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Mike Blum Chief technical officer Finance industry Goldman Sach Company No presentation No clear outline Assuming audience knows everything about the stock market Storytelling: experience Comparison:present and past, how analytic takes place in financial trading Q&A

- 25 yrs - Engineering company: failure of those components such as increase in vibration

25 yrs ago

- Mathematical model predicts stock market; high frequency trading, identify signals - Data was limited - Only looked at seller and buyer relationship

Present

- driven by quantitative models - Correlation between things globally; current events - Looking for patterns to identify large buyer/seller - Look for trades in chicago; future exchange - Look at global exchange data; stock data in the USA - Processed a large amount of data - Quantitative researcher: machine learning, find patterns and correlations and

integrate data - Software developer: manage to get the trade down from chicago to new york - Working in team - goldman?

Quantitative trading:

- Center: chicago - How to start: internship - Hard:learn the market;so many trades - Taking advantages of technology - Collaboration - Risks

- optimize resources of companies Market making: willing to buy/sell Example: toilet paper; buy from wholesale and put on shelf with a higher value (high frequency trading) Efficiency of market - # of people trading - different reasons - hard to find a pattern - dynamic market - easier to deal with general scenario, difficult with natural disaster Programming languages: Python java, C++ C++ EXECUATION Python: model Trading: c++ java Slang - in house programming language https://news.ycombinator.com/item?id=1581069 PGA - simple instruction