bigdata and algorithms - la algorithmic trading

LA Algorithmic Trading Meetup - Winter 2013

Algorithmic Trading

(In Los Angeles)

What’s Good?

WelcomeTim Shea \ tim@whatsgood.com \ @sheanineseven

Data Scientist

Ad Agency Guy (Razorfish, Universal, TBWA\Chiat\Day)

Founder and CTO of WhatsGood.com

Big interest in convergence of Tech and Finance communities

Elevator PitchDigital Menu Platform for picky eaters on-the-go.

Data-centric POV

Search/Sort/Slice/Dice, Answering “What’s Good Here”?

The “Good” in WhatsGood varies by person.

“Dimensionality”

Hundreds of data points *behind* each menu item.

This data is *hidden* by traditional analog menus.

Dimensionality = Personalization.

Thomas Guide

Thomas Guide :: Google Earth

As

Paper Menu :: What’sGood

Hypothesis

We’re Empiricists

ProblemNoise

80/20 - In any scenario where you’re ordering food (ex. at-home, in-restaurant, etc) 80% of menu info is noise.

Bad In-store. Worse when considering multiple locations.

Paper menus dont help this situation at all.

ResultHuman error.

Leads to:

Frustration - “Ill just get what I usually get”

Alienation - “I’m going out with my meat-eating friends, Ill just bring a granola bar”

Accidents - “The waiter didnt know there was soy sauce in there, and I ended up in

the hospital”

HypothesisBigData + Machine Learning + The Crowd

Will remove these pain points.

And create something truly valuable for people.

Literally improve the way we discover food, permanantly.

What’sGood Algos

ClydeStorm

Components

FoodNet + Vegas8 + Rhombus

ClydeStormMenu Ingestion - Every 2 weeks, reconcile 400,000 Restaurants and 50MM Menu Items (Add/Edit/Delete)

NLP Classifiers - Then, for every dish, we run 8 NLP classifiers to determine (V,G,N,L,P,&Pop)

Data Mapping - Orthoginal datasets that “dont quite fit”

Search - Handles all the modern indexing and retrieval operations consumers are accustomed to.

Vegas8Based on a simple human Intuition:

“Signal Words” helps us make 1 of 3 determinations:

1. Definitely Positive - “Vegan”: All bets are off, obviously vegan.

2. Strongly Negative - “Ribeye Steak”: Pretty damn confident, not vegan.

3. Fuzzy Signal - Not enough info, conflicting info, fuzzy signal.

The Intuition

FoodNetBased loosely on WordNet - Open Source Princeton project

Lexical Knowledge Graph or word relations (vs a list)

ex. Obviously “MILK” is a signal for “Contains Lactose”

But so are all of its other permutations:

- Synonyms

- Hyper- & Hypo-nyms

- Other languages

- All the foods in the world that commonly use MILK as an ingredient

First Attempt

First VersionRead from Menu DB - 50MM Venue, Dish Title & Description

Read from Synonym DB - Slam it into a big RegEx

For Each record - Any matches?

Save Results

Results?Medoicre

- Took forever to run

- Unexpected results (think: RegEx)

- Tons of edge cases

Algorithms and NLP

Stepping BackHow do we find better tools for the job?

How do we measure any improvements we make?

Is there a more “Algorithmic” approach?

Such as Machine Learning in general, or NLP specifically?

Not #NLP*Not* Nuero Linguistic Programming

What is NLP?Natural Language Processing

Attempt to formalize the ways in which humans understand language, into a computer program.

Slippery - We’re not accustomed to thinking about how we understand each other, we just do it.

Widely ApplicableSemantic Analysis - Whats the overall mood here?

Text Classification - What is this document I’m reading?

Knowledge Mapping - Which things relate to which?

Info Extraction - What are the major topics discussed?

What’sGood Use Cases

1. Similarity

Are these the same?

A Frame, 12565 W Washington Blvd, Culver City, CA, 90066

A-Frame, 12565 Washington Blvd, Los Angeles, CA, 90066

This Problem

Creme Brulee

vs

Crème brûlée

vs

Cr�e Br\u001lee

This Problem

OrthogonalityRhombus - The What’sGood Decoder Ring

Library that attempts to resolve “Matching Problems”

For Example: Public Calorie Database - Can I even use it?

TextGrounderDisambiguate:

- Georgia vs Georgia

Context:

- Melrose Heights vs West Hollywood vs Los Angeles

2. Sentiment

“Bag of Words”Type of Naive Bayes Classifer

Tokenize

Remove Stop Words

Stemming the remaining words

Frequency Distribution - How many times did this occur?

Edge CasesYelp Review - Comme Ca

“You’d expect a place with such a diverse selection of french food, wonderfully accomodating staff, and a world class chef to live up to its amazing reputation, but it just simply did not.”

Other Great TricksPart of Speech Tagging

N-Grams

Levenshtein Distance

RevMiner

Humans!!National Weather Service

Tries to quantify the effect of humans:

- Precipitation forecasts - 25% lift

- Temperature forecasts - 10% lift

Traders

Need human judgement when a model is failing.

3. Relevancy

Popularity Algorithm“Social Triangulation”

(A * (# star ratings)

+

B * (# of dish mentions/total reviews at restaurant)

+

C * (# of photos/avg mentions per restaurant in specific geography)

) * Arbitrary population weight

Search WeightsWhich signals are more important:

Number of times your search query matched something?

Your previous searches & behaviors?

Does Proximity to you outweigh other factors?

Does Popularity?

Infrastructure

StackRunning on Windows

Web/REST Tier in the Cloud

Dedicated RDMS on Solid State Drives

C# & SQL Server

Python & NLTK

Solr Lucene

Results“Vegas8 - RegEx 1”

Raw RegEx, RackSpace Cloud, Shared CPU

5 classifiers

~1 record/sec

50MM Records = 50MM Seconds

14,000 Hours

~578 Days

ResultsResults Sec/Record Total Sec Total Hours Total DaysRegEx 1 1 50,000,000 13,888.89 578.70

Tokenize 1 25 2,000,000 555.56 23.15

Tokenize 2 (SSD & dedicated CPU) 110 454,545 126.26 5.26

Tokenize 3 with 50MM caching 16 3,125,000 868.06 36.17

Tokenize 4 with 10K caching 225 222,222 61.73 2.57

Token/Stem/Stop 230 217,391 60.39 2.52

Token/Stem/Stop w/ 4 parallel pro-cesses

874 57,208 15.89 0.66

Levenstein/Weights/Biz Rules/Ha-doop

?? ?? ?? ??

Improvements?Serialization - eats ~40-60% of overhead. How do we remove it?

Dedicated Hardware - SSDs & Dedicated CPU

Parallelization - Hadoop? RightScale? Custom Solution?

Indexing - SQL “dumb” storage. Solr for search.

ExpertsPanel of Resident Nutritionists

Formalizing things like:

“What is Hangover Food“

“How to get Huge fast”

“How to be a really annoying Yogi”

Final Thoughts

Trading ParallelsDynamic vs Static Systems

Knowledge/Signal Graph

If you’re monitoring “Apple” youll need to monitor:

- Apple, $APPL, Tim Cooke, iPhone, FOXCONN

- And assign a signal weight and signal vector for each

Orthogonality

Using loosely correlative systems

Data ScienceBurgeoning skill set:

Data

Programmer

Sys admin

Full stack knowledge

Stats

Probability

Algorithms

Empirical methodology

Business

“Real world” knowledge

Subjectivity

Modeling uncertainty

ResourcesData Science Toolkit

NLTK

Nate Silver - The Signal and the Noise

Tim Shea

@sheanineseven

tim@whatsgood.com