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When a securities analyst focuses on a corporation’s financial data and statistics, rather than subjective factors such as management experience, product/service mix, employee moral or brand recognition, then that process is known as quantitative analysis, and it is utilized by essentially every investment bank, brokerage and trading firm on Wall Street.
For trading purposes, quantitative analysis involves the use of computer models and algorithms to evaluate potential investments. This typically consists of searching vast arrays and data sets for patterns and correlations to discern trend following or mean reversion opportunities.
BluQuant uses BluFractal as its model for trend following, and BluNeural as its model for mean reversion or pivot points. It also employs a risk analysis algorithm when selecting certain securities to trade. It then puts this all together to select among the largest and most liquid stocks and ETFs for a long-only investment portfolio.
Each of the trades generated by BluQuant typically last for several weeks at a time as it attempts to enter a new trade shortly after its pivot point and then ride that trend to exhaustion. The performance results from this approach are remarkably strong and consistent. Its quantitative methodology has proven to be far superior to conventional or qualitative analysis.