Traders must make informed trading decisions as quickly as possible in order to stay competitive and wrong decisions can be costly. Every second matters when incoming basket trading requests (IOI’s) are distributed to a number of competing brokers, so the time needed to analyze the pre-trade data becomes especially critical.

In this webinar, Panopticon's Peter Simpson and Neil McGovern from Sybase describe the architecture for a simple Equity Basket Pre-Trade Analytics system built using Sybase RAP - The Trading Edition, which incorporates the Sybase IQ column-oriented database, combined with Panopticon’s EX visual data analytics platform. Viewers will get a good overview of how they can use these tools throughout the trading cycle — from back testing with pre-trade analytics through testing and post-trade reporting and risk monitoring.

This system allows traders to:

• Identify the estimated trade time for each basket constituent and the basket as a whole.
• Identify the risk associated with each basket constituent and the basket as a whole.
• Identify the return associated with each basket constituent and the basket as a whole.
• Show the impact of trading limits.

The system supports the efficient storage of vast historical data sets of tick data and trading data. Sybase RAP - The Trading Edition system allows users to retrieve that information and perform calculations very quickly — much faster than with any traditional relational database. The Panopticon data visualizations display basket performance criteria at the basket and constituent level grouped by industry and/or region. Users can drill down to access additional details and screen across baskets to identify problem positions, find correlations between basket constituents, examine expected trade progressions compared to historic trading patterns, and see how expected trade patterns change given different market conditions and trading periods.

The demonstration portion of the webinar covers:

• Arrival of new baskets at set times.
• Generation of basket risk and return data.
• Visual drill down into baskets to identify performance outliers.
• Visual drill down into basket constituents to review historic trading patterns across different market conditions.

Peter and Neil also explain how the demonstration system was constructed and the implementation options available to trading firms.