO long-tail
$49/month
20 paying users who upload at least 3 strategy files each within 30 days
MVP Scope · 1–2 weeks
- Define 10 core validation checks including expectancy, cost drag, benchmark drift, and forward-return windows
- Build CSV schema ingestion for OHLC plus signal columns
- Create a simple Python engine to compute hit rate, average win, average loss, and expectancy
- Design plain-English report templates for common failure modes
- Launch a landing page with sample report screenshots and waitlist form
- Add automated bias heuristics for shared-anchor effects and suspicious target overlap
- Implement baseline comparisons against passive long and randomized anchors
- Build a minimal web UI for file upload and downloadable report output
- Add cost and slippage assumptions with editable presets
- Run pilot analyses for 10 early users and collect retention and correction-rate feedback
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1Users with enough sophistication to value this may prefer their own research stack and avoid a hosted tool.
- 2Automated bias detection may produce false alarms or miss nuanced issues, damaging trust.
- 3Many traders enjoy idea generation more than disciplined invalidation, limiting conversion from curiosity to paid usage.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
The strongest recurring theme was that raw accuracy is not enough. Roughly a dozen comments pushed for expectancy, forward-return, cost-aware testing, and careful handling of leakage or persistence. Several participants also debated whether the result was truly predictive or just a byproduct of target construction. This indicates a sharp need for software that audits strategy claims before money is put behind them.
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
Bias & Tradability Checker for Backtests
Sub-headline
Build a web app that audits strategy tests for expectancy, transaction-cost realism, leakage, persistence effects, and invalid target construction. The core value is preventing traders from putting capital behind patterns that are descriptive but not tradable.
Who It's For
For Independent traders, aspiring quants, and funded retail traders who test ideas in Python or spreadsheets but lack institutional-grade validation discipline.
Feature List
✓ CSV or notebook-result upload with automatic expectancy and cost analysis ✓ Bias scanner for lookahead, target leakage, shared-anchor inflation, and selection bias ✓ Forward-return and benchmark report with passive, random-anchor, and regime-split comparisons
Where to Validate
Share your landing page in r/r/algotrading — that's exactly where these pain points were discovered.
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