One component of an agentic trading system, focused on the problem of perceiving market microstructure in live conditions
In a multi-component agentic trading architecture, perception is the foundational layer. Before any reasoning or action can happen, the system needs to maintain an accurate, high-resolution model of what the market is currently doing. EPIC IDENT is the component of the architecture responsible for this — specifically, for processing order flow and microstructure data into a representation that downstream agents can reason about.
This article describes the design of IDENT as a research component. It is written for engineers working on related problems and for readers interested in how this layer of an agentic trading architecture is structured.
IDENT is an internal research component. It is not licensed, sold, or made available to outside parties, and this article does not constitute an offer to do so.
The problem IDENT is designed to solve
Order books are not just static lists of resting orders. They are continuously changing surfaces shaped by the behavior of every participant in the market — humans, slow algorithms, fast algorithms, market makers, directional traders, and the various species of automated systems that have come to dominate modern markets. The information that matters is rarely in any single snapshot of the book; it’s in the patterns of how the book changes over time.
A system trying to perceive what is happening in a market has to extract several distinct kinds of information from this stream: where genuine liquidity is concentrated (as opposed to liquidity that will be pulled the moment it’s pressured), whether aggressive flow is being absorbed or is moving the market, what regime the participants are operating in, and whether the pattern of activity matches any of the recognizable behavioral signatures that recur in microstructure data.
These are different problems with different time horizons and different signal characteristics. IDENT is designed as a perception layer that addresses them in parallel and presents the results in a form that reasoning agents can use.
Architectural overview
IDENT operates as a context provider for the rest of the system. Reasoning agents query IDENT for the current state of the market; IDENT does not itself make trading decisions. This separation is important. Mixing perception and reasoning creates systems that are difficult to debug because incorrect actions can stem from either layer, and the diagnostic process becomes ambiguous. Keeping the layers separate means perception failures and reasoning failures can be diagnosed independently.
The component handles several distinct analytical functions:
Liquidity absorption analysis. When aggressive flow encounters resting liquidity, the behavior of that liquidity reveals information. Does it absorb the flow and replenish, suggesting genuine size? Does it pull immediately, suggesting iceberg detection or stop-running setups? Does the replenishment rate decay over the course of an absorption sequence, suggesting exhaustion? These patterns are recognizable but require continuous tracking across the order book to identify reliably.
Microstructure pattern recognition. Certain patterns recur across markets and time periods — not in their specific numerical form, but in their structural shape. The shape of a spoofing pattern, the shape of liquidity walk-up, the shape of a coordinated cancellation sequence. A pattern recognition layer that maintains a library of these structural patterns and matches against them in real time provides downstream agents with categorized context rather than raw data.
These are patterns the system is designed to recognize in order to inform downstream decisions about resting and aggressive participation in the market. The system does not generate or replicate these patterns.
Participant behavior fingerprinting. Different automated participants in a market have characteristic behavioral signatures — recognizable patterns of order placement, cancellation timing, response to specific market conditions. These signatures evolve as the participants update their systems, but the rate of evolution is slow enough that maintaining a dynamic library of recognized behavioral patterns is tractable. When the pattern of activity matches a known signature, that context is meaningful for downstream decisions.
Regime classification. Markets move through different regimes — trending, range-bound, volatile, compressed, liquid, illiquid — and the appropriate response to any given pattern depends on which regime the market is currently in. A perception layer that classifies the current regime and detects transitions between regimes provides essential context for reasoning agents.
Integration with the broader architecture
IDENT outputs probabilistic assessments rather than binary signals. The output is not “buy” or “sell” or even “liquidity is being absorbed”; it’s something closer to “the pattern of activity in the last N seconds matches the absorption signature with confidence X, conditional on the current regime being Y.” This format is more useful for downstream reasoning because it preserves uncertainty and allows the reasoning layer to weight perception against other inputs rather than treating perception as ground truth.
The integration discipline is that reasoning agents are required to query perception fresh rather than caching previous outputs. This is more expensive computationally but it prevents a class of bugs where the reasoning layer acts on stale perception. In a system where market state can change meaningfully in milliseconds, stale perception is worse than no perception.
What this is not
A few clarifications about what IDENT is and is not, because the order flow analysis space includes both genuine technical work and a substantial amount of vendor marketing; this article is the former and should not be read as the latter.
IDENT is not a signal generator that predicts price movement. It is a perception layer that describes market state. What downstream agents do with that description is a separate problem solved by separate components.
IDENT does not depend on any particular data feed or vendor relationship. The architecture is designed around processing order flow data; the specific source of that data is an implementation detail that can vary across deployments.
IDENT is not a finished product. It is a component of a research architecture that has been developed and refined over nine years. The interesting questions about it are about its failure modes and limits — when does the pattern library miss patterns that turn out to matter, when do behavioral signatures shift faster than the system can adapt, when does regime classification lag behind actual regime change. These are the topics future writing will address.
Why perception matters more than people think
A common pattern in systematic trading is to focus enormous engineering effort on the reasoning and execution layers while treating perception as a solved problem — assuming that good data plus standard processing equals adequate market state representation. In practice, this is wrong. The hardest problems in this space are perception problems: figuring out what is actually happening in a market is much harder than figuring out what to do once you know.
The argument for investing in perception architecture is that good perception makes mediocre reasoning effective, while bad perception makes excellent reasoning useless. A reasoning layer acting on misperceived market state is going to make decisions that are wrong in ways that are difficult to diagnose, because the diagnostic process will keep coming back to “the reasoning seems sound, the action was correct given the perception, so why did the outcome fail?” The answer is often that perception was the failure point all along.
This is the conviction behind the IDENT design and the reason it gets disproportionate engineering attention within the broader research architecture this work is part of.