A Multiagent Approach to $Q$-Learning for Daily Stock Trading

Lee, Jae Won; Park, Jonghun; Jangmin, O; Lee, Jongwoo; Hong, Eunki

doi:10.1109/tsmca.2007.904825

Cited by 96 publications

(42 citation statements)

References 13 publications

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“…Data not present in Table 2 showed that the average return per trade is 0.06537% for the long trades and 0.2691% for short trades, which means that the OneR classifier was extremely profitable in downwards periods for this particular experiment. For the other classifiers, while prediction accuracies may be similar to those observed in the literature (Chen & Shih, 2006;Eng et al, 2008;Kim, 2003;Lee et al, 2007; S. Li & Kuo, 2008;Tenti, 1996), the average return per trade does not allow the models to have a positive return at the end of the trading period. These initial results suggest that it is possible to generate positive return with modest middle range accuracy.…”

Section: Single Training Experimentssupporting

confidence: 52%

“…Maggini et al (Maggini, Giles, & Horne, 1997) had pointed out that there is an inherent difficulty in generating statistically reliable technical indicators, due to the fact that the rules inferred to produce accurate predictions are changing continually in financial time series, and that it is even possible to evidence the presence of a high number of contradictory instances in the training sets due to the fact that market data exhibit statistical characteristics found in other types of time series. This situation is reflected in the large volume of papers (Chen & Shih, 2006;Eng, Li, Wang, & Lee, 2008;Kim, 2003;Lee, Park, O, Lee, & Hong, 2007;S. Li & Kuo, 2008;Tenti, 1996) that have reported accuracies under 60% with ML models which have shown impressive performance in areas other than financial prediction.…”

Section: Machine Learning In Financial Forecastingmentioning

confidence: 99%

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Evaluating machine learning classification for financial trading: An empirical approach

Gerlein

McGinnity

Belatreche

et al. 2016

Expert Systems with Applications

133

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Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.

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Section: Single Training Experimentssupporting

confidence: 52%

Section: Machine Learning In Financial Forecastingmentioning

confidence: 99%

Evaluating machine learning classification for financial trading: An empirical approach

Gerlein

McGinnity

Belatreche

et al. 2016

Expert Systems with Applications

133

View full text Add to dashboard Cite

show abstract

“…It was conducted using several selected shares on the Italian stock market over an investment period of 30 stock market years with the algorithm making buy and sell decisions on the six selected shares over the period [13]. Deep Q-learning have also been applied to the foreign exchange market against the baseline buy and hold strategy and an expert trader [14] as well as to a stock market index [15]. Sornmayura in 2019 applied this methodology and compared its performance against the expert trade and baseline buy and hold strategy using the currency pairs EUR/USD and USD/JPY within 15 years of foreign exchange market data [14].…”

Section: On and Off Policy Reinforcement Learningmentioning

confidence: 99%

Reinforcement Learning in Financial Markets

Meng

Khushi

2019

Data

104

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Recently there has been an exponential increase in the use of artificial intelligence for trading in financial markets such as stock and forex. Reinforcement learning has become of particular interest to financial traders ever since the program AlphaGo defeated the strongest human contemporary Go board game player Lee Sedol in 2016. We systematically reviewed all recent stock/forex prediction or trading articles that used reinforcement learning as their primary machine learning method. All reviewed articles had some unrealistic assumptions such as no transaction costs, no liquidity issues and no bid or ask spread issues. Transaction costs had significant impacts on the profitability of the reinforcement learning algorithms compared with the baseline algorithms tested. Despite showing statistically significant profitability when reinforcement learning was used in comparison with baseline models in many studies, some showed no meaningful level of profitability, in particular with large changes in the price pattern between the system training and testing data. Furthermore, few performance comparisons between reinforcement learning and other sophisticated machine/deep learning models were provided. The impact of transaction costs, including the bid/ask spread on profitability has also been assessed. In conclusion, reinforcement learning in stock/forex trading is still in its early development and further research is needed to make it a reliable method in this domain.Google Scholar was used to search for the reinforcement learning articles for this systematic review. By typing into Google Scholar the key phrases "reinforcement learning forex" and "reinforcement learning stock trading" and then all the results were filtered according to the selection process given below in Figure 1. The search results returned from Google Scholar was automatically sorted by relevance, therefore only the first few pages are selected for manual inspection for eligibility. Afterwards, the results of the searched phrases were manually inspected without opening the article links to determine the most relevant articles for this systematic review. The selected articles from this step were then opened and their content read through to determine the final list of articles to be reviewed. Of 27 articles reviewed, 20 articles implemented or simulated trades to maximise profit and 7 articles were only interested in forecasting future financial asset prices. Of 20 trading articles 11 articles provided comparison with other models and 10 did not.

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“…The comparison for the performance of Q‐Learning and RRL to optimize the portfolios is provided in (Du, Zhai, and Lv ()), and the authors concluded that RRL delivers superior results. The authors (Lee, Park, Jangmin, Lee, and Hong ()) proposed a model that incorporates multiple Q‐Learning agents to provide adequate decision support for the daily stock trading problem. Moody and Saffell () introduced a trading system based on direct RL to optimize stock trading.…”

Section: Introductionmentioning

confidence: 99%

Trend following deep Q‐Learning strategy for stock trading

Chakole

Kurhekar

2019

Expert Systems

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Computers and algorithms are widely used to help in stock market decision making. A few questions with regards to the profitability of algorithms for stock trading are can computers be trained to beat the markets? Can an algorithm take decisions for optimal profits? And so forth. In this research work, our objective is to answer some of these questions. We propose an algorithm using deep Q‐Reinforcement Learning techniques to make trading decisions. Trading in stock markets involves potential risk because the price is affected by various uncertain events ranging from political influences to economic constraints. Models that trade using predictions may not always be profitable mainly due to the influence of various unknown factors in predicting the future stock price. Trend Following is a trading idea in which, trading decisions, like buying and selling, are taken purely according to the observed market trend. A stock trend can be up, down, or sideways. Trend Following does not predict the stock price but follows the reversals in the trend direction. A trend reversal can be used to trigger a buy or a sell of a certain stock. In this research paper, we describe a deep Q‐Reinforcement Learning agent able to learn the Trend Following trading by getting rewarded for its trading decisions. Our results are based on experiments performed on the actual stock market data of the American and the Indian stock markets. The results indicate that the proposed model outperforms forecasting‐based methods in terms of profitability. We also limit risk by confirming trading actions with the trend before actual trading.

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A Multiagent Approach to $Q$-Learning for Daily Stock Trading

Cited by 96 publications

References 13 publications

Evaluating machine learning classification for financial trading: An empirical approach

Evaluating machine learning classification for financial trading: An empirical approach

Reinforcement Learning in Financial Markets

Trend following deep Q‐Learning strategy for stock trading

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