Big data framework for quantitative trading system

Dai, Shuji; Wu, Xing; Pei, Mengqi; Du, Zhikang

doi:10.1007/s12204-017-1821-9

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…In contrast to quantitative trading approaches utilised for decades, financial technology (FinTech) companies have significantly changed these trading strategies to handle big data appropriately. FinTechs are mainly established around integrating artificial intelligence (AI) and finance [6]. As one of the primary tools of FinTechs, AI has become a popular research subject in quantitative trading and presents excellent efficiency in discovering profitable trading rules [7,8].…”

Section: Introductionmentioning

confidence: 99%

Applying Artificial Intelligence in Cryptocurrency Markets: A Survey

2022

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The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple’s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening every day. Similar to other financial systems, price prediction is one of the main challenges in cryptocurrency trading. Therefore, the application of artificial intelligence, as one of the tools of prediction, has emerged as a recently popular subject of investigation in the cryptocurrency domain. Since machine learning models, as opposed to traditional financial models, demonstrate satisfactory performance in quantitative finance, they seem ideal for coping with the price prediction problem in the complex and volatile cryptocurrency market. There have been several studies that have focused on applying machine learning for price and movement prediction and portfolio management in cryptocurrency markets, though these methods and models are in their early stages. This survey paper aims to review the current research trends in applications of supervised and reinforcement learning models in cryptocurrency price prediction. This study also highlights potential research gaps and possible areas for improvement. In addition, it emphasises potential challenges and research directions that will be of interest in the artificial intelligence and machine learning communities focusing on cryptocurrencies.

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Section: Introductionmentioning

confidence: 99%

Applying Artificial Intelligence in Cryptocurrency Markets: A Survey

2022

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Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method

Vezeris

Schinas

Kyrgos³

et al. 2019

Comput Econ

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Hedging and non-hedging trading strategies on commodities using the d-Backtest PS method. Optimized trading system hedging

Vezeris

Kyrgos

Schinas

2018

Investment Management and Financial Innovations

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Modern trading systems are mechanic, run automatically on computers inside trading platforms and decide their position against the market through optimized parameters and algorithmic strategies. These systems now, in most cases, comprise high frequency traders, especially in the Forex market.In this research, a piece of software of an automatic high frequency trading system was developed, based on the technical indicator PIVOT (price level breakthrough). The system made transactions on hourly closing prices with weekly parameters optimization period, using the d-Backtest PS method.Through the search and checking of the results, two findings for optimization of trading strategy were found. These findings with the order they were examined and are presented in this paper are as follows: (1) the simultaneous use of “long and short” positions, with different parameters in a hedging account, acts as a hedging strategy, minimizing losses, in relation to a “long or short” in a non-hedging account for the same time period and (2) there is weak correlation of past backtesting periods between the same systems, if they are configured for “long and short” trades, or for just “long” or for just “short”.

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Big data framework for quantitative trading system

Cited by 4 publications

References 7 publications

Applying Artificial Intelligence in Cryptocurrency Markets: A Survey

Applying Artificial Intelligence in Cryptocurrency Markets: A Survey

Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method

Hedging and non-hedging trading strategies on commodities using the d-Backtest PS method. Optimized trading system hedging

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