Can Quantitative Finance Benefit from IoT?

Peng Zhang

Stony Brook University

Stony Brook, NY 11794, USA

peng.zhang@stonybrook.edu

Xiang Shi

Advanced Risk and Portfolio

Management

New York, NY 10023, USA

xiang.shi@arpm.co

Samee U. Khan

North Dakota State University

Fargo, ND 58108, USA

samee.khan@ndsu.edu

ABSTRACT

The Internet of Things (IoT) is a novel paradigm that

communicates information among smart devices that are

connected to the Internet. In this context, such devices would

leverage our understanding and capabilities of big data, deep

analysis and artificial intelligence to solve problems in

real-time. The IoT paradigm has successfully benefited many

applications in the social sciences and industries. However, in

the rise of IoT, there is at least one question that has been left

unanswered: Can Quantitative Finance (QF) benefit from IoT?

The QF is a field that extends sophisticated mathematical

models and utilizes advanced computer techniques to link with

global finance markets. By taking market and social

information as input, a QF model can derive profitable insights

and control the risk to make trading decisions. Today, many

Internet-based techniques are extensively employed in the field

as: (a) market and social data is provided via Internet; (b) big

data infrastructures are built in the Cloud; and (c) deep learning

tools are accessible in Internet. Even trading models and

strategies could be exerted through Internet. In this paper, we

will provide an overview of challenges and opportunities

presented by this new paradigm in the QF industry. To unlock

the potential of IoT, a system architecture, termed QuantCloud,

is proposed for modern quantitative trading firms in the field.

CCS CONCEPTS

• Software and its engineering → Software organization

and properties → Software system structures

KEYWORDS

Internet of Things, Quantitative Finance, Big Data, Cloud

Computing

ACM Reference format:

P. Zhang, X. Shi, Samee U. Khan. 2017. In Proceedings of Second

ACM/IEEE Symposium on Edge Computing: Workshop on Smart

IoT (SmartIoT’17), San Jose / Silicon Valley, CA, USA, October

14, 2017, 6 pages.

https://doi.org/10.1145/3132479.3132491

1 INTRODUCTION

The Internet of Things, or IoT for short, is an entirely new paradigm that motivates a renewed thinking in many fields, such as retail, healthcare, cyber and physical infrastructures [1]. With the advances in communication technologies: (a) more scattered information could be effectively integrated in a consolidated big data management system; (b) knowledge-based decision could be made more accurately based on the consolidated information; and (c) tools for modeling and integrating variety and large volumes of metadata could be more rapidly deliverable from vendors to customers through the Internet. These IoT benefits are drawing attention of the social, sciences, and industries [1, 2].

Quantitative finance (QF) plays a key role in many fields of the modern financial markets in stocks, bonds, and foreign exchange. The QF is a field that relies on sophisticated mathematical models, statistical tools, machine learning, and computer techniques to derive profitable insights to control portfolio risks of the rapid-changing markets and make trading decisions [3, 4]. In the field, proprietary trading firms were the pioneers in the use of high-frequency quantitative trading, which accounts for more than half of US equity volumes and about 45% of futures trading, according to Tabb Group estimates. In the past, the firms primarily focused on the speed between the exchanges. However, today, only being fast is insufficient to make profits. One evidence is that US high frequency trading (HFT) equity market marker revenue decreased from more than $7B in 2009 to $1B in 2016. There is a growing trend of firms doing big and deep data analysis to improve their trading decisions. In general, we require the following: (a) consolidating vast amounts of data of different instruments from different sources at different locations; (b) developing mathematical models and statistical tools that are able to deep mine “big values”; (c) building hardware platforms to grab market inefficiency in a timely manner; and (d) deliver data, software, and hardware services as an integrated solution.

The QF evolution from ultra-low-latency systems to “smart trading” systems could be an opportunity for the rise of IoT in revolutionizing the trading industry. This motivated us to

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investigate the connectivity between IoT and QF. Consequently, in this paper, we present the potential benefits for QF from utilizing IoT. We also propose a novel Internet-based system architecture, termed QuantCloud that may leverage the missed opportunities and challenges in this field.

The main contributions of this work are as follows:

1. Discussion on the benefit the QF may gain from IoT and the undergoing evolution of quantitative trading from “fast trading” to “smart trading”.

2. The needs for Internet-based technologies in QF are presented in detail and discussed in essential aspects such as data, methods, platforms and services, etc.

3. A QuantCloud system architecture is proposed as a next-generation quantitative finance platform that is able to leverage “fast and smart trading” for this field.

This work makes an attempt to extend this IoT paradigm and gain IoT benefits in an industrial environment. There is a strong connectivity and business need for practitioners who can develop an innovative IoT solution so that the firms could quickly benefit from the IoT insights in the future. Our work is a step forward towards this industry-driven approach.

The remainder of the article is organized as follows. In Section 2, some necessary background on modern QF systems is reviewed, which also serves as a motivation for the work. To fulfill the needs discussed in Section 2, Section 3 presents an IoT QF system, termed the QuantCloud. We discuss some preliminary QuantCloud results in Section 4. Finally, we conclude the work in Section 5.

2 BACKGROUND AND MOTIVATIONS

Things, within the IoT refers to a wide variety of aspects, which in the context of QF could be a mixture of data, methods, platforms, and services.

2.1 Financial Big Data

Data is the most important “thing” in quantitative analytics. To date, financial big data is the major challenge for financial institutions [5-7]. The 3 Vs of Big Data: volume, velocity, and variety, never stop to grow [5, 6].

First, data volume in market transactions is increasing at a tremendous rate. For example, there was a tenfold increase in market data between 2008 and 2011, and the data volumes are growing stronger in all areas of the financial domain [5]. The New York Stock Exchange (NYSE) by itself creates several terabytes of market and reference data per day covering the use and exchange of financial instruments [5, 8]. For a bigger picture, the total number of transactions increased by 50 times, compared to 20 years ago, and this number being more than 120 times bigger during the financial crisis [6].

Second, data velocity is an important factor in preserving a competitive advantage. High-speed market data are directly delivered to the high frequency trading (HFT) firms through low-latency networks. These HFT transactions are highly sensitive to small price fluctuations even at the microsecond level. It has been recorded that these HFT transactions can deal with several thousands of orders per day [5].

Third, modern financial firms focus on “wide data”, not just big data, in their strategies. The unstructured data from social media, such as news, Twitter, and Facebook are needed to be modeled to gain insights about risk analysis and trading predictions [5, 7]. Consequently, it is no longer possible for a traditional relational database management system (RDBMS) to handle such heterogeneous data [9, 10].

Under this scenario, a proprietary database management system is the first “must-have” component that needs to be optimized for handling time-series queries. In the best practice, the columnar database is viewed as the most preferred option for financial big data applications [10]. Consequently, we also use the columnar database approach in our big data infrastructure.

2.2 CEP and AI Methods

Big data is not just volume, velocity, and variety but a better analytics method is what decision-makers really need. The financial services industry is a pioneer in utilizing the complex event processing (CEP) technology [11] to organize data-driven events so that it could inform algorithmic trading behavior by timely identifying opportunities and/or risks. Nowadays, the CEP technology is extensively utilized in most financial applications, such as quantitative trading, signal generations, and risk management. Such CEP-based approach is also a popular IoT solution to process multiple streams of data/events to identify patterns of interest [11, 12].

The financial firms also are utilizing artificial intelligent (AI) as another novel fast-developing approach. For example, it is reported that the traditional hedge funds, such as Renaissance Technologies and Bridgewater Associates have heavily invested in AI to generate investment ideas [13]. Coincidentally, AI is also a fast-developing tool within the world of the IoT [2, 14]. However, most conservative investors, though eager on the idea of AI, are still slow to adopt this emerging technology.

In practice, the CEP technology and AI algorithms must be integrated with the time-series databases. An ideal case is when the time-series databases are data providers for historical and real-time data; meanwhile, the CEP and AI methods are data consumers to derive hidden opportunities and assess portfolio risks [3, 6, 11]. This is a cornerstone feature in our proposed system, described in Section 3.

2.3 Cloud Computing Platform

The IoT and Cloud are different paradigms but we consider them complimentary. The IoT generates vast amounts of data, and the Cloud provides a scalable platform to store and process the data. For example, the popular cloud IoT platforms include Amazon Web Services IoT [15], Google Cloud IoT [16], Microsoft Azure IoT [17], etc. Similarly, quantitative analytics in finance is also moving the computing tasks to the Cloud. For example, popular cloud platforms of such classification include Amazon Web Services for Financial Services [18], Google Cloud for Financial Services Solutions [19], Microsoft Azure for Financial Services [20], etc. Consequently, both the IoT and QF are using the Cloud as a platform [21]. Naturally, we also utilize Cloud as a platform of our proposed system, described in Section 3.

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2.4 Internet-based Services

By moving to the Cloud, the financial services sector is currently undergoing a significant change. Traditionally, the enterprise-level integrated trading systems was affordable by only by the top financial institutions. Today, this situation has changed. The Internet-based services enabled an explosion in the availability of integrated trading platforms for smaller firms and even professional individual traders. For example, QuantStart offers Internet-based educational resources for learning algorithmic trading [22]. Quantopian is a platform that supports coding of investment algorithms [23] and QuantConnect is yet another example [24]. Among all of these platforms/tools, the Collective2 provides a rich set of multi-source data, such as stocks, forex, futures, and options [25]. We predict that more powerful tools and expressive services will be implemented and delivered via the Internet, shaping the financial services model infused with collaborative technologies [6, 26, 27]. The same ideological change is happening within the world of the IoT [28]. Consequently, we adopt the same Internet-based service model in our proposed architecture, described in Section 3.

2.5 Summary

Observing industrial cases across various applications helps us understand the real needs in industries. The overview of quantitative analytics in finance is outlined in Fig 1.

Figure 1: Overview of Quantitative Analytics in Finance

3 A QUANTCLOUD SYSTEM ARCHITECTURE

After in-depth analysis of motivational factors in QF, we present an integrated system architecture, termed QuantCloud, to leverage the capabilities of financial big data, time-series analytics techniques, parallel processing, and Internet-based services while preserving legacy interfaces, such as Python.

3.1 System Architecture

The QuantCloud system architecture is composed of three abstraction layers, namely: user, client, and server, as shown in Fig 2. The user layer provides an Internet portal through which users submit their tasks in XML and receive their results in CSV. The portal supports quantitative analysts to program their algorithms in C/C++ or Python. Specifically, a task could be a strategy the analyst builds, such as market data types, a trade strategy and frequency, and the user account and exchange information. Such a design will minimize hardware

and device-to-cloud communication requirements for the end-point Internet-connected devices. It also is possible to access results by mobile devices, such as smartphones.

The client layer is at the heart of quantitative analytics. Briefly, it consists of the following modules:

a. Data push and fetch services: It queries time-series data from its connected server (fetch); and pushes results to a user on completion of the user tasks (push).

b. Shared memory system (SHM): It buffers queried time-series and allows other modules to make use of the data.

c. Complex event processing (CEP): On arrival of a user task, it analyzes the dependencies between tasks and data. It then sends queries to server and starts to execute the tasks as long as queried data arrive at SHM.

d. Artificial intelligence (AI): It is a built-in function module that is callable by tasks in CEP. When a function call is made, an AI subroutine reads associated data from SHM and starts analyzing on the read data.

e. Accelerators (ACC): These are additional computational units for host processors. An accelerator appears as a device on the bus for better performance. In general, some specific operators, such as large matrix operations could be accelerated on the Nvidia GPU [29, 30]; and some specific complex models, such as machine learning models could be improved in speed and accuracy on the Google TPU.

The server layer is at the heart of quantitative data and is comprised of:

a. Database (DB): Ideally it adopts a non-relational columnar data storage. It needs to be optimized for time-series queries. In short, time-series is data that has a timestamp, such as IoT device data and QF stocks transactions. Further, a real-time data collection interface will be added to collect market information streams in real-time through the Internet.

b. Hybrid storage solution: It combines in-memory and on-disk storage. Particularly, an in-memory database (IMDB) is just a part of the DB in memory for most frequently accessed data, such as stocks trades. An on-disk database that may consist of a SSD and a HDD, stores the rest of data, such as stocks quotes.

c. Data push and fetch services: It pushes queried data to the requester client (push); and fetches data in real-time from sources, such as financial markets and exchanges (fetch).

3.2 Key Components and Their Functions

3.2.1 Big Data Management

Within the server, data is managed in a non-relational columnar storage. In support of the QF use cases, we considered the following factors: (a) fast range queries for time series; (b) support simultaneous read operations; (c) data compression; and (d) data hashing for security [27].

At the client side, data is stored and managed within the SHM, and a client adopts a hybrid multi-threading programming model. On arrival of packets from server, data packets are decrypted and restored as time series for other subroutines to use [27].

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3.2.2 Complex Event Processing (CEP)

A task could be viewed as a data-driven decision-making process and is comprised of multiple data streams and data-dependent subtasks (i.e. events). Technically, a CEP solution needs to, at least, analyze data dependencies between events and process events on the availability of the needed data. In a practical perspective, users could simply define the events and their behaviors but do not need to worry about the execution ordering, which is a model-driven development. In QF, an event instance could be just a simple event, such as a quotation or a composite event, derived from other discrete events. In financial index models, an event could also be an index of interest using some regression approaches.

Given all this, a data-driven paradigm is a solution to understand the data dependencies between complex events for creation of a data-dependent matrix [31]. Taking this matrix as an input, a scheduler executes an event when its dependency is ready. This approach allows concurrent execution of multiple events to enable event-level parallel processing.

3.2.3 Nvidia GPU and Google TPU Accelerators

Today, GPU is one of most popular accelerators, which has exhibited its superiority over CPU in some specific algorithms,

such as large-scale matrix operations and Monte Carlo (MC). The MC is extensively used to calculate portfolio risk for the simple reason that it does not require closed-form expressions. However, accuracy of estimated risks in MC is dependent on the number of generated scenarios. Therefore, in practice, a considerable number of scenarios are calculated. In this case, the GPU is a preferable computing means to solve such a problem.

Another example is the cone programming problem in modern portfolio theory [32, 33]. The algorithms include the linear programming (LP), quadratic programming (QP) and semidefinite programming (SDP). In contrast to CPU, GPU is more powerful in solving these algorithms. Therefore, compute intensive methods, such as MC, LP, QP, and SDP must move to GPUs from traditional CPUs.

Google TPU is a novel accelerator and is purpose-built specifically for machine learning (ML). However, its access method is now limited to the cloud. To harness the TPU benefit, the training workloads and the execution of the ML models must also be exported to the Google Cloud. This usage is different from the usage of GPU that allows to be used locally.

Figure 2: A QuantCloud System Architecture

3.3 Software Environments

Among popular programming languages for the QF and IoT developments, Python is a preferred language for building solutions as it requires fewer lines of codes and it has a wide availability of statistical libraries. On the other hand, C++ is still the first-choice language for programmers who code at the lowest layer of the software. In theory, there is not much difference between these high-level languages for writing desktop apps and servers. However, in practice, there are big differences in writing codes for the next-generation Internet-based “things”. For example, most computing resources are remote and all of the communication goes through the network. Ideally, a user should not be concerned

with any of this and should simply implement the algorithm as objects.

Keeping all of this in mind, in our software environment, C++ is used for developing the big data system architecture [27] and its built-in CEP scheduler [34]. At the high-level user environment, in addition to these convention C++ callbacks, a Python interface is supplied to execute the Python codes in multi-threaded C++ runtime. This integrated software architecture is important as it provides an effortless interface to use many Python libraries, such as Theano and Pylearn2 for machine learning [35, 36], and StatsModels for statistical tests and data exploration [37].

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3.4 Hardware/Software Co-Development for QF

In development, we collaborate the software specification with the hardware properties and prejudice neither hardware nor software implementation. This co-design method has been extensively-used for powering the IoT development [38] so it is hereby applied for this QF development.

In the off-the-shelf processors, a manycore architecture is the most popular, containing a number of independent cores and shared memory. Technically, a program must be written for a degree of parallel processing so it may fully explore the power of a manycore processor system. Our platform uses a hybrid multi-threading and multi-coprocessing approach.

A manycore processor usually has just a few cores (e.g. 4, 8, 16) and may be complemented by an accelerator, such as Nvidia GPU in a heterogenous system. Each GPU device has its own memory. Communication between host CPU and its attached GPUs goes through the host memory. Strictly, GPU is also a form of manycore architecture but more suitable for highly-parallel compute-intensive applications.

Google Tensor Processing Unit, called as Cloud TPU, may be considered as another form of novel accelerator, only being suitable for specific purpose: machine learning (ML). The TPU

is available now as part of Google Cloud and programmable in TensorFlow. Currently, a ML application or object has to be moving to the cloud for using this TPU. This is changing the hardware acquisition, which simultaneously requires a change in the software development. Under this scenario, the client part in Fig. 2 is designed as an Internet-based analytics provider, rather than mere a standalone instance. Consequently, QF can benefit from a transform towards an Internet-based architecture paradigm.

4 PRELIMINARY RESULTS

We build a proof-of-concept (POC) system to demonstrate the benefit for QF from IoT. This POC system is shown in Fig. 3. In this POC, we simulated a conventional user who operates a personal desktop to perform analysis locally and an Internet-based user who uses Internet-based services to perform same analysis on the Cloud. For this conventional user, we use the Matlab toolbox in a Microsoft Windows operating system. For this Internet-based user, we use a laptop to submit tasks to one of clients in the Cloud using the TCP/IP. On receipt of a task, the client queries data from server and does the task. In this, the big data system infrastructure followed the work [27].

Figure 3: A Proof-of-Concept System for the QuantCloud Architecture

We tested the autoregressive moving-average (ARMA) model using this POC system. In this test, we assume that the conventional user operates a local computer that has a relatively old CPU: Intel Xeon E5 processor; on the other hand, the Internet-based user accesses a cloud compute instance that has a novel CPU: Intel Xeon Phi (Knights Landing) processor. Both users run the ARMA code in Python in StatsModels [37] and use a total of 7-day trade data. It took the conventional user 78 seconds to process 16 stocks and the Internet-based user 18 seconds to process 64 stocks. In other words, in an hour, a conventional user can only process a total of 46 stocks on his local server but an Internet-based use could process as many as 195 stocks using one single cloud instance. The NYSE exchange trades stocks for some 2800 companies. So, an Internet-based user needs only 15 compute instances to complete such analysis on all stocks in an hour. To compare, this conventional user needs about 2.5 days for this job. Therefore, at the IoT age, such conventional users may quickly lose their competitive advantage in the industry. Some example “things” in our model could be:

1. Real-time collection of a socio-temporal event: People use mobile devices, such as smartphones to comment on social affairs. For example, people “like” or “dislike” a company’s news. These events are collected through smartphones and transmitted to a cloud and organized as socio-temporal events. Analyzing such events may help us understand the preference of customers on a company and its products in a timely fashion. Therefore, in this manner, such mobile devices are tangible “things” for the financial cloud to understand the social timely impact on the financial market.

2. Place an order using smart devices: Individual traders can use their smartphone to place an order, for example, buy or sell stocks. Such individual orders are transmitted through a network to an exchange broker where orders are placed and executed. Therefore, in this manner, these smart phones are tangible “things” for the financial cloud to help its customers to place orders in an agile way.

3. Extract live news from a website: For example, get live news about a company or a sector, from markets.wsj.com and

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www.nytimes.com. Search some keywords and use AI models to predict how these live news data impact a company’s stocks. Therefore, in this manner, such news websites are intangible “things” but important for the financial cloud to reflect the public opinions on the companies.

4. Extract a company financial data: These data are collected to a cloud center and organized as time-series events used to understand a company’s financial situation and help price its stocks. Therefore, in this manner, the company websites are intangible “things” but reliable sensors for the financial cloud to demonstrate a company’s performance in a timely manner.

5 CONCLUSIONS

In this work, we see a great potential in leveraging the IoT paradigm for the quantitative trading firms to transform business practices. By extending this IoT paradigm, we could be able to collect multi-source data through the Internet, utilize Internet-based toolchains to gain deep insights from the collected data, minimize the resource provisioning costs by using the Cloud, and create end-to-end integrated solutions in a timely manner. The benefit that this IoT paradigm could bring would change the best practice of most quantitative trading firms. Therefore, the rise of IoT is an opportunity to revolutionize the financial industry as it is better aligned to the needs of modern financial practitioners. To harness this opportunity, the QuantCloud system architecture is one solution with the clear focus on the capabilities of financial big data, complex event processing, artificial intelligence, and Cloud portability.

ACKNOWLEDGMENTS

Samee U. Khan’s work supported by (while serving at) the National Science Foundation. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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