Many traders use divergence to spot potential reversals, but the process is often subjective. Different traders may draw trendlines differently, leading to inconsistent signals and uncertainty when making trading decisions.
This course introduces a more structured approach by using linear regression to define trends objectively and applying Price-Momentum bullish divergence within a data-tested framework. By combining clear rules with historical research, traders can evaluate setups with greater consistency and confidence.
Identify rare but powerful Price-Momentum divergence setups that can generate 10% to 20% returns
Many traders are introduced to divergence as a reversal signal, but in practice it often becomes confusing and inconsistent. Different traders may draw trendlines in different ways, interpret indicator movements differently, and arrive at completely different conclusions from the same chart. As a result, what appears to be a clear divergence signal for one trader may look like noise to another, making it difficult to rely on the technique with confidence.
This course addresses that problem by introducing a more structured and objective approach. Instead of relying on discretionary chart drawing, you’ll learn how to use linear regression to define the true direction of price movement and compare it with the behavior of the Price-Momentum line. This removes much of the subjectivity involved in traditional divergence analysis and provides a clearer framework for identifying potential bullish divergence setups.
In addition, the course goes beyond visual examples by presenting historical testing across 500 U.S. stocks from 2010 to 2024. By examining how the strategy performs across different holding periods—such as 10, 20, 30, and 40 days—you will see how the timing of exits affects outcomes and where the strategy has shown stronger statistical performance. This combination of structured analysis and historical evidence helps traders evaluate the strategy more realistically, rather than relying on isolated chart examples.
With over 15 years of experience in cross-asset trading, portfolio management and entrepreneurship.
He has been featured by several major media like  Business Times, Yahoo News, TechInAsia, etc., and invited as a panel speaker by various financial related institutions such as Singapore Stock Exchange, Indonesia Stock Exchange, Share Investors, etc., to give various insights on the how Artificial Intelligence and finance will shape the investment landscape.
Today Rein is managing prop trading fund and actively teaching retail investors and professional on systematic and quantitative trading approach. And he loves to share professional knowledge and experience on how to consistently profit from the stock market.
He started his career as a portfolio manager at Citigroup, holds degrees in Information Systems from LSE and AI from NTU, later gained a large following, and managed over $100 million in assets.
Enroll in this short course to learn which approach has historically given traders a better edge.
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