Learn how leading investment banks are bringing complex financial products to market quickly and effectively with MongoDB, whereas in past rigid relational schema have inhibited time to market. Delegates attending this webinar will see how MongoDB can be used to create complex new products, capture new trades and calculate values and exposures.

1. MongoDB in FS
Position & Trade Management
27th November 2012
Daniel Roberts

2. Relational Database Challenges
Data Types Agile Development
• Unstructured data • Iterative
• Semi-structured • Short development
data cycles
• Polymorphic data • New workloads
Volume of Data New Architectures
• Petabytes of data • Horizontal scaling
• Trillions of records • Commodity
servers
• Tens of millions of
queries per second • Cloud computing
2

3. Why MongoDB in FS?
• Flexible schemas
• Scale Out
• Aggregations
• High Volume Data Feeds
• Agile Development • Tick Data / Time series
• Risk Analytics
• Product Catalogs & Trade Capture
• P&L Reporting
• Reference Data Management
• Portfolio Management
• Quantitative Analysis
• Automated Trading
3

4. Agile development
RDBMS MongoDB
{
_id: ObjectId("4c4ba5e5e8aabf3"),
"NewOrder-Single" : {
"Header" : {
"BeginString" : "FIX.4.2",
"BodyLength" : 190,
"MsgType" : "D",
"HeaderFields" : {
"SenderCompID" : "Client",
"TargetCompID" : "TradingGateway",
"MsgSeqNum" : 4,
"SendingTime" : {
"UTCFormat_2051-100" :
"Fri Jun 01 09:36:26 BST 2012”
}....
4

5. Scalable Out
5

6. Global Replication
Primary
Secondary
Secondary
6

7. MongoDB Solutions

8. Use cases
Order Capture Product Catalogs
Aggregate / calculate data Trade History
Globally replicated reference
Time Series data
data
Aggregation Framework Fast, In-Place Updates
8

9. Product Catalogs
Catalogs of complex financial products
• ‘Exotics’ difficult to model in relational db.
• ‘On-boarding’ new products in hours.
• Representation in RDBMS may require >~50 tables.
• What’s the impact from technology?
• Once created how do we capture the details of a new trade?
Why MongoDB?
• Flexible schema means we don’t need to go back to the database
when we have a product to sell.
• Single collection for all products... even if they vary greatly
• Trades potentially exist for long periods
• Newer trade can have different data with not impact on the db.
9

10. Order Capture
{
_id: ObjectId("4c4ba5.."),
"NewOrder-Single" : {
"Header" : {
• Capture messages "BeginString" : "FIX.4.2",
"BodyLength" : 190,
"MsgType" : "D",
• Variable formats "HeaderFields" : {
"SenderCompID" : "Client",
– Eg Fix 4.2 or 5.0 "TargetCompID" :
"TradingGateway",
"MsgSeqNum" : 4,
"SendingTime" : {
"UTCFormat_2051-100" :
"Fri Jun 01 09:36:26 BST 2012”
}....
10

11. Execution reports
{
"_id" : ObjectId("5051d573036441c9062d6629"),
"ExecutionReport" : {
"StandardMessageHeader" : {
"BeginString" : "FIX.4.2",
"BodyLength" : 265,
"MsgType" : "8”,
},
"ExecutionReportBody" : {
"OrderID" : "OrderID_142201",
"ClOrdID" : "20120909-142159-4597031",
"Instrument" : {"Symbol" : "MSFT” },
"OrderQtyData" : { "OrderQty" : 100000 },
"OrdTYpe" : "1",
"Currency" : "USD",
"LastShares" : 50000,
"LastPx" : 100.5,
"LeavesQty" : 50000,
"CumQty" : 50000,
"AvgPx" : 100.5,
"TransactTime" : {
"UTC" : "Sun Sep 09 14:22:01 BST 2012"
},
…..
}
}
11

12. Aggregation Framework
db.ExecutionReport.aggregate(
{
$match : {"Instrument.Symbol":"MSFT"},
$group : {
_id : "$ExecutionReport.ReportBody.ClOrdID",
reportsPerOrd : { $sum : 1 },
totalNumOrdered : { $sum :
"$ExecutionReport.ReportBody.OrderQtyData.OrderQty"},
avgPrice : { $avg :
"$ExecutionReport.ExecutionReportBody.AvgPx"
}
}
});
12

13. Results
{
"result" : [
{
"_id" : "20120909-142604-3800196",
"reportsPerOrd" : 2,
"totalNumOrdered" : 200000,
"avgPrice" : 100.5
},
{
"_id" : "20120909-142603-7560516",
"reportsPerOrd" : 2,
"totalNumOrdered" : 200000,
"avgPrice" : 100.25
}
…
],
"ok" : 1
}
13

14. Aggregate View of Positions
{ "account" : 123000234, "instrument" : "MSFT", "quantity" : 10000, "ts" :
ISODate("2012-12-25T00:00:00Z"), "buyPrice" : 26.67 }
{ "account" : 123000234, "instrument" : "MSFT", "quantity" : 4534, "ts" :
ISODate("2012-12-27T00:00:00Z"), "buyPrice" : 27.39 }
{ "account" : 123000234, "instrument" : "MSFT", "quantity" : 4534, "ts" :
ISODate("2012-12-20T00:00:00Z"), "buyPrice" : 24.39 }
{ "account" : 123000234, "instrument" : "GOOG", "quantity" : 8768, "ts" :
ISODate("2012-12-25T00:00:00Z"), "buyPrice" : 526.67 }
{ "account" : 123000234, "instrument" : "GOOG", "quantity" : 324, "ts" :
ISODate("2012-12-20T00:00:00Z"), "buyPrice" : 630.59 }
14

15. Calculate P&L – aggregated positions
var currentPrice = 656.54;
db.pl.aggregate([
{$match:{"instrument" : "GOOG"}},
{$project : {instrument:1,
currentValue : { $multiply:["$quantity", currentPrice] },
purchaseValue : { $multiply:["$quantity", "$buyPrice"]}}
},
{$group : {_id: "$instrument",
currentValue : {$sum : "$currentValue"},
purchaseValue: {$sum : "$purchaseValue"}}
},
{$project: {_id: 0, instrument: "$_id",
currentValue: 1,
purchaseValue: 1,
profit : {$subtract : ["$currentValue","$purchaseValue"]}}}
]);
15

16. Results
{
"result" : [
{
"currentValue" : 5969261.68,
"purchaseValue" : 4822153.72,
"instrument" : "GOOG",
"profit" : 1147107.96
}
],
"ok" : 1
}
16

17. Trade History
• Store historical trades and deltas over time
• Maintain all data online.. Eg. 7 years
• Recent data maintained hot in ‘Working Set’
300 GB Working Set
128 GB 128 GB 128 GB
RAM RAM RAM
1 TB 1 TB 1 TB
Data on Data on Data on
Disk Disk Disk
Shard 1 Shard 2 Shard 3
17

18. Summary
• Product Catalogs
• Trade Capture
• Handle changes easily over time
• Aggregate trades / orders / executions
• Calculate P&L across stored positions
• Store trade updates and deltas
18

19. For More Information
Resource User Data Management
Location
MongoDB Downloads www.mongodb.org/download
Free Online Training education.10gen.com
Webinars and Events www.10gen.com/events
White Papers www.10gen.com/white-papers
Customer Case Studies www.10gen.com/customers
Presentations www.10gen.com/presentations
Documentation docs.mongodb.org
Additional Info info@10gen.com
19