RolzieInvestment

  Happy New Year everyone. Looking back at 2025, the precious metals market was undoubtedly the "star of the year": silver hit a record high at the end of the year, briefly approaching $80 and even touching $84/oz, before experiencing intraday volatility of around 10%; gold also continued its upward trend, reaching a record high of around $4,550/oz on December 26th, and ending the year around $4,300/oz, representing a year-on-year increase of approximately 65%–66%. Looking ahead to 2026, many institutions remain bullish. For example, JPMorgan Chase predicts that gold prices could potentially move towards $5,000/oz in the fourth quarter of 2026. The question is: facing this "trend + high volatility" market, is there an executable strategy to systematically capture these opportunities? This is exactly what my project is researching: comparing Fixed RR, ATR trailing, and the 50/50 scale-out exit framework around XAUUSD/XAGUSD, and then implementing the backtesting find...

 In July this year, I was studying Prof. Dharmadharan's valuation course, and one of the models that is slightly simpler than DCF is multiple linear regression, which has also been confirmed to be true and usable in the Chinese A-share market. This model is simply to use a basket of stocks, such as (bank etf) of various indicators, to find out the linear relationship between the independent variable and multiple dependent variables, for example, suppose the dependent variable is PB, independent variables are PE, ROE, the bank's non-performing loan ratio. This will give us a regression equation. For example PB = a + x* PE + y ROE + z NPL ratio of banks. With this we can substitute PE, ROE, NPL ratio of individual stocks to get a predicted PB, if the predicted PB is lower than the current PB then it means currently undervalued. Below I will combine python code, and Prof. Dharmadharan's course, together with a detailed look at this method. You are also welcome to discuss wit...