Showing papers on "Algorithmic trading published in 2021"

Quantitative Trading: How to Build Your Own Algorithmic Trading Business

Abstract: Preface. Acknowledgments. Chapter 1: The Whats, Whos, and Whys of Quantitative Trading. Who Can Become A Quantitative Trader? The Business Case for Quantitative Trading. Scalability. Demand on Time. The Nonnecessity of Marketing. The Way Forward. Chapter 2: Fishing for Ideas. How to Identify a Strategy That Suits You. Your Working Hours. Your Programming Skills. Your Trading Capital. Your Goal. A Taste for Plausible Strategies and Their Pitfalls. How Does It Compare with a Benchmark and How Consistent Are Its Returns? How Deep and Long is the Drawdown? How Will Transaction Costs Affect the Strategy? Does the Data Suffer from Survivorship Bias? How Did the Performance of the Strategy Change Over the Years? Does the Strategy Suffer from Data-Snooping Bias? Does the Strategy "Fly under the Radar" of Institutional Money Managers? Summary. Chapter 3: Backtesting. Common Backtesting Platforms. Excel. MATLAB. TradeStation. High-End Backtesting Platforms. Finding and Using Historical Databases. Are the Data Split- and Dividend-Adjusted? Are the Data Survivorship Bias Free? Does Your Strategy Use High and Low Data? Performance Measurement. Common Backtesting Pitfalls to Avoid. Look-Ahead Bias. Data-Snooping Bias. Transaction Costs. Strategy Refinement. Summary. Chapter 4: Setting up Your Business. Business Structure: Retail or Proprietary? Choosing a Brokerage or Proprietary Trading Firm. Physical Infrastructure. Summary. Chapter 5: Execution Systems. What an Automated Trading System Can Do for You. Building a Semiautomated Trading System. Building a Fully Automated Trading System. Minimizing Transaction Costs. Testing Your System by Paper Trading. Why Does Actual Performance Diverge from Expectations? Summary. Chapter 6: Money and Risk Management. Optimal Capital Allocation and Leverage. Risk Management. Psychological Preparedness. Summary. Appendix: A Simple Derivation of the Kelly Formula when Return Distribution is Gaussian. Chapter 7: Special Topics in Quantitative Trading. Mean-Reverting versus Momentum Strategies. Regime Switching. Stationarity and Cointegration. Factor Models. What Is Your Exit Strategy? Seasonal Trading Strategies. High-Frequency Trading Strategies. Is it Better to Have a High-Leverage versus a High-Beta Portfolio? Summary. Chapter 8: Conclusion: Can Independent Traders Succeed? Next Steps. Appendix: A Quick Survey of MATLAB. Bibliography. About the Author. Index.

An application of deep reinforcement learning to algorithmic trading

Abstract: This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in the stock market It proposes a novel DRL trading policy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets Denominated the Trading Deep Q-Network algorithm (TDQN), this new DRL approach is inspired from the popular DQN algorithm and significantly adapted to the specific algorithmic trading problem at hand The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data In order to objectively assess the performance of trading strategies, the research paper also proposes a novel, more rigorous performance assessment methodology Following this new performance assessment approach, promising results are reported for the TDQN algorithm

A Q-learning agent for automated trading in equity stock markets

Abstract: Trading strategies play a vital role in Algorithmic trading, a computer program that takes and executes automated trading decisions in the stock market. The conventional wisdom is that the same trading strategy is not profitable for all stocks all the time. The selection of a trading strategy for the stock at a particular time instant is the major research problem in the stock market trading. An optimal dynamic trading strategy generated from the current pattern of the stock price trend can attempt to solve this problem. Reinforcement Learning can find this optimal dynamic trading strategy by interacting with the actual stock market as its environment. The representation of the state of the environment is crucial for performance. We have proposed two different ways to represent the discrete states of the environment. In this work, we trained the trading agent using the Q-learning algorithm of Reinforcement Learning to find optimal dynamic trading strategies. We experimented with the two proposed models on real stock market data from the Indian and American stock markets. The proposed models outperformed the Buy-and-Hold and Decision-Tree based trading strategy in terms of profitability.

Rise of the machines? Intraday high-frequency trading patterns of cryptocurrencies

Abstract: This research analyses high-frequency data of the cryptocurrency market in regards to intraday trading patterns related to algorithmic trading and its impact on the European cryptocurrency market. ...

Exploring the Scale-Free Nature of Stock Markets: Hyperbolic Graph Learning for Algorithmic Trading

Abstract: Quantitative trading and investment decision making are intricate financial tasks in the ever-increasing sixty trillion dollars global stock market. Despite advances in stock forecasting, a limitation of most existing neural methods is that they treat stocks independent of each other, ignoring the valuable rich signals between related stocks’ movements. Motivated by financial literature that shows stock markets and inter-stock correlations show scale-free network characteristics, we leverage domain knowledge on the Web to model inter-stock relations as a graph in four major global stock markets and formulate stock selection as a scale-free graph-based learning to rank problem. To capture the scale-free spatial and temporal dependencies in stock prices, we propose HyperStockGAT: Hyperbolic Stock Graph Attention Network, the first model on the Riemannian Manifolds for stock selection. Our work’s key novelty is the proposal of modeling the complex, scale-free nature of inter-stock relations through temporal hyperbolic graph learning on Riemannian manifolds that can represent the spatial correlations between stocks more accurately. Through extensive experiments on long-term real-world data spanning over six years on four of the world’s biggest markets: NASDAQ, NYSE, TSE, and China exchanges, we show that HyperStockGAT significantly outperforms state-of-the-art stock forecasting methods in terms of profitability by over 12%, and risk-adjusted Sharpe Ratio by over 4%. We analyze HyperStockGAT’s components’ contributions through a series of exploratory and ablative experiments to demonstrate its practical applicability to real-world trading. Furthermore, we propose a novel hyperbolic architecture that can be applied across various spatiotemporal problems on the Web’s commonly occurring scale-free networks.

Analysis of news sentiments using natural language processing and deep learning

Abstract: This paper investigates if and to what point it is possible to trade on news sentiment and if deep learning (DL), given the current hype on the topic, would be a good tool to do so. DL is built explicitly for dealing with significant amounts of data and performing complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking to improve their trading process. Moreover, DL and specifically LSTM seem a good pick from a linguistic perspective too, given its ability to “remember” previous words in a sentence. After having explained how DL models are built, we will use this tool for forecasting the market sentiment using news headlines. The prediction is based on the Dow Jones industrial average by analyzing 25 daily news headlines available between 2008 and 2016, which will then be extended up to 2020. The result will be the indicator used for developing an algorithmic trading strategy. The analysis will be performed on two specific cases that will be pursued over five time-steps and the testing will be developed in real-world scenarios.

Two robust long short-term memory frameworks for trading stocks

Abstract: This paper aims to find a superior strategy for the daily trading on a portfolio of stocks for which traditional trading strategies perform poorly due to the low frequency of new information. The experimental work is divided into a set of traditional trading strategies and a set of long short-term memory networks. The networks incorporate general and specific trading patterns, where the former takes into account the universal decision factors for trading across many stocks, while the latter takes into account stock-specific decision factors. Our research shows that both long short-term memory networks, regardless of whether they are based on universal or stock-specific decision factors, significantly outperform traditional trading strategies. Interestingly, however, on average neither has the edge compared to the other, thus remaining ambivalent as to whether universality or specificality is to be preferred when it comes to designing long short-term memory networks for optimal trading.

Ensembling and Dynamic Asset Selection for Risk-Controlled Statistical Arbitrage

Abstract: In recent years, machine learning algorithms have been successfully employed to leverage the potential of identifying hidden patterns of financial market behavior and, consequently, have become a land of opportunities for financial applications such as algorithmic trading. In this paper, we propose a statistical arbitrage trading strategy with two key elements: an ensemble of regression algorithms for asset return prediction, followed by a dynamic asset selection. More specifically, we construct an extremely heterogeneous ensemble ensuring model diversity by using state-of-the-art machine learning algorithms, data diversity by using a feature selection process, and method diversity by using individual models for each asset, as well models that learn cross-sectional across multiple assets. Then, their predictive results are fed into a quality assurance mechanism that prunes assets with poor forecasting performance in the previous periods. We evaluate the approach on historical data of component stocks of the S&P500 index. By performing an in-depth risk-return analysis, we show that this setup outperforms highly competitive trading strategies considered as baselines. Experimentally, we show that the dynamic asset selection enhances overall trading performance both in terms of return and risk. Moreover, the proposed approach proved to yield superior results during both financial turmoil and massive market growth periods, and it showed to have general application for any risk-balanced trading strategy aiming to exploit different asset classes.

Do President Trump's tweets affect financial markets?

Abstract: Frequent tweets of the former president of the United States, Donald Trump, provide a unique opportunity to study how financial markets respond to his statements. To do this, we utilize a precise timestamp of each tweet together with high-frequency financial data. We start by analyzing the impact of tweets in general, irrespective of their content. We find that tweets by President Trump are followed by increased uncertainty, increased trading and a decline in the US stock market. We utilize two methods in order to study whether the market reaction depends on the content of the tweets. First, classification of Trump's tweets depending on whether they contain a specific word reveals that market response is particularly negative for tweets containing the words “products” and “tariff”. Second, we use Latent Dirichlet Allocation to affiliate tweets with distinct topics. We find that while most topics do not impact financial markets, the US stock market responds to tweets related to the topic of a “trade war” by price decline, increased trading volume and increased uncertainty. The “trade war” tweets affect other financial markets too, as the Chinese stock market responds to these tweets negatively, while the price of gold responds positively. We illustrate the practical importance of our approach by an automated trading system, which achieves positive abnormal returns.

Decentralized Finance: Regulating Cryptocurrency Exchanges

Abstract: Global financial markets are in the midst of a transformative movement. The creation of Bitcoin and Facebook’s proposed distribution of Diem mark a watershed moment in the evolution of the financial markets ecosystem. Purportedly, peer-to-peer distributed digital ledger technology eliminates legacy financial market intermediaries such as investment banks, depository banks, exchanges, clearinghouses, and broker-dealers. Yet careful examination reveals that cryptocurrency issuers and the firms that offer secondary market cryptocurrency trading services have not quite lived up to their promise. Notwithstanding cryptoenthusiasts’ calls for disintermediation, evidence reveals that platforms that facilitate cryptocurrency trading frequently employ the long-adopted intermediation practices of their traditional counterparts. In fact, when emerging technologies fail, cryptocoin and token trading platforms partner with and rely on traditional financial services firms. As a result, these platforms face many of the risk-management threats that have plagued conventional financial institutions as well as a host of underexplored threats. Automated or algorithmic trading strategies, accelerated high frequency trading tactics, and sophisticated Ocean’s Eleven-style cyberheists leave crypto investors vulnerable to predatory practices. Early responses to fraud, misconduct, and manipulation emphasize intervention when originators first distribute cryptocurrencies— the initial coin offerings. This Article rejects the dominant regulatory narrative that prioritizes oversight of primary market transactions. Instead, this Article proposes that regulators introduce formal registration obligations for cryptocurrency intermediaries —the exchange platforms that provide a marketplace for secondary market trading. This approach recognizes the dynamic nature of cryptocurrency secondary market actors seeking to achieve disintermediation yet balances the potential benefits of trading intermediaries with normative regulatory goals—protecting investors from fraud, theft, misconduct, and manipulation; enforcing accountability; preserving market integrity; and addressing enterprise and systemic risk management concerns

A synchronous deep reinforcement learning model for automated multi-stock trading

Abstract: Automated trading is one of the research areas that has benefited from the recent success of deep reinforcement learning (DRL) in solving complex decision-making problems. Despite the large number of researches done, casting the stock trading problem in a DRL framework still remains an open research area due to many reasons, including dynamic extraction of financial data features instead of handcrafted features, applying a scalable DRL technique that can benefit from the huge historical trading data available within a reasonable time frame and adopting an efficient trading strategy. In this paper, a novel multi-stock trading model is presented, based on free-model synchronous multi-agent deep reinforcement learning, which is able to interact with the trading market and to capture the financial market dynamics. The model can be executed on a standard personal computer with multiple core CPU or a GPU in a convenient time frame. The superiority of the proposed model is verified on datasets of different characteristics from the American stock market with huge historical trading data.

Dark Pool Trading and Information Acquisition

Abstract: Theory suggests that dark pools may facilitate or discourage information acquisition. We find that more dark pool trading leads to greater information acquisition. We measure information acquisition using stock price dynamics around earnings announcements. To overcome endogeneity concerns, we exploit a large exogenous decrease to dark pool trading that results from the implementation of the Security and Exchange Commission’s (SEC’s) Tick Size Pilot Program. The results cannot be explained by lit venue liquidity, algorithmic trading, or informational efficiency. A battery of additional tests, such as documenting a shift in SEC EDGAR searches, supports the information acquisition interpretation.

Accounting for unadjusted news sentiment for asset pricing

Abstract: A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.,Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.,Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.,The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

Intelligent Algorithmic Trading Strategy Using Reinforcement Learning and Directional Change

Abstract: Designing a profitable trading strategy plays a critical role in algorithmic trading, where the algorithm can manage and execute automated trading decisions. Determining a specific trading rule for trading at a particular time is a critical research problem in financial market trading. However, an intelligent, and a dynamic algorithmic trading driven by the current patterns of a given price time-series may help deal with this issue. Thus, Reinforcement Learning (RL) can achieve optimal dynamic algorithmic trading by considering the price time-series as its environment. A comprehensive representation of the environment states is indeed vital for proposing a dynamic algorithmic trading using RL. Therefore, we propose a representation of the environment states using the Directional Change (DC) event approach with a dynamic DC threshold. We refer to the proposed algorithmic trading approach as the DCRL trading strategy. In addition, the proposed DCRL trading strategy was trained using the Q-learning algorithm to find an optimal trading rule. We evaluated the DCRL trading strategy on real stock market data (S&P500, NASDAQ, and Dow Jones, for five years period from 2015-2020), and the results demonstrate that the DCRL state representation policies obtained more substantial trading returns and improved the Sharpe Ratios in a volatile stock market. In addition, a series of performance analyses demonstrate the robust performance and extensive applicability of the proposed DCRL trading strategy.

Machine Learning and Social Theory: Collective Machine Behaviour in Algorithmic Trading

Abstract: This article examines what the rise in machine learning (ML) systems might mean for social theory. Focusing on financial markets, in which algorithmic securities trading founded on ML-based decisio...

Adversarial Attacks Against Reinforcement Learning-Based Portfolio Management Strategy

Abstract: Many researchers have incorporated deep neural networks (DNNs) with reinforcement learning (RL) in automatic trading systems. However, such methods result in complicated algorithmic trading models with several defects, especially when a DNN model is vulnerable to malicious adversarial samples. Researches have rarely focused on planning for long-term attacks against RL-based trading systems. To neutralize these attacks, researchers must consider generating imperceptible perturbations while simultaneously reducing the number of modified steps. In this research, an adversary is used to attack an RL-based trading agent. First, we propose an extension of the ensemble of the identical independent evaluators (EIIE) method, called enhanced EIIE, in which information on the best bids and asks is incorporated. Enhanced EIIE was demonstrated to produce an authoritative trading agent that yields better portfolio performance relative to that of an EIIE agent. Enhanced EIIE was then applied to the adversarial agent for the agent to learn when and how much to attack (in the form of introducing perturbations).In our experiments, our proposed adversarial attack mechanisms were > 30% more effective at reducing accumulated portfolio value relative to the conventional attack mechanisms of the fast gradient sign method (FSGM) and iterative FSGM, which are currently more commonly researched and adapted to compare and improve.

Machine Learning, Market Manipulation and Collusion on Capital Markets: Why the 'Black Box' matters

Abstract: This paper offers a novel perspective on the implications of increasingly autonomous and “black box” algorithms, within the ramification of algorithmic trading, for the integrity of capital markets. Artificial intelligence (AI) and particularly its subfield of machine learning (ML) methods have gained immense popularity among the great public and achieved tremendous success in many real-life applications by leading to vast efficiency gains. In the financial trading domain, ML can augment human capabilities in both price prediction, dynamic portfolio optimization, and other financial decision-making tasks. However, thanks to constant progress in the ML technology, the prospect of increasingly capable and autonomous agents to delegate operational tasks and even decision-making is now beyond mere imagination, thus opening up the possibility for approximating (truly) autonomous trading agents anytime soon. Given these spectacular developments, this paper argues that such autonomous algorithmic traders may involve significant risks to market integrity, independent from their human experts, thanks to self-learning capabilities offered by state-of-the-art and innovative ML methods. Using the proprietary trading industry as a case study, we explore emerging threats to the application of established market abuse laws in the event of algorithmic market abuse, by taking an interdisciplinary stance between financial regulation, law & economics, and computational finance. Specifically, our analysis focuses on two emerging market abuse risks by autonomous algorithms: market manipulation and “tacit” collusion. We explore their likelihood to arise on global capital markets and evaluate related social harm as forms of market failures. With these new risks in mind, this paper questions the adequacy of existing regulatory frameworks and enforcement mechanisms, as well as current legal rules on the governance of algorithmic trading, to cope with increasingly autonomous and ubiquitous algorithmic trading systems. It shows how the “black box” nature of specific ML-powered algorithmic trading strategies can subvert existing market abuse laws, which are based upon traditional liability concepts and tests (such as “intent” and “causation”). In concluding, by addressing the shortcomings of the present legal framework, we develop a number of guiding principles to assist legal and policy reform in the spirit of promoting and safeguarding market integrity and safety.

FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance

Abstract: Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to obtain an agent that automatically positions to win in the market, namely to decide where to trade, at what price and what quantity, due to the error-prone programming and arduous debugging. In this paper, we present the first open-source framework FinRL as a full pipeline to help quantitative traders overcome the steep learning curve. FinRL is featured with simplicity, applicability and extensibility under the key principles, full-stack framework, customization, reproducibility and hands-on tutoring. Embodied as a three-layer architecture with modular structures, FinRL implements fine-tuned state-of-the-art DRL algorithms and common reward functions, while alleviating the debugging work- loads. Thus, we help users pipeline the strategy design at a high turnover rate. At multiple levels of time granularity, FinRL simu- lates various markets as training environments using historical data and live trading APIs. Being highly extensible, FinRL reserves a set of user-import interfaces and incorporates trading constraints such as market friction, market liquidity and investor’s risk-aversion. Moreover, serving as practitioners’ stepping stones, typical trad- ing tasks are provided as step-by-step tutorials, e.g., stock trading, portfolio allocation, cryptocurrency trading, etc.

Practical Algorithmic Trading Using State Representation Learning and Imitative Reinforcement Learning

Abstract: Algorithmic trading allows investors to avoid emotional and irrational trading decisions and helps them make profits using modern computer technology. In recent years, reinforcement learning has yielded promising results for algorithmic trading. Two prominent challenges in algorithmic trading with reinforcement learning are (1) extracting robust features and (2) learning a profitable trading policy. Another challenge is that it was previously often assumed that both long and short positions are always possible in stock trading; however, taking a short position is risky or sometimes impossible in practice. We propose a practical algorithmic trading method, SIRL-Trader , which achieves good profit using only long positions. SIRL-Trader uses offline/online state representation learning (SRL) and imitative reinforcement learning. In offline SRL, we apply dimensionality reduction and clustering to extract robust features whereas, in online SRL, we co-train a regression model with a reinforcement learning model to provide accurate state information for decision-making. In imitative reinforcement learning, we incorporate a behavior cloning technique with the twin-delayed deep deterministic policy gradient (TD3) algorithm and apply multistep learning and dynamic delay to TD3. The experimental results show that SIRL-Trader yields higher profits and offers superior generalization ability compared with state-of-the-art methods.

Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis.

Abstract: This paper develops a machine learning-driven portfolio optimization framework for virtual bidding in electricity markets considering both risk constraint and price sensitivity. The algorithmic trading strategy is developed from the perspective of a proprietary trading firm to maximize profit. A recurrent neural network-based Locational Marginal Price (LMP) spread forecast model is developed by leveraging the inter-hour dependencies of the market clearing algorithm. The LMP spread sensitivity with respect to net virtual bids is modeled as a monotonic function with the proposed constrained gradient boosting tree. We leverage the proposed algorithmic virtual bid trading strategy to evaluate both the profitability of the virtual bid portfolio and the efficiency of U.S. wholesale electricity markets. The comprehensive empirical analysis on PJM, ISO-NE, and CAISO indicates that the proposed virtual bid portfolio optimization strategy considering the price sensitivity explicitly outperforms the one that neglects the price sensitivity. The Sharpe ratio of virtual bid portfolios for all three electricity markets are much higher than that of the S&P 500 index. It was also shown that the efficiency of CAISO's two-settlement system is lower than that of PJM and ISO-NE.

Deep Reinforcement Learning for Algorithmic Trading

Abstract: We employ reinforcement learning (RL) techniques to devise statistical arbitrage strategies in electronic markets. In particular, double deep Q network learning (DDQN) and a new variant of reinforced deep Markov models (RDMMs) are used to derive the optimal strategies for an agent who trades in a foreign exchange (FX) triplet. An FX triplet consists of three currency pairs where the exchange rate of one pair is redundant because, by no-arbitrage, it is determined by the exchange rates of the other two pairs. We use simulations of a co-integrated model of exchange rates to implement the strategies and show their financial performance.

Low-Latency Hardware Accelerator for Improved Engle-Granger Cointegration in Pairs Trading

Abstract: Pairs trading is a solidly profitable strategy in the algorithmic trading area, and an important step of this strategy is selecting pairs of stocks. Compared with other existing pairs selection approaches, the Engle-Granger cointegration is more stable and reliable. Nowadays, as trading is becoming faster and faster in stock markets all over the world, it is necessary to accelerate the pairs selection process to increase potential profits. However, intensive computations and complicated data flow in the cointegration approach bring challenges to hardware acceleration. In this paper, for the first time, we propose an efficient and hardware-friendly computing scheme to accelerate the Engle-Granger cointegration. Besides, a novel algorithmic strength reduction strategy and approximation methods are used to significantly reduce the complexity of the proposed scheme. Based on the improved algorithm, both FPGA and ASIC accelerators are developed. The implementation results show that our FPGA and ASIC accelerators perform $36\times $ and $207\times $ faster than GPU, respectively. Thus, our design can significantly reduce the latency of the pairs selection process, and make more profit for investors and traders.

Algorithmic Trading using Technical Indicators

Abstract: Financial markets are volatile and dynamic. The uncertainties involved in the market and various economic factors affect the asset price. Predicting trends in asset prices and calculating future value of an asset is a very challenging task. This is responsible for increased use of algorithmic trading amongst traders in financial markets. Algorithmic trading is a method of executing orders using pre-programmed automated trading instructions that consider asset variables including price and volume. Algorithmic trading is widely used in financial firms where large orders are executed and where humans take more time to respond. Algorithmic trading is also called black-box trading, automated trading, or Algo-trading. Algorithmic Trading uses the calculating powers of the computer. News or quotes are not sufficient to trade in financial markets. The challenges in trading can be reduced by proper analysis of data. Technical indicators consider the price and volume data of stock. These technical indicators together can be used to build trading strategies with calculated risks. This paper proposes trading strategies based on quantitative analysis of time series data. These strategies were developed for intraday high-profit trading. The strategy with RSI and MACD technical indicator gives the highest returns up to 12%.

Systemic failures and organizational risk management in algorithmic trading: Normal accidents and high reliability in financial markets.

Abstract: This article examines algorithmic trading and some key failures and risks associated with it, including so-called algorithmic ‘flash crashes’. Drawing on documentary sources, 189 interviews with ma...