The AlphaPulse Tactical Model is a quantitative framework designed to analyze long-term performance trends using historical data. It evaluates hypothetical results through metrics such as CAGR, Sharpe ratios, and Alpha. The model aims to assess historical volatility patterns and drawdown management over a 21-year period by analyzing price movements within a diverse selection of ETFs (Exchange Traded Funds). These ETFs span multiple market classes, subclasses, and sectors. The model emphasizes identifying top-performing assets from the analyzed dataset while incorporating diversification for a balanced framework. Based on historical back-testing over 21 years, the model's simulations indicate potential performance outcomes under varied market conditions, including challenging environments and bear markets. During these simulations, the model achieved an annual compounded growth rate (CAGR) of 22.34%, compared to approximately 9.74% for the benchmark, providing an analytical perspective on relative performance within the specific historical context.

AlphaMax Diversified is a carefully designed long-term quantitative model that aims to analyze potential performance across key metrics such as Compound Annual Growth Rate (CAGR), Sharpe ratio, and Alpha. Over a back-tested period of more than 21 years, the model attempts to manage volatility and minimize drawdowns, potentially providing insights into returns relative to volatility. By leveraging cross-sectional momentum, AlphaMax evaluates price movements across a diverse range of Exchange Traded Funds (ETFs), spanning various market classes, subclasses, and sectors. This approach highlights the importance of diversification, focusing on assets that may have historically performed well in simulations. In historical back-testing, AlphaMax may have demonstrated performance that exceeded benchmarks during challenging market conditions, including bear markets. The back-tested results indicate an annual compound growth rate (CAGR) of 19.06%, compared to the benchmark’s approximate 8.78% CAGR over the same period and The maximum drawdown is approximately -18.51%, contrasting the benchmark’s -39.32% during the 21 years backtest.

StableUp is a long-term quantitative model designed to analyze historical performance with a focus on consistent returns. The model attempts to emphasize volatility control and the minimization of maximum drawdowns over more than 21 years of back-testing. StableUp applies cross-sectional momentum to evaluate price movements across a curated selection of Exchange Traded Funds (ETFs) from multiple classes, subclasses, and sectors. Assets included in the model's analysis are selected based on historical risk profiles, aiming to highlight diversification and the potential benefits of prioritizing historically top-performing assets within a balanced framework. Back-tested over 21 years, StableUp may have demonstrated consistent performance in theoretical simulations, including during challenging market conditions and historical bear markets. In these back-testing scenarios, the model achieved an annual CAGR of 11.69%, compared to approximately 7.73% for the benchmark over the same period. These results suggest that the model’s analytical framework may provide insights into the potential benefits of volatility control and diversification. StableUp’s focus on analyzing historically top-performing assets and attempting to manage volatility makes it a tool for exploring long-term growth across a diverse range of asset classes.

VolatilityShield is a long-term quantitative model developed to analyze historical data with the attempt of delivering consistent and acceptable returns. The model incorporates a focus on historical performance metrics such as Compound Annual Growth Rate (CAGR), Alpha ratios, a high Sharpe ratio, and historically low levels of volatility and drawdowns. By emphasizing volatility control and drawdown minimization over a back-tested period spanning more than 21 years, the model may have shown rewards relative to its historical performance metrics. Using cross-sectional momentum, VolatilityShield analyzes price movements across a curated selection of Exchange Traded Funds (ETFs), including bonds and commodities. These assets are chosen based on their historical variance profiles, with an emphasis on low-volatility and historically well-performing options, aiming to highlight diversification and balance within the analyzed portfolio. Extensive back-testing suggests that VolatilityShield may have demonstrated consistent performance even during challenging market environments and historical bear markets. Over the back-tested period of 21 years, the model achieved an average annual CAGR of 6.63%, compared to approximately 5.4% for its benchmark. Additionally, the model demonstrated lower historical volatility and drawdowns, with a maximum drawdown of -6.66% compared to -14.7% for the benchmark, illustrating its potential focus on stability and risk control in simulated scenarios.

The SereneYield Model is a long-term analytical tool designed to evaluate historical data with a focus on low volatility and steady returns. Through extensive back-testing over 21 years, the model demonstrates a consistent approach to minimizing drawdowns and maintaining stability. By analyzing price movements within a curated selection of Exchange Traded Funds (ETFs), primarily bond-focused and low-volatility equity ETFs, the model identifies assets based on their historical variance profiles. This approach highlights a well-diversified and balanced portfolio, emphasizing historically low volatility and steady performance metrics such as Compound Annual Growth Rate (CAGR), Sharpe ratio, and Alpha ratio. The model's back-tested results show an average annual growth rate of 5.71%, with annual volatility of 4.56% and a maximum drawdown of -6.40%, compared to a benchmark CAGR of 3.84%, volatility of 7.50%, and maximum drawdown of -13.74%. These results reflect the model's historical focus and attempt on risk control and consistent returns under varying market conditions.