1. Algorithmic Finance and Tools for Grid Execution (the Swift Grid Scripting/Workflow tool) Tiberiu (Tibi) Stef-Praun

2. Outline l The main idea u Extend your research with Grid computing u Means to do parallel execution l How does it help you u speed up, reuse code, increase input sizes l What do you get u Free infrastructure (lots of machines) u Simple tools to manage large executions u Scaled-up research capabilities u The CI expertise and support l Example u MaxPain u Econ results

3. Others are doing it already o Algorithmic trading o Community investing o Industry produces parallel cores o clusters are everywhere, and are affordable o Grid computing in finance is already happening (see references) o You probably know others better

4. Grid computing and why do you want it Clusters of machines: o Uniform access o data o execution o It has: o security o applications o your own space o Think “Fragmented Computing Resources” Machines Cluster Machines

5. How would you use grid computing o Split up your application into components of durations 5min-2 hours o Distribute parts of your application onto the grid nodes o In the proper order, as determined by the logic and dependencies between the app components: o send the inputs to a node containing the component o invoke the execution on that node o copy back the outputs

6. What do you need? l The Glue u Globus (middleware) l Execution tools l Data transfer tools u Swift (workflow) l Jobs dependencies description language (for your problem) l Execution Engine l The Examples u Simple Financial App u Complex Econ App l US (the Comp. Inst.) u Experience with Finance u Experience with Computing

7. Example: The MaxPain model l Inspired from the trading folklore u Assume all the options sellers could influence stock price to minimize their cost at ITM options expiry u Find the stock price at which this cost is minimum l Extended to address more realistic assumptions u What if all ITM options are sold at any point in time, instead of having them wait for the expiry l Issues: u Data acquisition and processing u Options pricing (binomial, Black-Scholes), implied volatility computation - Computationally Intensive

8. MaxPain on a single machine l DATABASE-DRIVEN u Scripts to collect the data (50 symbols) (bash) u Scripts to process the data (SQL, PLSQL) l Trigger data processing within the DB u All the steps use tables as sources, generate intermediary tables u Results are displayed later u Sequential processing l Issues u Running time: 50 symbols, 8 hours u Fixes to data acquisition, adding more symbols

9. MaxPain in distributed computing l Split into self-sufficient components: u Data acquisition (in batches of ) u Per-symbol computing u Per-procedure execution (Volatility, option pricing, Interpolation) (Currently R with QuantLib) u Grouping for higher level results (arbitrage, portfolio optimizations) l Use Swift to u Catalog and use the remote procedures u Drive the execution u Manage the inputs, outputs and dependencies

10. Managing distributed executions “You provide the problem code, I’ll provide the execution environment” (Run DEMO)

11. Workflow facilities in Swift l General language constructs u Procedures (atomic, composite) u Flow control (loops, conditions) u Data structures (arrays, structs) u Variable manipulation utilities (strcat. etc) l Virtualization support u Applications (declared in catalog files) u Data (mappers) u Just-in-time execution

12. Scaling it up: Moral Hazard l Stage 1 26x26 runs ->2,4 l Stage 2 102x52 runs ->3 l Stage 3 40x40 runs ->5 l Stage 4 40x40 runs ->5 l Stage 5 30x30 runs

13. Conclusions l It helps others u Econ (Robert Townsend) u Molecular Sciences (Bennoit Roux) l Build a set of finance and financial mathematics tools, continuous validation l Can share your tools/work easily l Our initial research goals: u Options Indicators (like MaxPain) u Portfolio optimization u Time Series Analysis l Similar work: u http://proactive.objectweb.org