Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning

Price movement prediction has always been one of the traders' concerns in financial market trading. In order to increase their profit, they can analyze the historical data and predict the price movement. The large size of the data and complex relations between them lead us to use algorithmic trading and artificial intelligence. This paper aims to offer an approach using Twin-Delayed DDPG (TD3) and the daily close price in order to achieve a trading strategy in the stock and cryptocurrency markets. Unlike previous studies using a discrete action space reinforcement learning algorithm, the TD3 is continuous, offering both position and the number of trading shares. Both the stock (Amazon) and cryptocurrency (Bitcoin) markets are addressed in this research to evaluate the performance of the proposed algorithm. The achieved strategy using the TD3 is compared with some algorithms using technical analysis, reinforcement learning, stochastic, and deterministic strategies through two standard metrics, Return and Sharpe ratio. The results indicate that employing both position and the number of trading shares can improve the performance of a trading system based on the mentioned metrics

The Rise of Computerized High Frequency Trading: Use and Controversy

Over the last decade, there has been a dramatic shift in how securities are traded in the capital markets. Utilizing supercomputers and complex algorithms that pick up on breaking news, company/stock/economic information and price and volume movements, many institutions now make trades in a matter of microseconds, through a practice known as high frequency trading. Today, high frequency traders have virtually phased out the dinosaur floor-traders and average investors of the past. With the recent attempted robbery of one of these high frequency trading platforms from Goldman Sachs this past summer, this rise of the machines has become front page news, generating vast controversy and discourse over this largely secretive and ultra-lucrative practice. Because of this phenomenon, those of us on Main Street are faced with a variety of questions: What exactly is high frequency trading? How does it work? How long has this been going on for? Should it be banned or curtailed? What is the end-game, and how will this shape the future of securities trading and its regulation? This iBrief explores the answers to these questions

Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov 18-21, 201

A New Kind of Finance

Finance has benefited from the Wolfram's NKS approach but it can and will benefit even more in the future, and the gains from the influence may actually be concentrated among practitioners who unintentionally employ those principles as a group.Comment: 13 pages; Forthcoming in "Irreducibility and Computational Equivalence: 10 Years After Wolfram's A New Kind of Science," Hector Zenil, ed., Springer Verlag, 201

Dodd-Frank and the Spoofing Prohibition in Commodities Markets

The Dodd-Frank Act amended the Commodity Exchange Act and adopted an explicit prohibition regarding activity commonly known as spoofing in commodities markets. This Note argues that the spoofing prohibition is a necessary step towards improved market discipline and price integrity in the relevant commodities markets. It fills an important gap in the CEA in relation to an elusive form of price manipulation activity by providing an explicit statutory authority on which regulators and market operators may rely in policing suspect trading strategies falling under the spoofing umbrella. Congress’ explicit denouncement of spoofing as an illegal act has ramifications not only for traders, but also for brokers and market makers. In the past, when courts have considered the issue of secondary liability of brokers regarding manipulative activity of their customers in the context of wash sales, they have determined the CEA’s explicit prohibition of wash sales and the relatively easier identification of wash sales activity as important factors that may potentially increase the secondary liability risk of derivatives brokers. Applying the same analogy to spoofing, greater public awareness and the increasing visibility of spoofing activity (resulting from improvements in the monitoring systems of regulators and market operators) will provide strong incentives for market participants to adapt to changing norms. However, areas of concern, such as risk of selective enforcement and inconsistencies among the applicable market rules, will pose challenges in the spoofing prohibition’s implementation. Therefore, regulators must seek cooperation with relevant market operators to encourage structural reform and self-regulatory measures, such as implementation of appropriate structural safeguards into the trading infrastructure