Using Convolutional Neural Networks and Raw Data to Model Intraday Trading Market Behaviour

Milke, Vitaliy; Luca, Cristina; Wilson, George; Fatima, Arooj

doi:10.5220/0008992402240231

Cited by 1 publication

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“…The training of a neural network using raw tick data typically requires a 3‐D tensor of input which can be as large as 90 billion float‐type numbers (Milke et al, 2020). This requires significant and prohibitive computing resources (Nison, 1994; Riyazahmed, 2021; Sandubete & Escot, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The actual raw financial market data is big data in the form of tick information (prices and volumes of each trade or settlement). Such tick data occurs where there are minimal changes in the buy (bid) and sell (ask) prices.The training of a neural network using raw tick data typically requires a 3-D tensor of input which can be as large as 90 billion float-type numbers (Milke et al, 2020). This requires significant and prohibitive computing resources (Nison, 1994;Riyazahmed, 2021;Sandubete & Escot, 2020).…”

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confidence: 99%

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Reduction of financial tick big data for intraday trading

Milke,

Luca,

Wilson

2024

Expert Systems

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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.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%