The AlphaPulse Tactical Model is a quantitative framework designed for analyzing historical performance trends over 21 years using metrics such as CAGR, Sharpe ratios, and Alpha. By examining price movements across a diverse selection of ETFs spanning various market classes and sectors, the model evaluates hypothetical outcomes under different market conditions. Simulations demonstrate how the model has historically managed volatility, drawdowns, and asset selection, emphasizing diversification and balance. Over this historical back-testing period, the model achieved a compounded annual growth rate (CAGR) of 22.05% compared to a benchmark average of 9.93%, offering a data-driven perspective on performance trends within the analyzed context.

AlphaMax Diversified is a quantitative model developed to analyze historical performance metrics such as Compound Annual Growth Rate (CAGR), Sharpe ratio, and Alpha, with a focus on managing volatility and drawdowns with a higher CAGR. Over a back-tested period of more than 21 years, it evaluated price movements across diverse Exchange Traded Funds (ETFs) representing multiple market sectors and classes, leveraging cross-sectional momentum to highlight diversification's role in historical simulations. In these back-tests, AlphaMax demonstrated an annual CAGR of 18.71%, compared to the benchmark’s approximate 8.78%, and a maximum drawdown of -18.51%, compared to the benchmark’s -39.32%, suggesting possible resilience in challenging market conditions.

SmartVol is a quantitative model designed for volatility management, aiming to minimize peak drawdowns and achieve balanced diversification across Exchange Traded Funds (ETFs) spanning various asset classes, subclasses, and sectors. Leveraging cross-sectional momentum and systematic selection, the model analyzes price trends based on historical risk and performance data, incorporating principles of balance and diversification. Over 21 years of historical back-testing, the model demonstrated metrics such as a 13.91% annual compounded growth rate (CAGR) and consistent Sharpe and Alpha ratios, with observed steady results across diverse market conditions, including periods of decline. The historical back-testing indicates potential benefits of a dynamic rebalancing approach and a focus on high-performing assets in the analyzed time frame.

StableUp is a quantitative analytical model focused on long-term performance evaluation through historical data, emphasizing volatility management and minimizing drawdowns. Over 21 years of back-testing, the model analyzed price movements across a curated selection of Exchange Traded Funds (ETFs) from various asset classes and sectors, chosen based on historical risk profiles. Its approach integrates cross-sectional momentum to assess diversification benefits and prioritize historically top-performing assets within a balanced framework. The model's back-testing period, including challenging market conditions, yielded a theoretical annual CAGR of 11.44%, compared to 7.73% for the selected market benchmark, offering possible insights into the potential impact of volatility control and asset diversification on long-term outcomes.

VolatilityShield is a quantitative model designed for long-term analysis of historical data, with a focus on achieving consistent and risk-adjusted returns. Over a back-tested period of 21 years, it analyzed performance metrics such as Compound Annual Growth Rate (CAGR), Alpha, Sharpe ratio, volatility, and drawdowns, emphasizing volatility control and loss minimization. Using cross-sectional momentum, the model evaluated price movements across a curated selection of Exchange Traded Funds (ETFs), including bonds and commodities, chosen for their historical variance and diversification potential. Historical back-testing indicates that the model achieved an average annual CAGR of 6.59%, compared to 5.4% for its benchmark, with a lower maximum drawdown of -6.66% versus -14.7% for the benchmark, suggesting possible focus on historical stability and simulated performance during diverse market conditions.

The SereneYield Model is a long-term analytical tool that evaluates historical data to prioritize low volatility and consistent returns. Using over 21 years of back-testing, it could demonstrates a methodical approach to risk control, minimizing drawdowns, and maintaining portfolio stability. By analyzing price movements of a curated selection of Exchange Traded Funds (ETFs), predominantly bond-focused and low-volatility equity ETFs, the model identifies assets based on historical variance profiles to construct a well-diversified, balanced portfolio. Historical performance metrics, including a Compound Annual Growth Rate (CAGR) of 5.68%, annual volatility of 4.61%, and a maximum drawdown of -6.40%, illustrate the model's focus on stability compared to a benchmark CAGR of 3.84%, volatility of 7.50%, and maximum drawdown of -13.74%. These metrics could reflect the model's analytical approach to balancing loss and yields over diverse market conditions.