Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems

High-frequency trading is a method of intervention on the financial markets that uses sophisticated software tools, and sometimes also hardware, with which to implement high-frequency negotiations, guided by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so on. HFT strategies have reached considerable volumes of commercial traffic, so much so that it is estimated that they are responsible for most of the transaction traffic of some stock exchanges, with percentages that, in some cases, exceed 70% of the total. One of the main issues of the HFT systems is the prediction of the medium-short term trend. For this reason, many algorithms have been proposed in literature. The author proposes in this work the use of an algorithm based both on supervised Deep Learning and on a Reinforcement Learning algorithm for forecasting the short-term trend in the currency FOREX (FOReign EXchange) market to maximize the return on investment in an HFT algorithm. With an average accuracy of about 85%, the proposed algorithm is able to predict the medium-short term trend of a currency cross based on the historical trend of this and by means of correlation data with other currency crosses using techniques known in the financial field with the term arbitrage. The final part of the proposed pipeline includes a grid trading engine which, based on the aforementioned trend predictions, will perform high frequency operations in order to maximize profit and minimize drawdown. The trading system has been validated over several financial years and on the EUR/USD cross confirming the high performance in terms of Return of Investment (98.23%) in addition to a reduced drawdown (15.97 %) which confirms its financial sustainability.

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Grid Trading System Robot (GTSbot): A Novel Mathematical Algorithm for trading FX Market

Applied Sciences ◽

10.3390/app9091796 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1796 ◽

Cited By ~ 12

Author(s):

Rundo ◽

Trenta ◽

di Stallo ◽

Battiato

Keyword(s):

High Frequency ◽

High Performance ◽

Foreign Exchange Market ◽

Financial Time Series ◽

Trading Strategy ◽

High Frequency Trading ◽

Trading System ◽

Trading Systems ◽

Time Series Forecast ◽

Financial Transactions

Grid algorithmic trading has become quite popular among traders because it shows several advantages with respect to similar approaches. Basically, a grid trading strategy is a method that seeks to make profit on the market movements of the underlying financial instrument by positioning buy and sell orders properly time-spaced (grid distance). The main advantage of the grid trading strategy is the financial sustainability of the algorithm because it provides a robust way to mediate losses in financial transactions even though this also means very complicated trades management algorithm. For these reasons, grid trading is certainly one of the best approaches to be used in high frequency trading (HFT) strategies. Due to the high level of unpredictability of the financial markets, many investment funds and institutional traders are opting for the HFT (high frequency trading) systems, which allow them to obtain high performance due to the large number of financial transactions executed in the short-term timeframe. The combination of HFT strategies with the use of machine learning methods for the financial time series forecast, has significantly improved the capability and overall performance of the modern automated trading systems. Taking this into account, the authors propose an automatic HFT grid trading system that operates in the FOREX (foreign exchange) market. The performance of the proposed algorithm together with the reduced drawdown confirmed the effectiveness and robustness of the proposed approach.

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A short-term trend prediction model of topic over Sina Weibo dataset

Journal of Combinatorial Optimization ◽

10.1007/s10878-013-9674-0 ◽

2013 ◽

Vol 28 (3) ◽

pp. 613-625 ◽

Cited By ~ 5

Author(s):

Juanjuan Zhao ◽

Weili Wu ◽

Xiaolong Zhang ◽

Yan Qiang ◽

Tao Liu ◽

...

Keyword(s):

Prediction Model ◽

Short Term ◽

Trend Prediction ◽

Sina Weibo ◽

Term Trend ◽

Short Term Trend

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Short-term trend prediction in financial time series data

Knowledge and Information Systems ◽

10.1007/s10115-018-1303-x ◽

2018 ◽

Vol 61 (1) ◽

pp. 397-429

Author(s):

Mustafa Onur Özorhan ◽

İsmail Hakkı Toroslu ◽

Onur Tolga Şehitoğlu

Keyword(s):

Time Series ◽

Time Series Data ◽

Financial Time Series ◽

Series Data ◽

Short Term ◽

Trend Prediction ◽

Term Trend ◽

Financial Time ◽

Short Term Trend

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Assessment of Seam Properties of Ahimsa Silk Union Fabrics

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7917.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 3059-3062

Keyword(s):

Cotton Fabric ◽

Natural Fiber ◽

Silk Fiber ◽

Short Term ◽

Term Trend ◽

The Earth ◽

Cotton Yarns ◽

Sustainable Fashion ◽

Short Term Trend ◽

Eri Silk

Sustainable fashion is not merely a short term trend but it could last many seasons and for generations to survive on the earth. Silk fiber is the most beautiful natural fiber known as the “Queen of Textiles”. Ahimsa silk is a non-violent, eco-friendly and sustainable process of the production. Hand spun and hand woven cotton fabric is another model of sustainable fabrics. Therefore, union fabrics in different ratio viz. 33:67, 50:50 and 67:33 were prepared from cotton with Ahimsa (Eri) silk and Conventional (Muga and Tussar) silk yarns. Objective of the study was to assess sewability parameters of union fabrics. These fabrics were tested for their seam puckering, seam stiffness and seam thickness parameters. The results indicate that union fabrics produced by Ahimsa silk with cotton were compatible to the union fabrics produced by Conventional silk with cotton yarns in their sewability parameters, so these should be preferred for construction of various fashion garments and textile products

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Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning

Energies ◽

10.3390/en13102640 ◽

2020 ◽

Vol 13 (10) ◽

pp. 2640 ◽

Cited By ~ 4

Author(s):

Rae-Jun Park ◽

Kyung-Bin Song ◽

Bo-Sung Kwon

Keyword(s):

Reinforcement Learning ◽

Predictive Accuracy ◽

Learning Algorithm ◽

Load Forecasting ◽

Selection Method ◽

Selection Model ◽

Short Term ◽

Short Term Load Forecasting ◽

Similar Day ◽

Reinforcement Learning Algorithm

Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert’s experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms.

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