TRADING AND DATA SCIENCE HAO NI OXFORD-MAN INSTITUTE OF - PowerPoint PPT Presentation

ALGORITHMIC TRADING AND DATA SCIENCE HAO NI OXFORD-MAN INSTITUTE OF QUANTITATIVE FINANCE

STEREOTYPES OF BANKERS AND SCIENTISTS

THE EVOLUTION OF TRADING VENUE Algorithmic Trading It encompasses trading systems that are heavily reliant on complex mathematical formulas and high-speed, computer programs to determine trading strategies.

Data • Source: Massive financial data streams • Data collection Model • Quantify the real world problem • Propose a robust and effective model to describe the underlying data streams Method • Explore hidden patterns behind massive data streams • Make better prediction for the future market Execution • Place trades automatically

WHY ALGORITHMS HELPS TRADING? • The ability to handle more volume of trades High • High speed execution speed • Explore hidden patterns behind massive data streams Advanced • Make better prediction for the future market Learning techniques • Free of human emotions Decrease • Eliminate manual errors, missed opportunities etc human intervention

EXAMPLE: PAIRS TRADING Source: http://www.nasdaq.com/article/dont-be-fooled-by-the-fancy-name- statistical-arbitrage-is-a-simple-way-to-profit-cm254669

Source: http://htxpro.squarespace.com/blog/2014/10/26/the- math-of-pairs-trading-execution-part-i

2010 FLASH CRASH Source: TABB group

MY RESEARCH: CHANGE-POINT PROBLEM Input – Output Pair (X, Y) : Y ~ f(X) + e • f is random Bayesian • Prior distribution: GP(m, K) framework • Posterior distribution P( f | (Xi, Yi)): updated based on the observations (Xi, Yi). Change-point • K is a region-switching type( [3] ). Application: Detect and Predict the structural change in the correlation of financial time series.

“MODELERS’ HIPPOCRATIC OATH” I will remember that I didn’t make the world and it does not satisfy my equations. I will never sacrifice reality for elegance without explaining why I have done so No will I give the people who use my model false comfort about its accuracy. Instead I will make explicit its assumptions and oversights. I understand that my work may have enormous effects on society and the economy, many of them are beyond my comprehension.

BIBLIOGRAPHY [1] Scott Patterson, The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It; Crown Business, 2011 . [2] Scott Patterson, Dark Pools: The rise of A.I. trading machines and the looming threat to Wall Street; Crown Business, 2013. [3] Garnett, Roman, et al. "Sequential Bayesian prediction in the presence of changepoints and faults." The Computer Journal (2010): bxq003.