Optimizing stock market execution costs using reinforcement learning

2020 ◽  
Author(s):  
Abdulrahman A. Ahmed ◽  
Ayman Ghoneim ◽  
Mohamed Saleh
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Execution Costs

scholarly journals Recommending Cryptocurrency Trading Points with Deep Reinforcement Learning Approach

2020 ◽  
Vol 10 (4) ◽  
pp. 1506 ◽  
Author(s):  
Otabek Sattarov ◽  
Azamjon Muminov ◽  
Cheol Won Lee ◽  
Hyun Kyu Kang ◽  
Ryumduck Oh ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Historical Data ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Specific Knowledge ◽  
Right Action ◽  
Net Profit ◽  
Market Needs ◽  
The Right

The net profit of investors can rapidly increase if they correctly decide to take one of these three actions: buying, selling, or holding the stocks. The right action is related to massive stock market measurements. Therefore, defining the right action requires specific knowledge from investors. The economy scientists, following their research, have suggested several strategies and indicating factors that serve to find the best option for trading in a stock market. However, several investors’ capital decreased when they tried to trade the basis of the recommendation of these strategies. That means the stock market needs more satisfactory research, which can give more guarantee of success for investors. To address this challenge, we tried to apply one of the machine learning algorithms, which is called deep reinforcement learning (DRL) on the stock market. As a result, we developed an application that observes historical price movements and takes action on real-time prices. We tested our proposal algorithm with three—Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH)—crypto coins’ historical data. The experiment on Bitcoin via DRL application shows that the investor got 14.4% net profits within one month. Similarly, tests on Litecoin and Ethereum also finished with 74% and 41% profit, respectively.

scholarly journals BUILDING AN ARTIFICIAL STOCK MARKET POPULATED BY REINFORCEMENT‐LEARNING AGENTS

2009 ◽  
Vol 10 (4) ◽  
pp. 329-341 ◽  
Author(s):  
Aleksandras Vytautas Rutkauskas ◽  
Tomas Ramanauskas
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Learning Algorithm ◽  
Self Regulation ◽  
Market Model ◽  
Emergent Properties ◽  
Q Learning ◽  
Evolutionary Selection ◽  
Learning Agents ◽  
Artificial Stock Market

In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement-learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self-regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision-making, application of the Q-learning algorithm for driving individual behaviour, and rich market setup. Along with that a parallel version of the model is presented, which is mainly based on research of current changes in the market, as well as on search of newly emerged consistent patterns, and which has been repeatedly used for optimal decisions’ search experiments in various capital markets.

scholarly journals Share Market Prediction using Deep Neural Network

2019 ◽  
Vol 8 (3) ◽  
pp. 8619-8622
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Stock Market ◽  
Deep Neural Network ◽  
Learning Technology ◽  
Financial Investment ◽  
The Future ◽  
Future Prediction

People, due to their complexity and volatile actions, are constantly faced with challenges in understanding the situation in the market share and the forecast for the future. For any financial investment, the stock market is a very important aspect. It is necessary to study while understanding the price fluctuations of the stock market. In this paper, the stock market prediction model using the Recurrent Digital natural Network (RDNN) is described. The model is designed using two important machine learning concepts: the recurrent neural network (RNN), multilayer perceptron (MLP) and reinforcement learning (RL). Deep learning is used to automatically extract important functions of the stock market; reinforcement learning of these functions will be useful for future prediction of the stock market, the system uses historical stock market data to understand the dynamic market behavior when you make decisions in an unknown environment. In this paper, the understanding of the dynamic stock market and the deep learning technology for predicting the price of the future stock market are described.

scholarly journals Stock Market Prediction and Investment using Deep Reinforcement Learning- a Continuous Training Pipeline

2020 ◽  
Vol 10 (2) ◽  
pp. 93-98
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Language Processing ◽  
Learning Algorithms ◽  
Statistical Prediction ◽  
Experimental Result ◽  
Future Market ◽  
Gradient System ◽  
Continuous Training ◽  
Market Trends

Fluctuating nature of the stock market makes it too hard to predict the future market trends and where to invest. Hence, there is a need for a cross application backed by an ultramodern architecture. With the latest advancement in Deep Reinforcement Learning, successive practical problems can be modeled and solved with human level accuracy. In this paper, an agent-based Deep Deterministic Policy Gradient system is proposed to imitate professional trading strategies which is a state-of-the-art framework that can predict and make investment of customers money with high return. In addition to this, dealing with interday trading strategy, the proposed architecture is designed as a continuous training pipeline so that the model saved is up-to-date with the recent market trends by giving higher accuracy in prediction. The framework outperforms the base reinforcement learning algorithms and maximizes portfolio return. The experimental result shows how natural language processing and statistical prediction can help us to choose the trending stock based on news headlines and historical data so that model invests money only in the market which gives higher return. To evaluate the performance of the proposed method, comparison of our portfolio results was done with various other reinforcement learning algorithms by keeping the same configuration.

scholarly journals Stock Market Trading Based on Market Sentiments and Reinforcement Learning

2022 ◽  
Vol 70 (1) ◽  
pp. 935-950
Author(s):  
K. M. Ameen Suhail ◽  
Syam Sankar ◽  
Ashok S. Kumar ◽  
Tsafack Nestor ◽  
Naglaa F. Soliman ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Stock Market Trading
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