A Deep Learning Approach for Optimization of Systematic Signal Detection in Financial Trading Systems with Big Data

Abstract

Expert systems for trading signal detection have received considerable attention in recent years. In financial trading systems, investors' main concern is determining the best time to buy or sell a stock. The trading decisions are often influenced by the emotions and feelings of the investors. Therefore, investors and researchers have aimed to develop systematic models to reduce the impact of emotions on trading decisions. Nevertheless, the use of algorithmic systems face another problem called "lack of dynamism". Due to dynamic nature of financial markets, trading robots should quickly learn and adapt as human traders. Recently, a solution for detecting trading signals based on a dynamic threshold selection was proposed. In this study, we extend this approach by adopting several different rule based systems and enhancing it by using the Recurrent Neural Network algorithm. Recurrent Neural Networks learn the connection weights of subsystems with arbitrary sequences of inputs that make them a great fit for time series data. Our model is based on Piecewise Linear Representation and Recurrent Neural Network with the goal of detecting potential excessive movements in noisy stream of time series data. We use an exponential smoothing technique to detect abnormalities. Trading signals are produced using fixed time interval data from Istanbul Stock Exchange. The evaluations indicated that our model produces successful results in trading data. Future work will focus on further improvements and scalability of the model.

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

“…The authors of [107] used several technical indicator features and time series data with Principal Component Analysis (PCA) for dimensionality reduction cascaded with DNN (2-layer FFNN) for stock price prediction. In [108], the authors used Market microstructures based trade indicators as inputs into RNN with Graves LSTM detecting the buy-sell pressure of movements in Istanbul Stock Exchange Index (BIST) in order to perform the price prediction for intelligent stock trading. In [109], next month's return was predicted and top to be performed portfolios were constructed.…”

A survey of deep learning applications in cryptocurrency

“…Karaoglu et al. 79 improved RNN to detect excessive movement in noisy time series data streams. Berat Sezer et al.…”

Deep Learning for Financial Applications : A Survey

“…Meanwhile, LSTM was the most preferred DL model in these implementations. In [35], market microstructures based trade indicators were used as the input into RNN with Graves LSTM to perform the price prediction for algorithmic stock trading. Bao et al [36] used technical indicators as the input into Wavelet Transforms (WT), LSTM and Stacked Autoencoders (SAEs) for the forecasting of stock prices.…”