algorithmic trading and data science
TRANSCRIPT
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ALGORITHMIC
TRADING AND DATA
SCIENCE HAO NI
OXFORD-MAN INSTITUTE OF QUANTITATIVE FINANCE
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STEREOTYPES OF BANKERS AND
SCIENTISTS
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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.
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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
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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
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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
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Source: http://htxpro.squarespace.com/blog/2014/10/26/the-math-of-pairs-trading-execution-part-i
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2010 FLASH CRASH
Source: TABB group
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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.
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“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.
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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.