Stock Market Trading Agent Using On-Policy Reinforcement Learning Algorithms

2020 ◽  
Author(s):  
Shreyas Lele ◽  
Kavit Gangar ◽  
Harshal Daftary ◽  
Dewashish Dharkar
Keyword(s):  
Reinforcement Learning ◽  
Stock Market ◽  
Learning Algorithms ◽  
Trading Agent ◽  
Stock Market Trading

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 Importance of Machine Learning in Making Investment Decision in Stock Market

2021 ◽  
pp. 025609092110599
Author(s):  
Akhilesh Prasad ◽  
Arumugam Seetharaman
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Stock Market ◽  
Stock Price ◽  
Learning Algorithms ◽  
Technical Analysis ◽  
Processing Unit ◽  
Accuracy Score ◽  
Research Papers ◽  
Price Trends

Executive Summary Predicting stock trends in the financial market is always demanding but satisfying as well. With the growing power of computing and the recent development of graphics processing unit and tensor processing unit, analysts and researchers are applying advanced techniques such as machine learning techniques more and more to predict stock price trends. In recent years, researchers have developed several algorithms to predict stock trends. To assist investors interested in investing in the stock market, preferably for a short period, it has become necessary to review research papers dealing on machine learning and analyse the importance of their findings in the context of how stock price trends generate trading signals. In this article, to achieve the stated task, authors scrutinized more than 50 research papers focusing on various machine learning algorithms with varied levels of input variables and found that though the performance of models measured by root-mean-square error (RMSE) for regression and accuracy score for classification models varied greatly, long short-term memory (LSTM) model displayed higher accuracy amongst the machine and deep learning models reviewed. However, reinforcement learning algorithm performance measured by profitability and Sharpe ratio outperformed all. In general, traders can maximize their profits by using machine learning instead of using technical analysis. Technical analysis is very easy to implement, but the profit based on it can vanish too soon or making a profit using technical analysis is almost difficult because of its simplicity. Hence, studying machine, deep and reinforcement learning algorithms is vital for traders and investors. These findings were based on the literature review consolidated in the result section.

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

scholarly journals Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4948
Author(s):  
Lorenzo Canese ◽  
Gian Carlo Cardarilli ◽  
Luca Di Di Nunzio ◽  
Rocco Fazzolari ◽  
Daniele Giardino ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mathematical Models ◽  
Learning Algorithms ◽  
Single Agent ◽  
Critical Issues ◽  
Multi Agent ◽  
Pros And Cons ◽  
Application Fields

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

scholarly journals Artificial Intelligence Applied to Stock Market Trading: A Review

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 30898-30917 ◽  
Author(s):  
Fernando G. D. C. Ferreira ◽  
Amir H. Gandomi ◽  
Rodrigo T. N. Cardoso
Keyword(s):  
Artificial Intelligence ◽  
Stock Market ◽  
Stock Market Trading

scholarly journals Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control

2021 ◽  
Vol 298 ◽  
pp. 117164
Author(s):  
Marco Biemann ◽  
Fabian Scheller ◽  
Xiufeng Liu ◽  
Lizhen Huang
Keyword(s):  
Reinforcement Learning ◽  
Experimental Evaluation ◽  
Learning Algorithms ◽  
Model Free ◽  
Hvac Control

scholarly journals Synthetic Experiences for Accelerating DQN Performance in Discrete Non-Deterministic Environments

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 226
Author(s):  
Wenzel Pilar von Pilchau ◽  
Anthony Stein ◽  
Jörg Hähner
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Weighted Average ◽  
Up States ◽  
Experience Replay

State-of-the-art Deep Reinforcement Learning Algorithms such as DQN and DDPG use the concept of a replay buffer called Experience Replay. The default usage contains only the experiences that have been gathered over the runtime. We propose a method called Interpolated Experience Replay that uses stored (real) transitions to create synthetic ones to assist the learner. In this first approach to this field, we limit ourselves to discrete and non-deterministic environments and use a simple equally weighted average of the reward in combination with observed follow-up states. We could demonstrate a significantly improved overall mean average in comparison to a DQN network with vanilla Experience Replay on the discrete and non-deterministic FrozenLake8x8-v0 environment.

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