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Backtest Leak & Bias Auditor
Build a SaaS tool that audits backtests for look-ahead leakage, timestamp misuse, portfolio-state errors, and unrealistic execution assumptions. Instead of replacing every engine, it can ingest strategy outputs and data snapshots, then run invariant checks that flag suspicious equity curves before users deploy capital.
Por que isso importa
You have a backtest that looks strong, every trade seems individually reasonable, and yet something still feels off. The real problem is that subtle timing or accounting mistakes often do not break the code; they only make the results look better than reality. You may not discover the issue until paper trading or months of manual review. Existing tools tend to show performance, not prove historical integrity. What you want is a safety layer that checks whether your engine used information too early, changed the past when future data was added, or assumed fills that could not have happened.
- · Feito para Independent quants, serious retail traders, and small research teams building custom backtesting pipelines who need confidence that their historical results are not contaminated by hidden bugs..
- · Monetização mais provável: SaaS subscription.
A Dor · Narrativa
You have a backtest that looks strong, every trade seems individually reasonable, and yet something still feels off. The real problem is that subtle timing or accounting mistakes often do not break the code; they only make the results look better than reality. You may not discover the issue until paper trading or months of manual review. Existing tools tend to show performance, not prove historical integrity. What you want is a safety layer that checks whether your engine used information too early, changed the past when future data was added, or assumed fills that could not have happened.
Detalhe da pontuação
Sinal de Mercado
Go-to-Market
Individual and two-to-five-person quant teams already running custom Python backtests and worried their research is too good to be true.
~10K highly relevant early adopters globally
SEO long-tail
$99/month
10 paying users who upload at least 3 backtests each within 30 days
Escopo do MVP · 1–2 semanas
- Define 10 deterministic validation rules for leakage, timestamp order, and fill plausibility
- Build CSV upload and schema-mapping flow for trades, bars, and equity curves
- Implement frozen-date rerun check using uploaded snapshots or partitioned files
- Create a simple report page listing failed checks with severity labels
- Recruit 5 beta users from quant communities and collect sample datasets
- Add point-in-time availability validator for fundamentals and event data timestamps
- Implement fill-timing rules comparing signal timestamps to execution assumptions
- Add anomaly detection for suspicious equity jumps and perfect trade statistics
- Ship Python SDK to export backtest artifacts directly from notebooks
- Launch waitlist page with sample reports and early pricing test
Diferenciação
Por que isso pode falhar
Auto-refutação — o sinal de confiança mais importante
- 1The product may be seen as a nice-to-have script category rather than a recurring SaaS if users only run audits occasionally.
- 2Without trusted benchmark datasets showing real bug catches, sophisticated quants may not believe the tool adds value.
- 3Integrating with many custom backtest formats could create onboarding friction that blocks activation.
Resumo das evidências
Como a IA sintetizou este insight — sem citações literais
The strongest theme in the discussion was not fees but hidden forward leakage and time-order errors. Around half the commenters described bugs involving future data, timestamp semantics, state drift, or incorrect portfolio valuation. Several also emphasized that these issues can survive long code reviews because trade-level outputs look correct. That pattern supports a focused validation product rather than another generic backtester.
Plano de Ação
Valide esta oportunidade antes de escrever código
Próximo Passo Recomendado
Construir
Sinais de demanda fortes. Há dor real e disposição a pagar — comece a construir um MVP.
Kit de Textos para Landing Page
Textos prontos para colar, baseados na linguagem real da comunidade Reddit
Título Principal
Backtest Leak & Bias Auditor
Subtítulo
Build a SaaS tool that audits backtests for look-ahead leakage, timestamp misuse, portfolio-state errors, and unrealistic execution assumptions. Instead of replacing every engine, it can ingest strategy outputs and data snapshots, then run invariant checks that flag suspicious equity curves before users deploy capital.
Para Quem É
Para Independent quants, serious retail traders, and small research teams building custom backtesting pipelines who need confidence that their historical results are not contaminated by hidden bugs.
Lista de Funcionalidades
✓ Backtest ingestion from CSV, Python, and common portfolio logs ✓ Automated leak tests such as frozen-date replay and point-in-time consistency checks ✓ Timestamp audit for signal time, data availability time, and fill time assumptions ✓ Anomaly reports for equity-curve discontinuities, suspicious perfect fills, and changing past states
Onde Validar
Compartilhe sua landing page no r/r/algotrading — é exatamente lá que esses pontos de dor foram descobertos.
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