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@deanmalmgren @DsAtweet 2014 august nyc algorithmic trading quant skillz beyond wall st deriving value from large, non-financial datasets

quant skillz beyond wall st: deriving value from large, non-financial datasets

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Page 1: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren @DsAtweet

2014 august nyc algorithmic trading

quant skillz beyond wall stderiving value from large, non-financial datasets

Page 2: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data scientists thrive with ambiguitysolve for x

x = 5 + 2

proj

ect e

volu

tion

Page 3: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data scientists thrive with ambiguitysolve for x

x = 5 + 2

proj

ect e

volu

tion

A x = b

Page 4: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data scientists thrive with ambiguitysolve for x

x = 5 + 2

proj

ect e

volu

tion

A x = boptimize A x = b

subject to f(x) > 0

Page 5: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data scientists thrive with ambiguitysolve for x

x = 5 + 2

proj

ect e

volu

tion

A x = b optimize f(x)

optimize A x = b

subject to f(x) > 0

Page 6: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data scientists thrive with ambiguitysolve for x

x = 5 + 2

proj

ect e

volu

tion

A x = b optimize f(x)

optimize A x = b

subject to f(x) > 0

optimize “our profitability”

Page 7: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

origins of ambiguitymany feasible approaches

Page 8: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

origins of ambiguityunclear problems

identify the best locations to plant new trees

Page 9: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

origins of ambiguityunclear problems

@deanmalmgren | bit.ly/design-data

identify the best locations to plant new treeshow many?

what kinds of trees? move old trees?

replace old trees?

Page 10: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

origins of ambiguityunclear problems

identify the best locations to plant new treeshow many?

what kinds of trees? move old trees?

replace old trees?

aesthetically pleasing? maximize growth? increase foliage? offset CO2 emissions?

@deanmalmgren | bit.ly/design-data

Page 11: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

generate hypotheses

build prototype

evaluate feedback

“design process” is used everywhereanticipate failure

1-4 week iterations

Page 12: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

generate hypotheses

build prototype

evaluate feedback

surveys, interviews, focus groups split testing, A/B testing QA; requirements churn

personas, scenarios, use cases business/product requirements story/user cards

build device prototypes minimum viable product write code

human-centered design lean startup agile programming

“design process” is used everywhereanticipate failure

1-4 week iterations

Page 13: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

generate hypotheses

build prototype

evaluate feedback

design and data sciencechallenges in practice

1-4 week iterations

Page 14: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

generate hypotheses

build prototype

evaluate feedback

problem lost in translation

design and data sciencechallenges in practice

1-4 week iterations

Page 15: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

generate hypotheses

build prototype

evaluate feedback

problem lost in translation

takes a long time to collect data, analyze, and build visualization

design and data sciencechallenges in practice

1-4 week iterations

Page 16: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

generate hypotheses

build prototype

evaluate feedback

proof is in the pudding

problem lost in translation

takes a long time to collect data, analyze, and build visualization

design and data sciencechallenges in practice

1-4 week iterations

Page 17: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

how do projects start?

Page 18: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

how do projects start?

Page 19: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

how do projects start?

Page 20: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

how do projects start?

Page 21: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

how do projects start?

Page 22: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

informal conversation to stated goalsmostly bad ideas, but a few good ones

Page 23: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data@deanmalmgren | bit.ly/design-data

mostly bad ideas, but a few good onesinformal conversation to stated goals

Page 24: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data@deanmalmgren | bit.ly/design-data

mostly bad ideas, but a few good ones

Lorem Ipsum: a narrative about blankets.

Author: Charlie Brown

Date: 31 Jan 2012 !Lorem Ipsum is a dummy text used when typesetting or marking up documents. It has a long history starting from the 1500s and is still used in digital millennium for typesetting electronic documents, page designs, etc. !In itself, the original text of Lorem Ipsum might have been taken from an ancient Latin book that was written about 50 BC. Nevertheless, Lorem Ipsum’s words have been changed so they don’t read as a proper text. !Naturally, page designs that are made for text documents must contain some text rather than placeholder dots or something else. However, should they contain proper English words and sentences almost every reader will deliberately try to interpret it eventually, missing the design itself. !However, a placeholder text must have a natural distribution of letters and punctuation or otherwise the markup will look strange and unnatural. That’s what Lorem Ipsum helps to achieve. !I would like to thank Peppermint Patty for her support on studying

Lorem Ipsum as well as the infinite wisdom of Linus van Pelt and his willingness to use his blanket in my experiments.

informal conversation to stated goals

Page 25: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data@deanmalmgren | bit.ly/design-data

mostly bad ideas, but a few good ones

Lorem Ipsum: a narrative about blankets.

Author: Charlie Brown

Date: 31 Jan 2012 !Lorem Ipsum is a dummy text used when typesetting or marking up documents. It has a long history starting from the 1500s and is still used in digital millennium for typesetting electronic documents, page designs, etc. !In itself, the original text of Lorem Ipsum might have been taken from an ancient Latin book that was written about 50 BC. Nevertheless, Lorem Ipsum’s words have been changed so they don’t read as a proper text. !Naturally, page designs that are made for text documents must contain some text rather than placeholder dots or something else. However, should they contain proper English words and sentences almost every reader will deliberately try to interpret it eventually, missing the design itself. !However, a placeholder text must have a natural distribution of letters and punctuation or otherwise the markup will look strange and unnatural. That’s what Lorem Ipsum helps to achieve. !I would like to thank Peppermint Patty for her support on studying

Lorem Ipsum as well as the infinite wisdom of Linus van Pelt and his willingness to use his blanket in my experiments.

informal conversation to stated goals

Page 26: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data@deanmalmgren | bit.ly/design-data

mostly bad ideas, but a few good onesinformal conversation to stated goals

Page 27: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 28: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 29: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 30: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 31: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 32: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 33: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 34: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 35: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 36: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 37: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 38: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 39: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 40: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 41: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

Page 42: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

concept sketch comparisonsqualitative a/b testing

search engine with relevance metrics

demographics human readable expertise summary

Page 43: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

from sketch to blue print to prototypeadd detail to get feedback (while building)

Page 44: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

from sketch to blue print to prototypeadd detail to get feedback (while building)

Page 45: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

from sketch to blue print to prototypeadd detail to get feedback (while building)

Page 46: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

from sketch to blue print to prototypeadd detail to get feedback (while building)

Page 47: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

motoroladata-driven consumer feedback

Page 48: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

motorola

new product announcement

data-driven consumer feedback

Page 49: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

motorola

new product announcement

first versions from manufacturer

data-driven consumer feedback

Page 50: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

motorola

new product announcement

first versions from manufacturer

available in stores

data-driven consumer feedback

Page 51: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

motorola

new product announcement

first versions from manufacturer

available in stores

next generation to manufacturer

data-driven consumer feedback

Page 52: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

motorola

new product announcement

first versions from manufacturer

available in stores

next generation to manufacturer

product defects from consumers

data-driven consumer feedback

Page 53: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

motoroladata-driven consumer feedback

Page 54: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

motoroladata-driven consumer feedback

Page 55: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

motoroladata-driven consumer feedback

Page 56: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

motoroladata-driven consumer feedback

Page 57: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

Page 58: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

Page 59: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

Page 60: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

abou

t pat

ent

not

abou

t pat

ent

Page 61: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

abou

t pat

ent

not

abou

t pat

ent

turn over to plaintiffdon’t

turn over to plaintiff

adverse inference

Page 62: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

abou

t pat

ent

not

abou

t pat

ent

turn over to plaintiffdon’t

turn over to plaintiff

adverse inference

give away trade secrets

Page 63: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

abou

t pat

ent

not

abou

t pat

ent

turn over to plaintiffdon’t

turn over to plaintiff

adverse inference

give away trade secrets

Page 64: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

turn over to plaintiffdon’t

turn over to plaintiff

Page 65: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

Page 66: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

Page 67: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

algorithm design

patents

Page 68: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

algorithm design

patents

fantasy footballlunch

coffee

Page 69: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

algorithm design

patents

marketing

finances

fantasy footballlunch

coffee

Page 70: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

create a “document map”

algorithm design

patents

marketing

finances

fantasy footballlunch

coffee

Page 71: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

create a “document map”

fantasy football

algorithm design

patents

lunch

marketing

finances

coffee

Page 72: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

create a “document map”

fantasy football

algorithm design

patents

lunch

marketing

finances

coffee

Page 73: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

create a “document map”

fantasy football

algorithm design

patents

lunch

marketing

finances

coffee

Page 74: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

create a “document map”

fantasy football

algorithm design

patents

lunch

marketing

finances

coffee

Page 75: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

create a “document map”

fantasy football

algorithm design

patents

lunch

marketing

finances

coffee

Page 76: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

create a “document map”

fantasy football

algorithm design

patents

lunch

marketing

finances

coffee

Page 77: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

create a “document map”

fantasy football

algorithm design

patents

lunch

marketing

finances

coffee

Page 78: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

create a “document map”

fantasy football

algorithm design

patents

lunch

marketing

finances

coffee

review away shades of grey

Page 79: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

create a “document map”

fantasy football

algorithm design

patents

lunch

marketing

finances

coffee

review away shades of grey

reduce reviews by 90-99%

Page 80: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

Page 81: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

awesome!

Page 82: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

who cares?

awesome!

Page 83: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

who cares?

awesome!

<lots of iteration/>

Page 84: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

data-driven e-discoverydaegis

Page 85: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

quant skillz to data science?bit.ly/metis-ds

generate hypotheses

build prototype

evaluate feedback

1-4 week iterations

Page 86: quant skillz beyond wall st: deriving value from large, non-financial datasets

@deanmalmgren | bit.ly/design-data

quant skillz to data science?bit.ly/metis-ds

Page 87: quant skillz beyond wall st: deriving value from large, non-financial datasets

http://bit.ly/design-data http://bit.ly/metis-ds

!@deanmalmgren

[email protected]

solve ambiguous problems with quantitative, iterative approach