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We are designing the actual stock exchange, not the broker.
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Market Data Volume
Scalability and Redundancy
Modern stock exchanges rely on RESTful APIs and the FIX protocol to enable secure, efficient, and high-performance interaction between retail traders, brokers, and the exchange infrastructure. These systems facilitate seamless trading operations, real-time data dissemination, and low-latency execution.
RESTful APIs offer a versatile interface for both brokers and retail traders to perform essential trading operations like placing orders, querying market data, and fetching execution details. Brokers often implement these APIs to connect retail traders with the exchange.
POST /v1/ordersGET /v1/executionsGET /v1/marketData): Supplies real-time updates for L1 data (best bid/ask prices and volumes).GET /v1/orderBook): Provides L2 market data, including price levels and aggregated quantities.Optimization Techniques:
The Financial Information eXchange protocol is the backbone of institutional trading, offering a standardized, extensible, and precise method of communication between brokers and exchanges.
8=FIX.4.2|9=176|35=D|49=Broker123|56=ExchangeABC|11=OrderID123|55=GOOG|54=1|44=100.50|38=10|40=2|10=128
35=D: New order.
55=GOOG: Stock symbol.
54=1: Buy.
44=100.50: Price.
To optimize performance for high-frequency trading, FIX messages are often converted into Simple Binary Encoding:
For colocated brokers—those physically proximate to the exchange—SBE dramatically reduces latency, ensuring microsecond-level execution speeds and providing a competitive advantage.
Modern stock exchanges depend on a dual-layered integration approach:
By incorporating Simple Binary Encoding for high-frequency trading, exchanges achieve the ultra-low latency required by competitive markets. Together, these technologies ensure the speed, reliability, and security critical to modern financial ecosystems.
Product Model:
Represents financial instruments like stocks or options.
Order Model:
Tracks buy or sell instructions from clients.
Execution Model:
Captures trade results.
Relationships:
In-Memory vs. Database Storage:
Candlestick Chart Data Models
Candlestick charts visualize price movements over intervals (e.g., 1-minute, 1-hour) using pre-allocated ring buffers for efficient, low-latency time-series data storage. Attributes include open, close, high, low prices, volume, and timestamp. Ring buffers offer constant-time operations and memory efficiency by overwriting old data.
Memory and Persistence Optimization:
Cache-friendly designs and compression minimize memory usage. Data is periodically flushed to databases like InfluxDB for long-term storage and historical querying.
Market Data Integration: Real-time updates are triggered by execution data from the matching engine.
The Product, Order, and Execution Models form the backbone of trading operations and trade processing. The Candlestick Chart Models optimize market data visualization and analytics, balancing real-time performance with scalable, persistent storage for modern financial exchanges.
Client Gateway
The Client Gateway serves as the primary entry point for brokers and institutional clients, ensuring that all incoming requests are properly validated and secured. It handles tasks such as input validation to check order formats, rate limiting to prevent abuse, and authentication to verify clients. Additionally, it normalizes orders to maintain consistency across the system.
Order Manager
The Order Manager manages all orders, ensuring they are processed in the correct sequence. It conducts essential risk checks, such as validating that orders comply with trading limits, and verifies user wallets to ensure sufficient funds or holdings are available for the trade. Once validated, the orders are sent to the Sequencer or directly to the Matching Engine.
Sequencer
The Sequencer ensures determinism in the system by assigning a unique sequence ID to every incoming order and outgoing execution. This guarantees that events are processed in a consistent order. The Sequencer also writes all events, including orders and executions, to an Event Store for auditability and recovery purposes.
Matching Engine
At the core of the trading process, the Matching Engine is responsible for matching buy and sell orders based on rules such as price-time priority. It maintains the Order Book for each trading symbol, executes trades by matching orders, and distributes the resulting execution data to downstream systems for further processing.
Order Book
The Order Book is a dynamic data structure that stores all active buy and sell orders for a particular trading symbol. It organizes orders by price levels, enabling efficient operations such as adding, canceling, and matching orders in real time.
Market Data Publisher
The Market Data Publisher processes and distributes real-time market data to subscribers, including brokers and analytics platforms. It builds and updates candlestick charts, reflects changes in the order book, and disseminates this information to ensure all participants have timely and accurate market insights.
Reporting System
The Reporting System aggregates trading data for compliance, settlements, and analytics. It generates detailed reports for regulators, clients, and internal stakeholders, ensuring transparency and accountability across the exchange's operations.
Risk Management
Risk Management ensures adherence to regulatory and operational rules. It continuously monitors exposure limits for users and brokers and enforces trading rules and restrictions to maintain system integrity and compliance.
ID12345) to the order for determinism.The sequencer is the backbone of event ordering in the trading system, ensuring that all incoming events—new orders, cancellations, and matches—are processed in a globally consistent order. By assigning a strictly increasing sequence ID to each event, it guarantees that the system processes them in the exact order they were received, which is critical for determinism.
This determinism ensures that trading outcomes are repeatable and auditable. If the system needs to replay events, such as after a failure, the exact state can be reconstructed. For example, if a buy order for 100 shares of AAPL at 150 arrives first, it is assigned ID #1001. A sell order for 50 shares at 150 arriving next gets ID #1002. These sequence IDs ensure that the buy order is processed before the sell, preserving fairness and preventing race conditions.
The sequencer operates as a single-writer system to minimize contention and achieve low latency. It integrates closely with ring buffers from upstream components, stamping sequence IDs as events flow through, and forwarding them to the matching engine or event store. Events are also logged durably, often using memory-mapped files (mmap), to combine the speed of in-memory operations with the resilience of persistent storage.
In the event of failure, a backup sequencer seamlessly takes over, leveraging the consistent state maintained in the event store. This ensures uninterrupted operation and fault tolerance without compromising performance.
By maintaining strict ordering with minimal overhead, the sequencer enables the system to meet the high demands of modern trading.
At its heart, the matching engine is responsible for processing and executing trades by comparing incoming buy and sell orders against the existing state of the market. The order book serves as its repository, holding all active orders in memory for instant access. Together, these components must operate with sub-millisecond latency to meet the demands of modern financial markets.
The order book is structured to ensure efficient and fair processing. Buy orders are sorted by descending price, while sell orders are arranged in ascending order, ensuring that the highest-priority trades are executed first. Within each price level, orders are managed using FIFO (First In, First Out) principles to maintain fairness based on entry time.
The matching engine builds on this structure by continuously scanning the top levels of the order book for potential matches. When a buy order meets or exceeds the price of a sell order, a match is executed. Partial matches are common, with unmatched portions either being retained in the book for future trades or canceled based on user specifications. The deterministic nature of this process ensures that the same sequence of input orders always produces the same result, which is critical for compliance and auditability.
The trading system achieves ultra-low latency and dynamic scalability by combining in-memory processing, consistent hashing, sharding, and a hybrid infrastructure model tailored to the demands of high-frequency trading.
Low Latency Through In-Memory Processing
Latency-critical operations are executed entirely in memory, eliminating delays from disk I/O and network overhead. The system employs optimized data structures that are purpose-built for specific tasks:
For instance, adding an order to a price level is a constant-time operation, while finding the best price leverages efficient tree traversal. These optimizations collectively allow the system to process millions of orders per second with minimal overhead.
Cache alignment reduces latency further by structuring memory layouts to match CPU cache lines, minimizing misses and maximizing throughput. Ring buffers ensure constant-time read/write operations for critical paths, while pipeline parallelism divides tasks like validation, matching, and reporting into concurrent stages. Workload partitioning across CPU cores ensures high-activity symbols like AAPL are processed independently of lower-activity symbols, preserving overall system performance.
Scalable Sharding and Load Balancing
To manage workloads, the system employs sharding, where trading symbols are distributed across independent processing units. Consistent hashing ensures efficient workload distribution and minimal disruption during scaling or shard adjustments:
Consistent hashing ensures that when shards are added or removed (e.g., during scaling events), only a small subset of symbols is reassigned. This minimizes operational disruption and avoids the need to rebuild entire order books.
Each shard operates autonomously, maintaining its own in-memory order books and processing pipelines, ensuring parallel execution and fault isolation. This design ensures that surges in one shard, such as a spike in TSLA trading, do not impact the performance of others.
Real-time load balancing dynamically redistributes workloads based on metrics like CPU usage, memory consumption, and order latency. For example, during a TSLA trading surge, additional shards can be assigned to absorb the load seamlessly. Load adjustments leverage consistent hashing, which ensures efficient redistribution with minimal data movement.
Elastic Scaling for Flexibility
Latency-sensitive tasks are anchored in on-premises infrastructure, leveraging bespoke hardware like FPGAs and high-performance NICs to achieve deterministic microsecond-level performance. Auxiliary workloads, such as compliance reporting, analytics, and backups, are offloaded to cloud infrastructure for elasticity.
This architecture integrates low-latency in-memory processing, consistent hashing for efficient sharding, and elastic cloud scaling to deliver a trading platform that balances precision, scalability, and cost efficiency.
By anchoring latency-critical functions in highly optimized on-premises systems and offloading non-critical workloads to the cloud, the system processes billions of transactions daily with robustness and reliability. Dynamic resource allocation, predictive scaling, and fault-tolerant shard designs ensure the platform remains agile and performant, even under extreme trading conditions.
Robustness is non-negotiable in a trading system. Any downtime or data loss can result in significant financial and reputational damage. To mitigate this, the system employs a hot-warm replication architecture. The primary matching engine processes trades in real-time, while a secondary engine passively mirrors its state. This mirroring includes synchronizing the in-memory order book, event logs, and other critical data structures.
If the primary fails, the secondary is promoted without interrupting operations. Health checks and heartbeat signals continuously monitor the primary, ensuring that failover happens seamlessly. Event sourcing complements this by providing an immutable log of all state-changing actions, such as order placements, cancellations, and matches. These events are stored in a durable, append-only log, which acts as the single source of truth for the system.
During recovery, the system replays these events in their original order to rebuild the in-memory state. To optimize recovery times, the system also takes periodic snapshots of the order book. These snapshots capture the current state and are stored alongside the event log. In the event of a failure, the system can load the most recent snapshot and replay only the events that occurred after it, significantly reducing downtime.
The system not only processes trades but also generates valuable market insights in real-time. Candlestick charts, top-of-book prices, and deep order book views are published to clients with minimal latency. These insights are generated using execution data from the matching engine and distributed via optimized pipelines.
To ensure scalability, the system uses hierarchical publishing. Retail clients receive frequent updates for top-of-book data (L1), while institutional clients access deeper order book views (L2 and L3) on-demand. Advanced techniques like data compression and ring buffers minimize bandwidth usage and latency during high trading volumes.
The choice between a single sequencer and a distributed sequencer revolves around latency, fault tolerance, and scalability. A single sequencer provides low-latency, deterministic ordering ideal for high-frequency trading. Its simplicity ensures fast processing, but it introduces a single point of failure and scalability limits. Failures in the primary sequencer require robust failover mechanisms, potentially disrupting operations.
A distributed sequencer, using consensus protocols like Raft, improves fault tolerance and availability by replicating state across nodes. However, the added coordination introduces higher and more variable latency, complicating deterministic sequencing. This approach suits systems prioritizing resilience over ultra-low latency but adds implementation complexity.
The decision depends on system priorities. Single sequencers excel in low-latency environments, while distributed sequencers ensure higher availability.
When workers or nodes in the system crash, the architecture ensures the system remains operational and can recover swiftly. For example, each worker is responsible for a shard of the order book, which operates entirely in memory for ultra-low latency. In the event of a crash, this in-memory state is lost. To recover, the system relies on event sourcing and hot-warm redundancy. Event sourcing captures all state-changing events—such as new orders, cancellations, and executions—in an immutable log. This log serves as the system's source of truth. Upon recovery, the affected worker replays the event log to rebuild the in-memory order book. Checkpointing complements this by periodically taking snapshots of the order book to minimize replay time during recovery.
Hot-warm redundancy is implemented to reduce downtime further. While the "hot" primary instance actively processes events, the "warm" secondary instance mirrors the hot instance's state by consuming the same event stream but remains passive, not emitting any output. If the hot instance fails, the warm instance takes over immediately, promoting itself to primary. The system uses heartbeats and diagnostic signals to detect failures and initiate failover. This architecture minimizes the impact of a node crash, ensuring trading operations are not disrupted.
The design also incorporates dynamic load balancing to address potential imbalances caused by uneven trading volumes across shards. Symbols with high activity, such as AAPL during an earnings report, might overload a single worker. The system dynamically redistributes these workloads to other workers during periods of low activity, ensuring that no worker becomes a bottleneck.
The sequencer, which ensures global event ordering for determinism, introduces additional considerations in failure scenarios. Since the sequencer is critical to the system's correctness, it is often implemented as a highly optimized, centralized component with replication to avoid single points of failure. If the sequencer fails, a backup sequencer takes over, continuing the sequence from the last committed ID, ensuring no data loss or duplication.
Finally, the system addresses broader fault tolerance through leader election in distributed systems, typically using a consensus algorithm like Raft. In this architecture, the leader coordinates operations and ensures consistent state replication across nodes. If the leader fails, Raft’s election mechanism quickly selects a new leader, restoring coordination.
This layered approach—combining event sourcing, redundancy, dynamic load balancing, and consensus algorithms—ensures that the stock exchange remains highly available, fault-tolerant, and capable of handling failures without compromising performance or data integrity.
Implement auto-scaling systems that dynamically allocate resources based on market activity. For example, during market open or close, when order volumes spike, additional matching engine instances can be brought online automatically.
Decentralized Architectures
Decentralized architectures offer a forward-looking opportunity to further distribute the workload and reduce reliance on centralized systems. While traditional architectures use centralized matching engines for deterministic and high-speed processing, decentralized technologies such as blockchain or distributed ledger technology (DLT) could enable peer-to-peer matching in niche markets like digital assets. These architectures would allow participants to interact directly, with the ledger ensuring transaction integrity. Although decentralized systems often face challenges in latency and scalability, their potential for trustless and transparent operations makes them a promising avenue for specialized products or low-frequency trading environments.
Cloud Migration with Fair-Access Exchange
By using techniques like high-precision clock synchronization, adaptive delay mechanisms, and sharded matching engines, the system could address latency variability and scalability challenges inherent in the cloud. This enhancement would provide cost efficiency, resilience, and accessibility while ensuring fairness in order sequencing and market data dissemination.