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Backtest-Ready Data Pipeline SaaS
Build a SaaS that connects to market data vendors and turns raw historical files into standardized, backtest-ready datasets with continuous contract logic, daily refreshes, and export to common research formats. The value is not selling raw data itself, but saving advanced retail traders and small funds hours of engineering and reducing vendor lock-in.
Pourquoi c'est important
You are excited when historical data becomes cheap enough to justify testing more ideas, but the real bottleneck starts right after purchase. You still need to fetch, normalize, roll contracts, store, refresh, and export everything in a format your backtest can trust. If you trade futures or options, you often mix several vendors because no single source covers every instrument affordably. That means your research stack becomes a fragile set of scripts, chart exports, and manual checks. What you want is a reliable software layer that turns vendor data into analysis-ready files and keeps them current without forcing you to become a data engineer.
- · Conçu pour Independent futures and options traders, quant hobbyists, and small research teams who run backtests in Python and currently stitch together multiple data sources..
- · Monétisation la plus probable : SaaS subscription.
La douleur · Récit
You are excited when historical data becomes cheap enough to justify testing more ideas, but the real bottleneck starts right after purchase. You still need to fetch, normalize, roll contracts, store, refresh, and export everything in a format your backtest can trust. If you trade futures or options, you often mix several vendors because no single source covers every instrument affordably. That means your research stack becomes a fragile set of scripts, chart exports, and manual checks. What you want is a reliable software layer that turns vendor data into analysis-ready files and keeps them current without forcing you to become a data engineer.
Détail du score
Signal du marché
Mise sur le marché
Solo or two-person systematic traders already paying for at least one market data subscription and coding their strategies in Python.
~25K-75K globally
SEO long-tail
$49/month
15 paying users who connect at least one vendor account and schedule weekly refresh jobs within 30 days
Périmètre MVP · 1–2 semaines
- Build a landing page focused on futures backtest data automation and capture email interest
- Implement one vendor connector that downloads minute futures data into Parquet
- Create a simple continuous contract builder with two roll methods and one adjustment option
- Add a local CLI command to export a research-ready dataset for one symbol family
- Interview 10 active backtest users about their current data workflow and failure points
- Wrap the pipeline in a minimal web dashboard with job history and download links
- Add scheduled refresh jobs for daily updates and basic retry handling
- Implement dataset validation checks for gaps, duplicates, and rollover boundaries
- Integrate Stripe and launch a paid beta with a small monthly file retention cap
- Publish two tutorial pages targeting search terms around continuous futures backtesting
Différenciation
Pourquoi cela pourrait échouer
Auto-contre-argument — le signal de confiance le plus important
- 1Exchange and vendor licensing may block the easiest version of the product, forcing a connector-only model that feels less differentiated.
- 2Advanced traders may not trust automated roll logic or normalized outputs unless the software proves accuracy over time.
- 3Cheap alternatives from brokers and charting tools may be good enough for users with lower frequency research needs.
Résumé des preuves
Comment l'IA a synthétisé cet aperçu — pas de citations textuelles
Several participants highlighted that raw historical access is becoming more affordable for some futures datasets, but they also described maintaining recurring subscriptions, running scheduled updates, and combining multiple providers to cover futures and options properly. The recurring theme was that cheap data alone does not remove the engineering burden. Users still spend time exporting, refreshing, reconciling, and preparing datasets before they can backtest effectively.
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-Ready Data Pipeline SaaS
Sous-titre
Build a SaaS that connects to market data vendors and turns raw historical files into standardized, backtest-ready datasets with continuous contract logic, daily refreshes, and export to common research formats. The value is not selling raw data itself, but saving advanced retail traders and small funds hours of engineering and reducing vendor lock-in.
Pour Qui
Pour Independent futures and options traders, quant hobbyists, and small research teams who run backtests in Python and currently stitch together multiple data sources.
Liste des Fonctionnalités
✓ Vendor connectors for historical and scheduled refresh pulls ✓ Continuous futures construction with configurable roll and adjustment rules ✓ Standardized export to Parquet, CSV, and Python-ready datasets ✓ Dataset cost preview and usage tracking dashboard ✓ Automated daily sync jobs with data integrity checks
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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