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README
Apache-2.0

version

match-engine

介绍

match-trade超高效的交易所撮合引擎,采用伦敦外汇交易所LMAX开源的Disruptor框架,分布式内存存取,以及原子性操作。使用数据流的方式进行计算撮合序列,才用价格水平独立撮合逻辑,实现高效大数据撮合。PS:这不是一个具备生产上线的项目,仅供学习参考。生产级项目见:mxs-exchange

优势

  • match-engine水平价格为独立撮合逻辑,相比于订单队列为撮合队列的交易引擎来说,价格区间越小时,性能越优越。
  • match-engine不再对撮合薄进行排序,而是用并行流计算出最优撮合价格,进行撮合。
  • match-engine每个价格下的订单都是异步完成被撮合。独立价格下订单不影响下一个新发生的撮合。
  • match-engine每个价格撮合都是独立的,与下一个价格没的关系,实现快速吃单。
  • match-engine每个新的订单经历撮合处理器后,后续逻辑采用并行计算,能更快速反馈数据撮合结果。
  • match-engine使用数据流反应式MQ消费,降低由MQ带来的数据延迟。
  • match-engine撤单走独立的逻辑,不用和下单在一个处理序列。

技术选择

  • Disruptor: 号称每秒钟承载600万订单级别的无锁并行计算框架,主要选择原因还是并行计算。
  • Hazelcast: 很好进行内存处理,很强原子性保障的操作能力。同时分布式内存实现很简单,能自动内存集群。据说火币也在用。
  • Ignite: 内存占用小,速度快,能像做数据库撮合一样,强大的内存索引能力,玩转内存撮合。
  • rocketmq: 消息可以做到0丢失,支持10亿级别的消息堆积,不会因堆积导致性能下降,主要是经过双11检验
  • WebFlux: 它能够充分利用多核 CPU 的硬件资源去处理大量的并发请求。

描述

用户输入包括:

  • 创建新的委托单(NewOrder):一个新的委托单可以作为交易撮合引擎的输入,引擎会尝试将其与已有的 委托单进行撮合。
  • 取消已有的委托单(CancelOrder):用户也可以取消一个之前输入的委托单,如果它还没有执行的话,即开口订单。

委托单:

  • 限价委托单
    限价委托单是在当前的加密货币交易环境中最常用的委托类型。这种委托单允许用户指定一个价格,只有当撮合引擎找到同样价格甚至更好价格的对手单时才执行交易。
  • 市价委托单
    市价委托单的撮合会完全忽略价格因素,而致力于有限完成指定数量的成交。市价委托单在交易委托账本中有较高的优先级,在流动性充足的市场中市价单可以保证成交。不充足时,撮合完最后一条撤销。
  • 止损委托单
    止损委托单尽在市场价格到达指定价位时才被激活,因此它的执行方式与市价委托单相反。一旦止损委托单激活,它们可以自动转化为市价委托单或限价委托单。(未实现)

撮合流程

限价撮合: 输入图片说明

市价撮合: 输入图片说明 目前就实现这两种订单撮合

代码执行流程图(感谢读者提供的图)

输入图片说明

订单簿为撮合簿时代码解析

这个是一个简单流盘口计算demo

//获取匹配的订单薄数据
IMap<Long, Order> outMap = hzInstance.getMap(HzltUtil.getMatchKey(coinTeam, isBuy));
/**
 * -★
 * -使用Java 8 Stream API中的并行流来计算最优
 * -能快速的拿到撮合对象,不用排序取值,降低性能消耗
 */
Order outOrder = outMap.values().parallelStream().min(HzltUtil::compareOrder).get();

//这种方式最难的,就是整理盘口深度数据了

    /**
     * -★
	 * -获取行情深度
	 * 
	 * @param coinTeam 交易队
	 * @param isBuy    是否是买
	 * @return List<Depth>
	 */
	public List<Depth> getMarketDepth(String coinTeam, Boolean isBuy) {
		List<Depth> depths = new ArrayList<Depth>();
		IMap<Long, Order> map = hzInstance.getMap(HzltUtil.getMatchKey(coinTeam, isBuy));
		if (map.size() > 0) {
			/**
			 * -这个流:主要是安价格分组和统计,使用并行流快速归集。
			 */ 
			List<Depth> list = map.entrySet().parallelStream().map(mo -> mo.getValue())
					.collect(Collectors.groupingBy(Order::getPrice)).entrySet().parallelStream()
					.map(ml -> new Depth(ml.getKey().toString(),
							ml.getValue().stream().map(o -> o.getUnFinishNumber()).reduce(BigDecimal.ZERO, BigDecimal::add)
									.toString(),
							"0", 1, coinTeam, isBuy))
					.sorted((d1, d2) -> HzltUtil.compareTo(d1, d2)).collect(Collectors.toList());
			/**
			 * -这个流:主要是盘口的累计计算,因涉及排序选择串行流
			 */
			list.stream().reduce(new Depth("0", "0", "0", 1, coinTeam, isBuy), (one, two) -> {
				one.setTotal((new BigDecimal(one.getTotal()).add(new BigDecimal(two.getNumber()))).toString());
				depths.add(new Depth(two.getPrice(), two.getNumber(), one.getTotal(), two.getPlatform(),
						two.getCoinTeam(), two.getIsBuy()));
				return one;
			});
		} else {
			Depth depth = new Depth("0", "0", "0", 1, coinTeam, isBuy);
			depths.add(depth);
		}
		return depths;
	}

测试结果

在我8cpu,16G内存的开发win10系统上测试结果:

  • Disruptor单生产者初始化10万不能撮合的订单耗时:约700毫秒
  • Disruptor多生产者初始化10万不能撮合的订单耗时:约20秒
  • 实际单吃完1-100价格内随机数量的10万订单耗时:约400毫秒

学习讨论群

输入图片说明

想实现内存集群,或者撮合集群的(保证多台撮合,其中一台宕机,撮合正常),请加群。

重点:这套代码只是适合小量的交易场景,因分布式内存受RPC的性能影响全局TPS必然不可能很高,所以本套代码仅供参考。

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简介

最高效的交易所撮合引擎,采用伦敦外汇交易所LMAX开源的Disruptor框架,用Hazelcast/Ignite进行分布式内存存取,以及原子性操作。使用数据流的方式进行计算撮合序列,采用价格水平独立撮合逻辑,实现高效大数据撮合。 展开 收起
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