From Market Data to Execution: Understanding the Trading Technology Stack

These four layers are in series — the output of one is the input of the next. A failure anywhere propagates downstream.

Layer One: Market Data

This is where the chain begins. Exchanges continuously push out price, depth, and trade data. The market data layer receives that raw stream and converts it into a format the rest of the system can consume.

The problems this layer has to solve are more complex than they appear.
Data quality requires active maintenance. Raw exchange feeds aren’t always clean — messages can arrive out of order, duplicated, or missing entirely. The market data layer needs to handle deduplication, reordering, and gap detection, ensuring the signal layer receives a coherent, trustworthy snapshot of the market.

The time reference needs to be explicitly defined. Every data point carries a timestamp, but what that timestamp actually represents needs to be decided at design time — is it the exchange-side event time, or your local receive time? The gap between those two clocks directly affects the timing correctness of your signal layer, and it’s one of the most commonly overlooked details at this layer.

This is where latency enters the chain. The time from an exchange pushing data to your system receiving and parsing it is your market data latency. It’s shaped by network infrastructure, the architectural design of the data source, and the processing capacity of the receiving end.

This is also why the choice of market data layer has the broadest downstream impact of any decision in the stack — its quality and latency propagate into every layer that follows.

Layer Two: Signal Processing

Data is in. Now the system has to answer one question: should we trade right now?The signal processing layer converts raw market data into executable trading decisions. How it’s designed depends almost entirely on your strategy type.

High-frequency and market-making strategies typically build signal logic directly on order book microstructure — bid-ask spread, depth imbalance, short-term price momentum. The logic is relatively straightforward, but the speed requirements are extreme. A signal’s useful window may be only a few milliseconds.

Mid-to-low frequency strategies may incorporate statistical models, machine learning features, or cross-market relationships. Speed requirements are more relaxed, but demands on data completeness and historical consistency are higher.

The most common failure mode at this layer is the gap between model assumptions and data reality. In backtesting, data is perfect, timely, and ordered. In live trading, those assumptions start cracking — jitter, occasional message delays, brief data gaps — and the model performs in ways that systematically diverge from what backtesting suggested.

Layer Three: Risk Management

The signal layer says trade. But the risk layer is the last gate. Its job is to confirm, before execution, that this trade won’t put the account in danger.

Typical risk checks cover position limits, cumulative loss thresholds, exchange rate limits, and anomaly detection on incoming signals.

One architectural detail that often costs teams later: is the risk check synchronous or asynchronous? In some systems, risk runs asynchronously — which creates a dangerous window where an order has already been handed to the execution layer before the risk check completes. Under normal conditions, this is rarely a problem. Under peak message load, that window can allow orders that should have been blocked to go out early.

Ultimately, the effectiveness of any risk layer depends on how current its data is. A risk system making decisions on data that’s 2 seconds stale is effectively no risk system at all during extreme moves — not a logic design failure, but a data pipeline failure, with consequences that show up directly in the account.