Retail Access to Unconflated US Equity Market Depth Data
Summary
The document outlines data requirements for a retail algorithmic engine studying short-horizon order flow imbalance and depth changes in US equities. It seeks streaming market-by-price depth across ten levels, delivered through a direct interface without a desktop application. The author says that some retail broker feeds provide depth as conflated snapshots at intervals of roughly 100 to 250 milliseconds, which may omit order additions, cancellations, and trades occurring between updates.
It raises questions about whether unconflated depth data is available to nonprofessional users at a modest monthly cost, how researchers should account for snapshot lag when modeling order book imbalance, and whether exchange licensing makes direct feeds inaccessible to retail users. The document offers no answers, vendor comparisons, licensing analysis, or tested filtering method. Its value is in identifying the interaction between data resolution, microstructure research, system design, and market data fees; its claims and proposed price ranges are not substantiated within the text.
Key ideas
- Short-horizon order flow imbalance research may require frequent updates to order book depth.
- Conflated snapshots can omit order book events that occur between feed updates.
- The author questions whether nonprofessional users can access unconflated US equity depth at lower cost.
- The document does not answer its data access or modeling questions, or validate the stated feed limitations.
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Full text
# Sourcing un-conflated US Equities MBP data for retail algorithmic engines: Architectural and licensing constraints # Sourcing un-conflated US Equities MBP data for retail algorithmic engines: Architectural and licensing constraints I am building a headless, Java-based execution engine designed to calculate short-horizon alpha (specifically Order Flow Imbalance (OBI) and order book depth dynamics across 10 depth levels) for liquid US Equities (NYSE / NASDAQ). To accurately compute sub-second OBI and track liquidity sweeps, the engine requires a streaming, low-latency market depth feed delivered via direct sockets/APIs (e.g., WebSockets, raw TCP, or C++/Java SDKs) without desktop GUI dependencies. The Technical & Financial Bottleneck: Conflation in Retail Feeds: Standard retail broker APIs (such as IBKR TWS API) buffer and conflate Level 2 / MBP depth into 100ms–250ms snapshots. This periodic sampling drops intra-interval order additions, cancels, and executions, distorting micro-second volume delta and queue tracking. Institutional Non-Display Licensing: Direct, raw streaming TCP feeds (e.g., Databento, MayStreet) provide un-conflated L2/L3 data, but trigger exchange non-display fees and vendor platform base fees starting around $1,500–$3,000+/month. This creates a steep barrier for individual capital validation. Questions: Are there any market data vendors or clearing APIs offering raw, un-conflated MBP-10 depth for NYSE/NASDAQ equities under a Non-Professional schedule (targeting under $300/month)? From a quantitative research perspective, how do retail strategies typically account for or filter out the noise/lag introduced by 100ms conflated broker snapshots when modeling short-horizon order book imbalance? Is direct, un-conflated equity depth structurally locked behind institutional non-display licensing rules, making conflated snapshots an unavoidable architectural constraint for retail-tier US equity algorithms?
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