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r/algotrading
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Contextual Order Flow Aggregation API

An API that ingests raw Level 2 market data and outputs pre-calculated, contextual order flow metrics (e.g., cumulative delta, aggression ratios, volume absorption). It allows traders to confirm technical signals without building massive tick-data infrastructure.

1 canalTendance des mentions sur 30 jours: latest 1, peak 1, 30-day series
Voir sur Reddit
Découvert 12 mai 2026

Pourquoi c'est important

You want to incorporate order flow into your trading algorithms, but raw Level 2 data is a firehose of noise that crashes standard retail platforms. You need to know if buyers are actually supporting a move or just getting trapped, but calculating metrics like cumulative delta or volume absorption in real-time requires massive infrastructure. Existing broker feeds are too messy, forcing you to spend months building data pipelines instead of trading strategies.

  • · Conçu pour Algorithmic traders who want to incorporate tape reading and order flow into their models but lack the infrastructure to process raw Level 2 data..
  • · Monétisation la plus probable : Tiered SaaS subscription based on asset coverage and data granularity..

La douleur · Récit

You want to incorporate order flow into your trading algorithms, but raw Level 2 data is a firehose of noise that crashes standard retail platforms. You need to know if buyers are actually supporting a move or just getting trapped, but calculating metrics like cumulative delta or volume absorption in real-time requires massive infrastructure. Existing broker feeds are too messy, forcing you to spend months building data pipelines instead of trading strategies.

Détail du score

Intensité du problème8/10
Volonté de payer8/10
Facilité de réalisation3/10
Durabilité6/10

Signal du marché

Tendance des mentions sur 30 joursPic : 1
Sparkline: latest 1, peak 1, 30-day series
Canaux couverts
algotrading

Mise sur le marché

Utilisateur cible exact

Retail algorithmic traders looking to upgrade their technical indicator strategies with institutional-style tape reading metrics.

Nombre d'utilisateurs estimé

~50,000 intermediate-to-advanced algorithmic traders.

Canal d'acquisition principal

Hacker News launch focused on the engineering challenge of processing tick data, followed by quantitative finance newsletters.

Ancre de prix

$99/month for access to pre-calculated metrics on top 100 liquid equities.

Premier jalon

Secure 10 beta testers willing to pay a discounted rate to help validate the accuracy of the order flow metrics.

Périmètre MVP · 1–2 semaines

Semaine 1
  • Secure a developer license from a reliable tick data provider like Databento
  • Build a high-performance parser in Rust or C++ to ingest raw Level 2 data for a single highly liquid asset (e.g., SPY)
  • Implement the Lee-Ready algorithm to classify trades as buyer-initiated or seller-initiated
  • Calculate basic cumulative delta on a 1-minute timeframe
  • Store the aggregated metrics in a time-series database
Semaine 2
  • Develop a REST API to query the aggregated cumulative delta data
  • Add a secondary metric calculation, such as an aggression ratio or basic volume profile
  • Create a Python wrapper/SDK to make querying the API seamless for data scientists
  • Write a comprehensive tutorial showing how to use the API to filter out false breakout signals
  • Launch a closed beta offering free access to the single-asset data in exchange for feedback
Fonctions MVP: Pre-calculated cumulative delta and aggression ratio endpoints · Volume-at-price node identification · Point-in-time historical order flow data (no survivorship bias) · WebSocket feed for live tape confirmation signals · Python SDK for easy integration with pandas/numpy

Différenciation

Solutions existantes
AlphaSignalCuteMarkets API
Notre angle
There is a lack of plug-and-play 'kill switch' APIs that monitor macroeconomic regimes and order flow context to automatically pause retail trading algorithms during high-risk periods.

Pourquoi cela pourrait échouer

Auto-contre-argument — le signal de confiance le plus important

  1. 1The infrastructure costs required to process millions of ticks per second across thousands of assets will destroy profit margins.
  2. 2Exchange licensing fees for redistributing derived data can be prohibitively expensive and legally complex.
  3. 3The latency introduced by processing the data and serving it via API makes the signals too slow for effective tape reading.

Résumé des preuves

Comment l'IA a synthétisé cet aperçu — pas de citations textuelles

Traders express deep frustration with the quality of retail data feeds, noting that raw Level 2 data is noisy and difficult to process. Several users highlighted that the true edge lies in combining standard signals with order flow confirmation, specifically mentioning the need for clean, point-in-time data and metrics like volume absorption to avoid market traps.

1 1 publication analysée1 1 canalAI · Synthétisé par IA · pas de citations

Plan d'Action

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Kit de Textes pour Landing Page

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Titre Principal

Contextual Order Flow Aggregation API

Sous-titre

An API that ingests raw Level 2 market data and outputs pre-calculated, contextual order flow metrics (e.g., cumulative delta, aggression ratios, volume absorption). It allows traders to confirm technical signals without building massive tick-data infrastructure.

Pour Qui

Pour Algorithmic traders who want to incorporate tape reading and order flow into their models but lack the infrastructure to process raw Level 2 data.

Liste des Fonctionnalités

✓ Pre-calculated cumulative delta and aggression ratio endpoints ✓ Volume-at-price node identification ✓ Point-in-time historical order flow data (no survivorship bias) ✓ WebSocket feed for live tape confirmation signals ✓ Python SDK for easy integration with pandas/numpy

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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Questions fréquentes

Qui rencontre ce problème ?
Algorithmic traders who want to incorporate tape reading and order flow into their models but lack the infrastructure to process raw Level 2 data.
Est-ce une réelle opportunité ?
Cette opportunité obtient un score de 78/100 selon la métrique composite de Pain Spotter (intensité du problème, propension à payer, faisabilité technique et viabilité). Validez-la davantage avant d'y consacrer du temps de développement.
Comment dois-je la valider ?
Menez 5 entretiens de découverte client avec le public cible, publiez une landing page avec une liste d'attente, et vérifiez l'activité récente sur le post source lié avant de commencer le développement.