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Title: Cross Exchange Arbitrage Algorithm on High Frequency Trading Platform
Authors: Puri, Jasmeet Singh
Supervisor: Bhatia, Parteek
Arora, Vinay
Keywords: HFT (High Frequency Trading);Algo-Trading
Issue Date: 22-Jul-2015
Abstract: From Last few years, financial markets have seen a large growth in number of trades’ execution. This growth is due to high number of orders being generated in very less amount of time and this is not possible manually. Some algorithm is processing this large amount of orders without any human intervention. This is called Algorithmic Trading or Algo Trading. Algo Trading is just running an algorithm with some set of instructions to the system to process order and pick opportunities according to the given logic. NSE (National Stock Exchange) has reported tremendous growth of algo trades and this figure has reached almost to 40% of the daily trades. Algorithm can process order as fast as 100 – 150 microseconds on a good configured server with all the calculations. With more improvement in other technology, this speed is going to improve more. Human cannot virtually respond to these scenarios. Lots of financial houses are driving towards this automated algorithm trading. Algorithm is about how to process given information to improve the set of orders. Now a day, financial traders have reduced their profit per orders and started dealing very frequently accumulating smaller profits to have substantiated profits. But very less attempts are made into cross exchange trading. Algorithms performance suffers when it has to process data from two exchanges to send the order. Cross Exchange Algorithm Trading is the next big thing which will drive growth of overall financial markets all over the world. Besides Algorithm, we need more sophisticated computer network technology to improve the performance of sending an order. Cross Exchange Trading can be possible with few limitations but it has more profit due to difference in spread between same financial securities
Description: M.E. (CSED)
Appears in Collections:Masters Theses@CSED

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