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Low Latency Trading Systems in C++: A Deep Dive
0:00:11 Introduction: The speaker, David, introduces the topic of engineering low-latency trading systems, emphasizing a practical, engineering-focused approach rather than a theoretical one.
0:01:11 Historical Context: The talk begins with a historical overview of the Roman Empire, highlighting their early use of derivative trading (Roman future contracts) to manage economic uncertainty.
0:03:35 World uncertainity index: Demonstrated how physcological creatures attatch more weight to loss than gains, referencing sleeping at night.
0:04:37 Market Makers and Liquidity: Explains the role of market makers in providing liquidity and managing risk in modern financial markets, and the importance in a losers game, consistantly good at everything.
0:06:38 Low Latency Requirements: Discusses two primary reasons for low-latency programming in trading:
0:06:51 Fast Reaction: The need to react quickly to market events (news, price changes) to update or cancel orders.
0:07:16 Accuracy: The need for accurate pricing models, which relies on fast ingestion of market data.
0:07:50 Modern Trading System Architecture: Describes a typical modern trading system architecture, including exchanges, FPGAs, and software strategies. Explains why low-latency C++ is still crucial even with FPGAs:
0:08:37 Cost-Benefit: FPGAs are expensive to develop and maintain; software is more flexible.
0:09:05 Strategy Rules: Software strategies send rules to FPGAs, and these strategies themselves have low-latency needs.
0:10:27 Order Book Data Structure: Introduces the order book as a core component in trading systems, defining its properties (ordered sequences, price levels, order IDs) and low-latency requirements (fast updates, handling network card buffers).
0:14:51 Initial Order Book Implementation (std::map): Presents an initial implementation using two std::map (one for bids, one for asks). Highlights its good complexity (logarithmic for insertion, amortized constant for others) but poor cache locality.
0:19:56 Principle 1: Avoid Node Containers: Emphasizes avoiding node-based containers (std::map, std::set, std::list, std::unordered_map, std::unordered_set) in performance-critical code due to poor cache locality.
0:21:52 Improved Order Book Implementation (std::vector): Introduces an improved implementation using std::vector and std::lower_bound. Discusses its better cache locality but worse complexity (linear for insertions).
0:24:35 Optimizing Vector Implementation: Analyzes the data distribution of order book updates (exponentially distributed towards the top of the book) and reverses the vector to minimize memory shifts.
0:25:43 Principle 2: A well-stated problem is half-solved; understanding the problem domain is crucial.
0:26:12 Principle 3: Leverage specific problem properties for performance gains.
0:27:08 Profiling and Measurements Discusses the different measurement categories: retiring, bad speculation, front and bound and back end bound.
0:30:36 Branchless Binary Search: Introduces branchless binary search to reduce branch mispredictions, acknowledging the trade-off of no early exit.
0:35:15 Linear Search Optimization: Concludes that linear search is the fastest option for this specific order book implementation due to its excellent cache locality and hardware sympathy.
0:35:40 Principle 4: Simplicity and speed often go hand-in-hand; embrace simple solutions.
0:36:01 Principle 5: Mechanical sympathy β design algorithms that work in harmony with the hardware.
0:36:32 Additional Optimizations Includes use of "likely/unlikely" and discussion of inlining, compiler flags.
0:40:08 Networking and Concurrency: Briefly touches on networking (kernel bypass, shared memory) and concurrency (multi-process architectures).
0:42:53 Principle 6: Be efficient and mindful of your needs. Only use what you need.
0:44:11 Shared Memory: Explains the use of shared memory for inter-process communication (IPC) in low-latency systems.
0:46:59 Shared Memory Queue: Describes a custom, bounded, non-blocking, multi-consumer, single-producer shared memory queue with variable-length message support.
0:53:46 Queue Performance: Presents performance measurements of the custom queue, comparing it to Disruptor and Agrona.
0:58:54 Principle 7: You have a lot of different things that you can use, and it's imporant to use the right tool for the right task.
0:59:20 Further Queue Optimizations: Discusses potential further optimizations for the queue (bulk writing, NUMA awareness).
1:01:08 Intrusive profiling: Explanation of intrusive profiling.
1:04:39 Clang X-Ray: Introduces Clang X-Ray for low-overhead, dynamic instrumentation and profiling.
1:07:21 Principle 8: It's good to be fast, but staying fast is more difficult.
1:07:38 "You're Not Alone": Discusses the importance of considering the entire system's performance, not just individual components, due to shared resources like the L3 cache.
1:12:31 Principle 9: Emphasizes having empathy with your system. Not just your code performance, but other people's code performance matters too.
1:12:54 Final Thoughts: Reiterates the lack of a silver bullet in low-latency programming, emphasizing discipline, simplicity, and considering time-to-market latency.
1:14:15 Q and A: Starts questions and answers session.