Forecasting Forex Trend Indicators with Fuzzy Rough Sets

Sepúlveda, Julio; Lopez-Irarragorri, F.; Schaeffer, Satu Elisa

doi:10.1007/s10614-022-10281-3

Cited by 3 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers tend to work with interval financial data rather than with the raw data firstly because it is more accessible and secondly because less computational processing power is required. As shown in Table 1, researchers use daily (Araújo et al, 2019; Bukhari et al, 2020; Fischer & Krauss, 2018; Garza Sepúlveda et al, 2022; Heryadi & Wibowo, 2021; Pande et al, 2021; Sornmayura, 2019) or hourly (Ali, 2018; Ersan et al, 2020; Jirapongpan & Phumchusri, 2020; Moews et al, 2019; Naeem et al, 2021) integral data over the historical period of interest (usually varying from decades to months) in the form of Japanese candlesticks to train neural networks. For the analysis of intraday trading, five‐minute integral data is usually used (Barra et al, 2020; Chen et al, 2020; Markova, 2022; Mehtab & Sen, 2020; Zhao & Khushi, 2020).…”

Section: Introductionmentioning

confidence: 99%

Reduction of financial tick big data for intraday trading

Milke,

Luca,

Wilson

2024

Expert Systems

View full text Add to dashboard Cite

Various neural network architectures are often used to forecast movements in financial markets. Most research in quantitative analytics in finance uses interval financial data as this reduces the raw tick big data, but the averaging can lose key behaviour patterns. This work presents an alternative novel method to reduce raw tick data whilst retaining important information for training, as demonstrated with intraday trading using the EURO/USD currency pair. This time series reduction method focuses on short periods preceding significant movements in financial features and allows the most popular neural network architectures to be applied using less powerful but more readily available computer resources. It is shown that the proposed data preprocessing method for machine learning and other AI‐techniques successfully reduced the size of the selected dataset covering a three‐year period (2018–2021) by 275 times.

show abstract