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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.
Por qué es importante
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.
- · Creado para Independent futures and options traders, quant hobbyists, and small research teams who run backtests in Python and currently stitch together multiple data sources..
- · Monetización más probable: SaaS subscription.
El Dolor · Narrativa
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.
Desglose de puntuación
Señal de Mercado
Estrategia de lanzamiento
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
Alcance del MVP · 1-2 semanas
- 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
Diferenciación
Por qué esto podría fallar
Autorrefutación: la señal de confianza más importante
- 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.
Resumen de evidencia
Cómo la IA sintetizó esta información: sin citas textuales
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 de Acción
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Próximo Paso Recomendado
Construir
Señales de demanda fuertes. Hay dolor real y disposición a pagar — empieza a construir un MVP.
Kit de Textos para Landing Page
Textos listos para pegar, basados en el lenguaje real de la comunidad de Reddit
Titular
Backtest-Ready Data Pipeline SaaS
Subtítulo
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.
Para Quién Es
Para Independent futures and options traders, quant hobbyists, and small research teams who run backtests in Python and currently stitch together multiple data sources.
Lista de Funciones
✓ 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
Dónde Validar
Comparte tu landing page en r/r/algotrading — ahí es exactamente donde se descubrieron estos puntos de dolor.
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