High Frequency Trading Simulation System
SHIFT is a test-bed that can simulate realistically the behavior of modern high-frequency financial markets and will become a key tool for improving the functioning of these markets, by allowing investors, market makers, managers of exchanges, and regulators to test scenarios and strategies under much more realistic conditions -- prior to implementing those strategies in a live market.
This will reduce the risk of “rogue” behaviors propagating in live markets as new software is turned up (such as the Knight Capital scenario and many other “glitches” seen in a variety of markets in recent years). It will enable regulators and exchange managers to experiment safely with possible rule-changes. It will allow market participants to reduce the uncertainty associated with innovations in trading strategies.
SHIFT is designed to be very versatile. The system will support several application areas, including:
- Teaching microstructure finance and algorithmic trading courses.
- Designing and back testing high frequency trading algorithms.
- Answering research questions about the impact and interaction of HFT algorithms in the market.
- Designing and studying the impact of new regulations.
The system developed is in fact a complete state of the art Agent Based modeling simulator. Therefore, it can answer a variety of questions that are currently being addressed at a very small scale with simulated (read little intelligence) agents.
These questions include but are clearly not limited to:
- Testing new regulatory/rule changes at the exchange level and their effect to the trading ecosystem. The contemplated rules include the minimum life order rule, maximum order flow limits, half second resting time, order to trade ratio constraints, batch auction mechanism and many other rules. We will statistically measure the impact that each rule will have on the market participants as well as on the order fill ratio at the exchange level.
- Testing the market impact caused by the interaction of different HFT algorithms. This may include situations in which HFT traders artificially create new opportunities that other HFT traders take advantage of, but that wouldn’t necessarily exist if HFT traders were not part of the market. Testing market reaction to stress scenarios. To this end we will create rogue algorithms and test measures that may be put in place to detect and shut down such algorithms.
- The connection to the news engine will allow to gather the reaction of algorithms that use Natural Language Processing in trading to shocking words and phrases. We will test the impact of such news to the well-being of the market.
T. Winkler, Z. Ye, I. Florescu. "SHIFT Project: A Tool for Teaching and Research". 7th Annual Stevens Conference on High Frequency Finance and Analytics (2016).
I. Florescu, T. Winkler, Z. Ye. "Algorithmic trading / Machine learning in Finance: Assessing the Algorithms’ Interaction and Impact". The Eastern Conference on Mathematical Finance (2017).
H. Zhao, Z. Zhao, R. Chatterjee, T. Lonon, I. Florescu. “Pricing Variance, Gamma and Corridor Swaps Using Multinomial Trees”. Journal of Derivatives, Vol. 25, No. 2, 2017.
T. Winkler, I. Florescu."SHIFT: A High Frequency Market Simulator". Working Paper.
Z.Ye, I. Florescu. "Data Flow Analysis in U.S. Stock Exchanges". Working Paper.
- USPTO patent number US 62/239,351
- CME Group Foundation support starting from 2015
- Dr. Ionut Florescu, Research Associate Professor, Director of the Hanlon Financial Systems Center (email@example.com)
- Dr. Dragos Bozdog, Teaching Associate Professor, Deputy Director of the Hanlon Financial Systems Center (firstname.lastname@example.org)
- Thiago Winkler, Ph.D. Candidate in Financial Engineering (email@example.com)
- Ziwen Ye, Ph.D. Candidate in Financial Engineering (firstname.lastname@example.org)
- Gaojie Li, Master Student in Computer Science
- Hanrun Li, Master Student in Computer Science
- Weipu Xu, Master Student in Software Engineering
Former Members of the Team:
Jingyu Zeng, Shaoyong Tang, Chen Liu, Zhanyu Tan, Yuan Tian, Yuewei Mao, Xuan Luo, Jian Zhao, Xuming Bing, Shuoyu Mao, Lalita Gajbe, Xiaoshuai Luo, Yang Liu, Zhenjiu Dai, Isaac Cohen, Waris Bantherngpaesach, Xiaojian Zhu, Meng Zhi, Runxi Ding