Relative Value Trading Strategy Generation

Having close ties to some very quantitative hedgefunds has driven our development of the Quantlab platform both in terms of specialist financial libraries and also in connectivity and performance. Being able to run long time series of interest rate curve data for multiple markets and optimizing for smooth forward and discount curves, is not an easy task. This has been accomplished by using serialization and hand-cranked compiler optimizations in C++.

Post Lehman Swap Pricing

A comprehensive fixed income library for use in Quantlab with specific attention to interest rate and currency swap pricing has for some time been used by a multi asset market neutral hedgefund. By adding support for multi-curve forward curve creation together with fx market support, currency implied discount curves can be created for markets in three timezones. As the client have an extensive inhouse capacity for IT and Quant development, many functions are called using our C# API. This gives the users the flexibility to choose when to run internal or external libraries, and being secretive with their trading edge.

Tick-Data Storage Made Simple

Having sufficient IT capacity to read, process and store normalized data when dealing with +100 ticks per second usually demands high cost hardware and software. Using the realtime loaders for the History Server will efficiently and at low cost record the financial markets. Having this data stored in an easy to use database of the users choice is essential for analyzing patterns and calibrating algorithmic models. If run together with the Quantlab platform, a seamless integration between data and analytics can be achieved.

Simulated Exchange Trading

Whilst still in beta testing, we will soon unveil an extension server module to the Quantlab platform where our Qlang code has gotten FIX enabled. Using a simulated exchange with FIX interface, algorithmic trading strategies and market maker algorithms can be backtested and validated. The FIX library will enable all Quantlab and ARMS applications to directly go from analysis to execution. This will cut time to market for new trading ideas drastically, by not having to rewrite development and testing code for production environments.