Once a strategy has been identified and backtested against historical data, the next step is to put it into action. Executing a quantitative trading strategy requires a great deal of preparation, from utilizing a high-end trading infrastructure to developing a well-tested and highly agile algorithm. Thousand of securities may be scanned and potentially million of trading criteria may be considered every minute. Traders must make sure that the algorithms they build and the systems that run them can hold up against the unpredictability of the market and the large volume of information that needs to be quickly and efficiently processed, among many other challenges.
Unlike the backwards-looking backtesting, execution is forward looking. A high-end execution system, which allows one to trade live, also requires a sophisticated electronic infrastructure in order to send orders at a high rate., An algorithm is often built to behave according to or to take advantage of the nuances, inefficiencies and special characteristics of the targeted markets.
Same algorithms can be deployed into several local and foreign market places. In other words, expansion is not necessarily or exclusively predicated on new ideas and new strategies, but also on deployment of existing ones into foreign markets or into different asset classes locally. Each market is likely to introduce new opportunities. For example, the percentage of retail flow may vary significantly as can the number of local exchanges. Nonetheless, this is a straightforward consideration for increasing scale.
We’ve covered strategy identification, historical backtesting, data analysis, and execution. The next and final component is risk management.