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84puntuación
r/algotrading
SaaS subscription
Build

Order Flow Data Exporter for Retail Quants

Build a SaaS layer on top of professional market data feeds that lets traders fetch futures tick and depth data, then export it into research-ready CSV or Parquet with symbol mapping and presets. The value is not replacing data vendors, but making their data immediately usable for strategy research by independent traders who are stuck on bar-based workflows.

En aumento +121%5 canalesTendencia de menciones de 30 días: latest 5, peak 6, 30-day series
Ver en Reddit
Descubierto 16 jul 2026

Por qué es importante

You already have a working research setup built around minute-bar files, but the moment you want to test order flow ideas, the workflow breaks. The data you need lives in futures markets, arrives in formats designed for engineers, and comes with terminology that is easy to misuse if you trade CFDs or spot instruments. You are not only buying data; you are buying a way to avoid weeks of trial and error. Existing providers can deliver high-quality feeds, but they still leave you to figure out symbol selection, file conversion, and how to get something usable into your notebook or backtester.

  • · Creado para Independent algorithmic traders and small quant teams who trade CFDs, futures, or FX but need exchange-based order flow data in a backtesting-friendly format..
  • · Monetización más probable: SaaS subscription.

El Dolor · Narrativa

You already have a working research setup built around minute-bar files, but the moment you want to test order flow ideas, the workflow breaks. The data you need lives in futures markets, arrives in formats designed for engineers, and comes with terminology that is easy to misuse if you trade CFDs or spot instruments. You are not only buying data; you are buying a way to avoid weeks of trial and error. Existing providers can deliver high-quality feeds, but they still leave you to figure out symbol selection, file conversion, and how to get something usable into your notebook or backtester.

Desglose de puntuación

Intensidad del dolor9/10
Disposición a pagar8/10
Facilidad de construcción6/10
Sostenibilidad7/10

Señal de Mercado

Tendencia de menciones de 30 díasPico: 6
Sparkline: latest 5, peak 6, 30-day series
Canales cubiertos
algotradingfront_pagefintechproductivitysaas

Estrategia de lanzamiento

Usuario objetivo exacto

Solo Python-based traders currently using CSV bar data who want to test order flow strategies on equity index and metal futures within the next month.

Número estimado de usuarios

~20K-50K active globally

Canal de adquisición principal

SEO long-tail

Ancla de precio

$49/month

Primer hito

10 paying users who connect a vendor account and export at least 3 datasets within 30 days

Alcance del MVP · 1-2 semanas

Semana 1
  • Define a normalized schema for trades, quotes, and optional depth snapshots
  • Build a command-line importer for one provider's historical futures dataset
  • Create CSV and Parquet export jobs for ES, NQ, GC, and 6E
  • Set up a basic web dashboard for symbol selection and date-range requests
  • Write Python example notebooks showing immediate use in pandas and backtesting
Semana 2
  • Add user accounts, saved export presets, and download history
  • Implement spot-to-futures proxy guidance in the UI for FX and CFD users
  • Add lightweight validation checks for missing sessions, rollover dates, and time zones
  • Publish a landing page with sample files and a waitlist-to-paid conversion flow
  • Run outreach to early users and measure export completion and repeat usage
Funciones MVP: Connect to external historical futures data APIs · One-click export to normalized CSV and Parquet · Asset presets for indices, metals, commodities, and FX futures proxies · Python-ready dataset schemas and sample loaders · Usage-based download and storage management

Diferenciación

Soluciones existentes
DatabentoInteractive Brokers dataHistDataIQFeedRithmic
Nuestro enfoque
The unmet need is not simply raw market data; it is an easier end-to-end workflow that helps self-directed traders choose the right exchange data, transform it into usable research formats, and adapt existing backtesting systems without deep market microstructure expertise.

Por qué esto podría fallar

Autorrefutación: la señal de confianza más importante

  1. 1Data licensing could prevent a commercially attractive packaging model, forcing the product into a narrower bring-your-own-vendor workflow.
  2. 2The best users may already be comfortable with APIs and see little reason to pay for conversion and packaging.
  3. 3Acquisition may be expensive because the buyer pool is specialized and fragmented across many small communities.

Resumen de evidencia

Cómo la IA sintetizó esta información: sin citas textuales

The discussion consistently centers on the need for genuine exchange-based order flow rather than basic bars. Several participants pointed to a specialized data vendor as the practical choice, while multiple follow-up questions focused on file format, API access, and how to fit the data into an existing CSV workflow. That combination suggests a real opportunity in usability and workflow tooling rather than raw data creation.

1 1 publicación analizada5 5 canalesAI · Sintetizado por IA · sin citas textuales

Plan de Acción

Valida esta oportunidad antes de escribir código

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

Order Flow Data Exporter for Retail Quants

Subtítulo

Build a SaaS layer on top of professional market data feeds that lets traders fetch futures tick and depth data, then export it into research-ready CSV or Parquet with symbol mapping and presets. The value is not replacing data vendors, but making their data immediately usable for strategy research by independent traders who are stuck on bar-based workflows.

Para Quién Es

Para Independent algorithmic traders and small quant teams who trade CFDs, futures, or FX but need exchange-based order flow data in a backtesting-friendly format.

Lista de Funciones

✓ Connect to external historical futures data APIs ✓ One-click export to normalized CSV and Parquet ✓ Asset presets for indices, metals, commodities, and FX futures proxies ✓ Python-ready dataset schemas and sample loaders ✓ Usage-based download and storage management

Dónde Validar

Comparte tu landing page en r/r/algotrading — ahí es exactamente donde se descubrieron estos puntos de dolor.

Regístrate para desbloquear el análisis profundo completo

GTM, alcance del MVP, por qué podría fallar, ActionPlan Copy Kit. El registro gratuito otorga 10 vistas detalladas/mes.

Report & PRDBUSINESS

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Preguntas frecuentes

¿Quién siente este problema?
Independent algorithmic traders and small quant teams who trade CFDs, futures, or FX but need exchange-based order flow data in a backtesting-friendly format.
¿Es esta una oportunidad real?
Esta oportunidad tiene una puntuación de 84/100 en la métrica compuesta de Pain Spotter (intensidad del dolor, disposición a pagar, viabilidad técnica y sostenibilidad). Valídala más a fondo antes de dedicar tiempo de ingeniería.
¿Cómo debería validarla?
Realiza 5 conversaciones de descubrimiento de clientes con el público objetivo, publica una landing page con lista de espera y revisa la publicación de origen enlazada para ver la actividad reciente antes de desarrollar.