How Does an Algorithmic Trading System Process an Order? | Trading Engineering #5
How Does an Algorithmic Trading System Process an Order?
Trading Engineering Series — 06
Algorithmic trading systems are designed to make trading decisions and execute orders automatically based on predefined strategies.
However, an algorithmic trading system is not simply a program that generates buy or sell orders.
From receiving market data to generating a trading signal, performing pre-trade risk checks, submitting the order to an exchange, and processing the execution result, an order passes through multiple components.
In low-latency trading, the efficiency and reliability of this entire process are critical.
1. The Overall Architecture of an Algorithmic Trading System
A typical order flow can be represented as:
Market Data → Market Data Processing → Trading Strategy → Trading Signal → Pre-Trade Risk Check → Order Manager → DMA/FEP → Exchange → Execution Report
The execution result is then fed back into the system:
Execution Report → Order Manager → Position / Strategy
This creates a continuous real-time feedback loop between market data, trading decisions, and execution.
2. Market Data
The first step is receiving and processing market data.
Depending on the strategy, the system may process:
- Order book updates
- Bid and ask prices
- Trade executions
- Market depth
- Instrument status
- Reference data
Algorithmic trading systems often process a large volume of market data in real time.
Therefore, efficient message processing, memory management, and stable data delivery are important.
Unnecessary memory copies, excessive object creation, or inefficient parsing can directly affect strategy response time.
3. Trading Strategy
The trading strategy analyzes market data and determines whether an order should be generated.
For example, a strategy may consider:
- Price movements
- Bid/ask spread
- Market depth
- Trading volume
- Position
- Risk exposure
- Statistical signals
- Predefined trading rules
The strategy itself may be computationally complex, but in latency-sensitive systems, the critical path should remain as simple and predictable as possible.
4. Generating a Trading Signal
When the strategy determines that a trade should be executed, it generates a trading signal.
A signal may contain information such as:
- BUY / SELL
- Instrument
- Price
- Quantity
- Order type
- Strategy ID
Separating the trading decision from the order execution process can make the overall architecture easier to manage and maintain.
It also allows the order-processing layer to focus on execution, risk control, and order state management.
5. Pre-Trade Risk Check
Before an order reaches the market, it must pass the required risk controls.
Typical checks include:
- Price limits
- Order quantity limits
- Order value limits
- Position limits
- Exposure limits
- Strategy-specific limits
For automated trading, pre-trade risk management is particularly important because a single software or strategy error can generate a large number of orders within a very short period of time.
At the same time, risk checks should be designed efficiently because they are located directly on the order execution path.
Fast execution without effective risk control is not a reliable trading system.
6. Order Management
After passing the risk check, the order is handled by the Order Management layer.
The system needs to maintain the state of each order, such as:
New → Submitted → Partially Filled → Filled / Cancelled / Rejected
Order state management is critical for maintaining consistency between the strategy, internal systems, and the exchange.
The Order Manager also needs to handle events such as:
- New order
- Cancel request
- Replace request
- Execution report
- Order rejection
- Connection failure
7. DMA and FEP
The order is then transmitted through the DMA and FEP infrastructure toward the exchange.
A simplified architecture can be represented as:
Order Manager → DMA Gateway → FEP → Exchange
The DMA layer provides an optimized path for transmitting orders, while the FEP handles external connectivity and communication with the exchange or market infrastructure.
This is where many of the low-latency considerations discussed in previous articles become important.
Reducing:
- Memory copies
- Inter-process communication overhead
- Context switches
- Lock contention
- Message parsing overhead
- Unnecessary processing
can help reduce the latency of the critical order path.
8. Exchange Execution and Execution Reports
Once the exchange receives the order, it may be:
- Accepted
- Rejected
- Partially filled
- Fully filled
- Cancelled
The execution result is transmitted back through the communication layer and delivered to the Order Manager.
The system then updates the corresponding order state and position information.
9. Execution Results Feed Back Into the Strategy
The execution result is not necessarily the end of the process.
For many algorithmic strategies, execution information becomes new input for the strategy itself.
For example:
Market Data → Strategy → Order → Execution → Position Update → Strategy
The strategy can then determine its next action based on the updated position and market conditions.
This feedback loop is one of the fundamental characteristics of an automated trading system.
10. Where Does Latency Matter Most?
The most latency-sensitive path can be simplified as:
Market Data → Strategy → Signal → Risk Check → Order → DMA/FEP → Exchange
This is the critical path of the trading system.
Other functions such as:
- Logging
- Monitoring
- Historical storage
- Statistics
- Reporting
do not necessarily need to be processed synchronously on the critical path.
Where appropriate, these functions can be handled asynchronously so that they do not unnecessarily delay order processing.
The key is not simply to make every component faster.
The goal is to identify the End-to-End Critical Path and optimize the components that actually affect trading latency.
11. Performance and Reliability Must Be Balanced
A high-performance trading system cannot be evaluated by latency alone.
A practical trading system must also provide:
Performance + Stability + Risk Control + Reliability + Maintainability
For example, removing every possible synchronization mechanism may reduce latency in certain situations, but it can also make the system more difficult to control and maintain.
Likewise, aggressively simplifying the architecture may improve the latency of the normal path while making failure recovery more difficult.
Therefore, trading system architecture requires a balance between performance and operational reliability.
12. Our Approach at FontesFintech
At FontesFintech, we approach algorithmic trading systems from an end-to-end perspective.
Our focus includes:
- High-performance market data processing
- Low-latency strategy execution
- Efficient pre-trade risk management
- High-performance order processing
- DMA and FEP connectivity
- Efficient memory management
- Lock and synchronization optimization
- Fault detection and recovery
- End-to-end latency measurement
Rather than optimizing a single component in isolation, we focus on the complete path from market data to exchange execution.
The architecture is also designed according to the customer's trading strategy, order volume, market environment, and risk requirements.
Conclusion
An algorithmic trading system is much more than an automated order generator.
It is an integrated real-time system connecting:
Market Data → Strategy → Risk Management → Order Management → DMA/FEP → Exchange → Execution
Every component can affect the final trading performance.
For this reason, building a high-performance algorithmic trading system requires an understanding of the entire execution path—not just the trading strategy itself.
Fast. Controlled. Reliable.
These are the principles we pursue when designing high-performance trading infrastructure at FontesFintech.