Quantitative investment strategies have evolved into complex tools with the advent of modern computers, but the strategies’ roots go back over 80 years. They are typically run by highly educated teams and use proprietary models to increase their ability to beat the market. Quant models always work well when back tested, but their actual applications and success rate are debatable. While they seem to work well in bull markets, when markets go haywire, quant strategies are subjected to the same risks as any other strategy.
Quant strategies are now accepted in the investment community and run by mutual funds, hedge funds and institutional investors. They typically go by the name alpha generators, or alpha gens.Just like in “The Wizard of Oz,” someone is behind the curtain driving the process. As with any model, it’s only as good as the human who develops the program. While there is no specific requirement for becoming a quant, most firms running quant models combine the skills of investment analysts, statisticians and the programmers who code the process into the computers. Due to the complex nature of the mathematical and statistical models, it’s common to see credentials like graduate degrees and doctorates in finance, economics, math and engineering.
Quant models always work well when back tested
Markets are complex and ever changing. QuantumStocks A.I. never sleeps and sifts through technicals, fundamentals and more across millions of possible trades every day to find the highest probability opportunities.
Arizet’s QuantumStocks platform constantly back tests quantitative strategies on about 3,500 U.S. stocks, as well as sectors and market cap groups.
For each strategy and stock, the system runs several dozens different back tests, with different position open/close versions, time-frames, etc.
Then, the strategies are rated based on their predictive power, which is based on the historical success rate, average gain, Sharpe Ratio and T-Score.
With this process repeated periodically, QuantumStocks acts as an A.I. powered self-learning system that rates the quantitative strategies, “learns” their predictive power and probability of success in current market conditions, trends, and stock and sector-specific events.
Quant Sentiment Score
On a given day, Quant Sentiment Score for a stock and BUY/SELL recommendation is calculated as a combination of all signals on the stock weighted by their corresponding Strategy Ratings.
The self-learning aspect of the system is that the Score composition changes constantly as the system automatically back tests more than 3 million strategies and “learns” by dynamically changing strategy ratings and their weights in Score calculation.
See more details about QuantumStocks and how it can help you find great trading opportunities here.