Research in High Frequency Trading and Pairs Selection Algorithm with Baltic Region Stocks

Vaitonis, Mantas; Masteika, Saulius

doi:10.1007/978-3-319-46254-7_17

Cited by 2 publications

(7 citation statements)

References 9 publications

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“…Trading; 6. Assessment of the pairs trading strategy (Driaunys et al, 2014;Masteika and Vaitonis, 2015;Vaitonis and Masteika, 2016). Before selecting the trading and data normalization window, every strategy must be trained.…”

Section: Methodsmentioning

confidence: 99%

“…It should be noted that a closing period is necessary, if there are no signals to close the positions and not to keep the position opened for too long. The selected parameters were based on our previous researches were we tested high frequency trading using microsecond data (Masteika and Vaitonis, 2015;Vaitonis and Masteika, 2016;Vaitonis and Masteika, 2017). Moreover it was necessary to test if the same parameters would work with nanosecond data.…”

Section: Data Normalizationmentioning

confidence: 99%

“…In our previous research, minimum squared distance method was used for selecting the trading pairs (Driaunys et al, 2014;Masteika and Vaitonis, 2015;Vaitonis and Masteika, 2016). Minimum squared distance method is a distance method.…”

Section: Pair Selection (Correlation)mentioning

confidence: 99%

“…It is assumed that the price did not change for that time. In this way, all time stamps of futures contracts are normalized for nanosecond and microsecond data (Masteika and Vaitonis, 2015;Vaitonis and Masteika, 2016).…”

Section: Data Normalizationmentioning

confidence: 99%

“…One of the most popular is distance method, which has been analysed in the previous research (Driaunys et al, 2014;Masteika and Vaitonis, 2015;Vaitonis and Masteika, 2016). The popularity of this method may be attributed to the use of simple linear correlation coefficient as a measure of dependence which makes the implementation convenient (Lau, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Experimental Comparison of HFT Pair Trading Strategies using Microsecond and Nanosecond Future Commodity Contracts Data

Vaitonis¹,

Masteika²

2018

BJMC

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Abstract. Nowadays, it is not necessary for humans to conduct trade; this task is performed by trading algorithms. The speed of trading is of the most importance, however, there are relatively few aoutademic researches on the increased speed of trading from milliseconds to nanoseconds. In order to address the aforementioned shortcoming, this research measures the differences in the effectiveness of the pairs trading strategies, emerging when microsecond and nanosecond data are included. The effect of the increased speed of data is analysed. We present different pairs trading strategies and one pair selection algorithm, based on the cointegration method. These trading strategies are implemented on five different commodity futures contracts using both microsecond and nanosecond historical data. The effectiveness is measured in accordance with the profit, generated at the end of the trading period. In order to measure the effectiveness of all presented pairs trading strategies, the Sharpe Ratio method was introduced.

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Section: Methodsmentioning

confidence: 99%

Section: Data Normalizationmentioning

confidence: 99%

Section: Pair Selection (Correlation)mentioning

confidence: 99%

Section: Data Normalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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