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Backtest-to-Live Data Reconciliation SaaS
Build a debugging platform that compares historical training data against live or broker feeds bar by bar and pinpoints why a trading model fails outside backtests. The product would surface mismatches in volume, session boundaries, roll dates, and adjustments before users blame the model or spend on unnecessary vendor changes.
Pourquoi c'est important
You spend months building a strategy that looks promising on historical futures data, then it falls apart the moment you test it in a paper or live environment. The issue is not obvious because price may look roughly similar while volume, session cutoffs, or rollover handling quietly drift enough to break your features. Existing broker dashboards and raw CSV checks make this painfully manual, and premium data vendors do not necessarily explain where the mismatch lives. What you need is a tool that shows exactly which bars differ, how the differences propagate into indicators, and whether your edge was real or came from a dataset artifact.
- · Conçu pour Independent systematic traders, small quant teams, and ML-based futures traders who research with one dataset and execute through a broker or separate live feed..
- · Monétisation la plus probable : SaaS subscription.
La douleur · Récit
You spend months building a strategy that looks promising on historical futures data, then it falls apart the moment you test it in a paper or live environment. The issue is not obvious because price may look roughly similar while volume, session cutoffs, or rollover handling quietly drift enough to break your features. Existing broker dashboards and raw CSV checks make this painfully manual, and premium data vendors do not necessarily explain where the mismatch lives. What you need is a tool that shows exactly which bars differ, how the differences propagate into indicators, and whether your edge was real or came from a dataset artifact.
Détail du score
Signal du marché
Mise sur le marché
Solo and two-to-five person quant trading teams running futures or intraday strategies with separate research and execution data sources.
~20K-50K active globally
SEO long-tail
$79/month
10 paying users who upload two feeds and run at least three reconciliation jobs each within 30 days
Périmètre MVP · 1–2 semaines
- Build CSV upload and schema mapping for OHLCV bars from two sources
- Implement timestamp alignment and diff logic for price and volume fields
- Create a basic web UI showing mismatched bars in a sortable table
- Add summary diagnostics for session boundary and missing-bar anomalies
- Prepare sample futures datasets and three reproducible mismatch test cases
- Add feature-level comparison for common indicators and model inputs
- Implement continuous contract roll-date comparison and alerts
- Ship a report export that summarizes likely root causes
- Integrate one broker API and one external data API for direct ingestion
- Launch a landing page with a self-serve trial and feedback capture
Différenciation
Pourquoi cela pourrait échouer
Auto-contre-argument — le signal de confiance le plus important
- 1The market may be too narrow because many users debug feed mismatches only once, reducing long-term retention.
- 2Serious quants may distrust a third-party diagnostics tool and prefer internal scripts they can inspect fully.
- 3Data licensing or broker API inconsistencies may prevent reliable automated ingestion across the providers users care about most.
Résumé des preuves
Comment l'IA a synthétisé cet aperçu — pas de citations textuelles
The discussion strongly centered on discrepancies between backtest data and broker or live bars. Roughly half the comments pointed to aggregation, volume, roll dates, and session boundaries as likely causes of model failure. Multiple participants described manual reconciliation workflows and warned that apparent alpha often disappears once feeds are matched properly. That combination indicates a sharp, expensive debugging problem with immediate value.
Plan d'Action
Validez cette opportunité avant d'écrire du code
Prochaine Étape Recommandée
Construire
Signaux de demande forts. Vraie douleur et volonté de payer détectées — commencez à construire un MVP.
Kit de Textes pour Landing Page
Textes prêts à coller, basés sur le langage réel de la communauté Reddit
Titre Principal
Backtest-to-Live Data Reconciliation SaaS
Sous-titre
Build a debugging platform that compares historical training data against live or broker feeds bar by bar and pinpoints why a trading model fails outside backtests. The product would surface mismatches in volume, session boundaries, roll dates, and adjustments before users blame the model or spend on unnecessary vendor changes.
Pour Qui
Pour Independent systematic traders, small quant teams, and ML-based futures traders who research with one dataset and execute through a broker or separate live feed.
Liste des Fonctionnalités
✓ Bar-by-bar historical versus live feed diff engine ✓ Automated detection of volume, timestamp, roll, and adjustment mismatches ✓ Feature parity checks that show downstream signal impact
Où Valider
Partagez votre landing page sur r/r/algotrading — c'est exactement là que ces points de douleur ont été découverts.
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