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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.
Why this matters
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.
- · Built for Algorithmic traders who want to incorporate tape reading and order flow into their models but lack the infrastructure to process raw Level 2 data..
- · Most likely monetization: Tiered SaaS subscription based on asset coverage and data granularity..
The Pain · Narrative
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.
Score Breakdown
Market Signal
Go-to-Market
Retail algorithmic traders looking to upgrade their technical indicator strategies with institutional-style tape reading metrics.
~50,000 intermediate-to-advanced algorithmic traders.
Hacker News launch focused on the engineering challenge of processing tick data, followed by quantitative finance newsletters.
$99/month for access to pre-calculated metrics on top 100 liquid equities.
Secure 10 beta testers willing to pay a discounted rate to help validate the accuracy of the order flow metrics.
MVP Scope · 1–2 weeks
- 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
- 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
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1The infrastructure costs required to process millions of ticks per second across thousands of assets will destroy profit margins.
- 2Exchange licensing fees for redistributing derived data can be prohibitively expensive and legally complex.
- 3The latency introduced by processing the data and serving it via API makes the signals too slow for effective tape reading.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
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.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Build
Strong demand signals detected. Real pain, real willingness to pay — start building an MVP.
Landing Page Copy Kit
Ready-to-paste copy based on real Reddit community language — no editing required
Headline
Contextual Order Flow Aggregation API
Sub-headline
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.
Who It's For
For Algorithmic traders who want to incorporate tape reading and order flow into their models but lack the infrastructure to process raw Level 2 data.
Feature List
✓ 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
Where to Validate
Share your landing page in r/r/algotrading — that's exactly where these pain points were discovered.
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