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C Optimize performance in high-frequency trading through the following techniques: reducing memory allocation and deallocation; optimizing data structures (such as hash tables and B-trees); using caching; leveraging multi-threaded programming.
Introduction
In the field of high-frequency trading, performance is crucial , any slight delay may result in trading losses. C is known for its efficient and low-latency properties, making it ideal for high-frequency trading tasks. This article will explore C's various techniques for improving performance in high-frequency trading.
Optimization Notes
Practical case
Optimize data structure:
// 使用哈希表快速查找订单 std::unordered_map<int, Order> orders; // 使用 B 树处理限价订单 std::multimap<double, Order> limit_orders;
Use cache:
// 缓存最近成交的价格 std::map<std::string, double> price_cache; // 从缓存获取价格,避免从主存储器读取 double get_price(std::string symbol) { auto it = price_cache.find(symbol); if (it != price_cache.end()) return it->second; // 如果未在缓存中,从主存储器加载价格... }
Multi-threaded programming:
// 并发处理订单 std::vector<std::thread> threads; for (auto& order : orders) { threads.push_back(std::thread([&order] { // 处理订单... })); } for (auto& thread : threads) { thread.join(); }
By applying these optimization techniques, C developers can significantly improve the performance of high-frequency trading applications to compete in the highly competitive financial markets have an advantage.
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