ALGORITHMIC

TRADING AND DATA

SCIENCE HAO NI

OXFORD-MAN INSTITUTE OF QUANTITATIVE FINANCE

STEREOTYPES OF BANKERS AND

SCIENTISTS

THE EVOLUTION OF TRADING VENUE

Algorithmic TradingIt 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?

High

speed

•The ability to handle more volume of trades•High speed execution

Advanced Learning

techniques

• Explore hidden patterns behind massive data streams

• Make better prediction for the future market

Decrease human

intervention

• Free of human emotions

• Eliminate manual errors, missed opportunities etc

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

Bayesian framework

• f is random

• Prior distribution: GP(m, K)

• 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.