Learn how you can enjoy the developer productivity, low TCO, and unlimited scale of MongoDB as a tick database for capturing, analyzing, and taking advantage of opportunities in tick data. This presentation will illustrates how MongoDB can easily and quickly store variable data formats, like top and depth of book, multiple asset classes, and even news and social networking feeds. It will explore aggregating and analyzing tick data in real-time for automated trading or in batch for research and analysis and how auto-sharding enables MongoDB to scale with commodity hardware to satisfy unlimited storage and performance requirements.

1. Sr. Solution Architect, MongoDB
Matt Kalan
How Capital Markets Firms
Use MongoDB as a Tick
Database

2. Agenda
• MongoDB One Slide Overview
• FS Use Cases
• Writing/Capturing Market Data
• Reading/Analyzing Market Data
• Performance, Scalability, & High Availability
• Q&A

3. MongoDB Technical Benefits
Horizontally Scalable
-Sharding
Agile &
Flexible
High
Performance
-Indexes
-RAM
Application
Highly
Available
-Replica Sets
{ name: “John Smith”,
date: “2013-08-01”),
address: “10 3rd St.”,
phone: [
{ home: 1234567890},
{ mobile: 1234568138} ]
}
db.cust.insert({…})
db.cust.find({
name:”John Smith”})

4. Most Common FS Use Cases
1. Tick Data Capture & Analysis
2. Reference Data Management
3. RiskAnalysis & Reporting
4. Trade Repository
5. Portfolio Reporting

5. Writing and Capturing Tick
Data

6. Tick Data Capture & Analysis
Requirements
• Capture real-time market data (multi-asset, top of
book, depth of book, even news)
• Load historical data
• Aggregate data into bars, daily, monthly intervals
• Enable queries & analysis on raw ticks or
aggregates
• Drive backtesting or automated signals

7. Tick Data Capture & Analysis –
Why MongoDB?
• High throughput => can capturereal-timefeeds for all products/assetclasses
needed
• High scalability=> all data and depth for all historical time periods can be
captured
• Flexible & Range-basedindexing => fast querying on time rangesand any
fields
• Aggregation Framework => can shape raw data into aggregates (e.g. ticks to
bars)
• Map-reduce capability(Native MR or Hadoop Connector) => batch analysis
looking for patternsand opportunities
• Easy to use => native language drivers and JSON expressionsthat you can

8. Trades/metrics
High Level Trading Architecture
Feed Handler
Exchanges/Mark
ets/Brokers
Capturing
Application
Low Latency
Applications
Higher Latency
Trading
Applications
Backtesting and
Analysis
Applications
Market Data
Cached Static &
Aggregated Data
News & social
networking
sources
Orders
Orders

9. Trades/metrics
High Level Trading Architecture
Feed Handler
Exchanges/Mark
ets/Brokers
Capturing
Application
Low Latency
Applications
Higher Latency
Trading
Applications
Backtesting and
Analysis
Applications
Market Data
Cached Static &
Aggregated Data
News & social
networking
sources
Orders
Orders
Data Types
• Top of book
• Depth of book
• Multi-asset
• Derivatives (e.g. strips)
• News (text, video)
• Social Networking

10. {
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS",
timestamp: ISODate("2013-02-15 10:00"),
bidPrice: 55.37,
offerPrice: 55.58,
bidQuantity: 500,
offerQuantity: 700
}
> db.ticks.find( {symbol: "DIS",
bidPrice: {$gt: 55.36} } )
Top of Book [e.g. equities]

11. {
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS",
timestamp: ISODate("2013-02-15 10:00"),
bidPrices: [55.37, 55.36, 55.35],
offerPrices: [55.58, 55.59, 55.60],
bidQuantities: [500, 1000, 2000],
offerQuantities: [1000, 2000, 3000]
}
> db.ticks.find( {bidPrices: {$gt: 55.36} } )
Depth of Book

12. {
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS",
timestamp: ISODate("2013-02-15 10:00"),
bids: [
{price: 55.37, amount: 500},
{price: 55.37, amount: 1000},
{price: 55.37, amount: 2000} ],
offers: [
{price: 55.58, amount: 1000},
{price: 55.58, amount: 2000},
{price: 55.59, amount: 3000} ]
}
> db.ticks.find( {"bids.price": {$gt: 55.36} } )
Or However Your App Uses It

13. {
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS",
timestamp: ISODate("2013-02-15 10:00"),
spreadPrice: 0.58
leg1: {symbol: “CLM13, price: 97.34}
leg2: {symbol: “CLK13, price: 96.92}
}
db.ticks.find( { “leg1” : “CLM13” },
{ “leg2” : “CLK13” },
{ “spreadPrice” : {$gt: 0.50 } } )
Synthetic Spreads

14. {
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS",
timestamp: ISODate("2013-02-15 10:00"),
title: “Disney Earnings…”
body: “Walt Disney Company reported…”,
tags: [“earnings”, “media”, “walt disney”]
}
News

15. {
_id : ObjectId("4e2e3f92268cdda473b628f6"),
timestamp: ISODate("2013-02-15 10:00"),
twitterHandle: “jdoe”,
tweet: “Heard @DisneyPictures is releasing…”,
usernamesIncluded: [“DisneyPictures”],
hashTags: [“movierumors”, “disney”]
}
Social Networking

16. {
_id : ObjectId("4e2e3f92268cdda473b628f6"),
symbol : "DIS”,
openTS: Date("2013-02-15 10:00"),
closeTS: Date("2013-02-15 10:05"),
open: 55.36,
high: 55.80,
low: 55.20,
close: 55.70
}
Aggregates (bars, daily, etc)

17. Querying/Analyzing Tick Data

18. Architecture for Querying Data
Higher Latency
Trading
Applications
Backtesting
Applications
• Ticks
• Bars
• Other analysis
Research &
Analysis
Applications

19. // Compound indexes
> db.ticks.ensureIndex({symbol: 1, timestamp:1})
// Index on arrays
>db.ticks.ensureIndex( {bidPrices: -1})
// Index on any depth
> db.ticks.ensureIndex( {“bids.price”: 1} )
// Full text search
> db.ticks.ensureIndex ( {tweet: “text”} )
Index Any Fields: Arrays, Nested,
etc.

20. // Ticks for last month for media companies
> db.ticks.find({
symbol: {$in: ["DIS", “VIA“, “CBS"]},
timestamp: {$gt: new ISODate("2013-01-01")},
timestamp: {$lte: new ISODate("2013-01-31")}})
// Ticks when Disney’s bid breached 55.50 this month
> db.ticks.find({
symbol: "DIS",
bidPrice: {$gt: 55.50},
timestamp: {$gt: new ISODate("2013-02-01")}})
Query for ticks by time; price
threshold

21. Analyzing/Aggregating Options
• Custom application code
– Run your queries, compute your results
• Aggregation framework
– Declarative, pipeline-based approach
• Native Map/Reduce in MongoDB
– Javascript functions distributed across cluster
• Hadoop Connector
– Offline batch processing/computation

22. //Aggregate minute bars for Disney for February
db.ticks.aggregate(
{ $match: {symbol: "DIS”, timestamp: {$gt: new ISODate("2013-02-01")}}},
{ $project: {
year: {$year: "$timestamp"},
month: {$month: "$timestamp"},
day: {$dayOfMonth: "$timestamp"},
hour: {$hour: "$timestamp"},
minute: {$minute: "$timestamp"},
second: {$second: "$timestamp"},
timestamp: 1,
price: 1}},
{ $sort: { timestamp: 1}},
{ $group :
{ _id : {year: "$year", month: "$month", day: "$day", hour: "$hour", minute:
"$minute"},
open: {$first: "$price"},
high: {$max: "$price"},
low: {$min: "$price"},
close: {$last: "$price"} }} )
Aggregate into min bars

23. …
//then count the number of down bars
{ $project: {
downBar: {$lt: [“$close”, “$open”] },
timestamp: 1,
open: 1, high: 1, low: 1, close: 1}},
{ $group: {
_id: “$downBar”,
sum: {$sum: 1}}} })
Add Analysis on the Bars

24. var mapFunction = function () {
emit(this.symbol, this.bidPrice);
}
var reduceFunction = function (symbol, priceList) {
return Array.sum(priceList);
}
> db.ticks.mapReduce(
map, reduceFunction, {out: ”tickSums"})
MapReduce Example: Sum

25. Process Data in Hadoop
• MongoDB’s Hadoop Connector
• Supports Map/Reduce, Streaming, Pig
• MongoDB as input/output storage for Hadoop jobs
– No need to go through HDFS
• Leverage power of Hadoop ecosystem against
operational data in MongoDB

26. Performance, Scalability, and High
Availability

27. Why MongoDB Is Fast and Scalable
Better data locality
Relational MongoDB
In-Memory
Caching
Auto-Sharding
Read/write scaling

28. Auto-sharding for Horizontal Scale
mongod
Read/Write Scalability
Key Range
Symbol: A…Z

29. Auto-sharding for Horizontal Scale
Read/Write Scalability
mongod mongod
Key Range
Symbol: A…J
Key Range
Symbol: K…Z

30. Sharding
mongod mongod
mongod mongod
Read/Write Scalability
Key Range
Symbol: A…F
Key Range
Symbol: G…J
Key Range
Symbol: K…O
Key Range
Symbol: P…Z

31. Primary
Secondar
y
Secondar
y
Primary
Secondar
y
Secondar
y
Primary
Secondar
y
Secondar
y
Primary
Secondar
y
Secondar
y
MongoS MongoS MongoS
Key Range
Symbol: A…F,
Time
Key Range
Symbol: G…J,
Time
Key Range
Symbol: K…O,
Time
Key Range
Symbol: P…Z,
Time
Application

32. Summary
• MongoDB is high performance for tick data
• Scales horizontally automatically by auto-sharding
• Fast, flexible querying, analysis, & aggregation
• Dynamic schema can handle any data types
• MongoDB has all these features with low TCO
• We can support you with anything discussed

33. Questions?

34. Sr. Solution Architect, MongoDB
Matt Kalan
#ConferenceHashtag
Thank You