Strategy Overview:

 

  • Objective:  The investment strategy aims to leverage trends within the constituents of a selected ETF, likely the S&P 500, employing a blend of trend analysis, risk assessment, and portfolio optimization techniques.

 

  • Alpha Model (CalculateTrendIndicators): It identifies high-performing securities by assessing their historical performance in relation to moving average, focusing on top 10% of securities.

 

  • Risk Model (CalculateRiskParameters): Estimates expected returns and covariance matrices of selected securities using historical price data to effectively manage risk.

 

  • Portfolio Construction (OptimizePortfolio): Utilizes the Black-Litterman model to optimize portfolio weights based on expected returns and covariance matrices.

 

  • Trade Execution (Execute_Trades): Executes trades to adjust portfolio holdings according to optimized weights, ensuring alignment with the strategy's objectives.

 

  • Rebalancing: Monthly rebalancing is employed, reflecting the strategy's responsiveness to evolving market conditions and trends.

 

Investment Thesis:

 

  • Trend Following: The strategy seeks to capitalize on historical price trends within the chosen ETF's constituents, indicating a belief in the persistence of such trends over time.

 

  • Risk Management: Emphasizes risk management through diversification and optimizing portfolio weights based on risk-adjusted returns, aiming to enhance long-term performance.

 

  • Quantitative Approach: Relies entirely on quantitative models rather than subjective analysis, demonstrating confidence in the effectiveness of mathematical methodologies in generating alpha.

 

  • Monthly Rebalancing: Monthly rebalancing underscores the strategy's systematic approach to portfolio management, adapting to evolving market dynamics over relatively short time frames.

 

  • Leverage: With a leverage set to 2.0, the strategy amplifies potential returns, albeit with increased risk, reflecting a balanced approach to risk and reward.