This thesis explores the problem of optimizing trading strategies for complex portfolio transitions. Institutional investors face costs during periodic portfolio rebalancing or transitions between asset managers, including trading costs and opportunity costs. The thesis proposes a methodology to measure opportunity costs and a strategy to minimize them through optimal portfolio transition execution. The benefits of the proposed trading strategy are benchmarked against industry standards. The research focuses on minimizing opportunity costs, which are the largest share of total trading costs. The thesis introduces a portfolio trading optimization (PTO) method based on minimizing portfolio trading tracking error. The tracking error is defined as a function of the current and target portfolios and proposed trades. The PTO method calculates the optimal trade size by finding where the marginal tracking error derivatives are equal. The study uses real-world data from a $1B portfolio and random portfolio samples to evaluate the PTO method. The results show that the PTO method significantly outperforms the baseline method in reducing tracking error. The PTO method also performs better than the modified-PTO method, which uses sector returns instead of individual equity returns. The PTO method is computationally efficient and can be applied to portfolios with up to 1,000 assets. However, the exponential growth in complexity may limit its use for larger portfolios without significant investment in computational hardware. The thesis suggests a combination of early PTO-optimized trading with a transition to sector-neutral trading for larger portfolios. The research also discusses potential future directions, including optimizing the current model and extending it to incorporate market impact estimation. The study highlights the importance of minimizing tracking error and the challenges of estimating market impact in portfolio transitions. The methodology is flexible and can support real-time trading optimization, pre-optimized trading strategies, and a combination of both.This thesis explores the problem of optimizing trading strategies for complex portfolio transitions. Institutional investors face costs during periodic portfolio rebalancing or transitions between asset managers, including trading costs and opportunity costs. The thesis proposes a methodology to measure opportunity costs and a strategy to minimize them through optimal portfolio transition execution. The benefits of the proposed trading strategy are benchmarked against industry standards. The research focuses on minimizing opportunity costs, which are the largest share of total trading costs. The thesis introduces a portfolio trading optimization (PTO) method based on minimizing portfolio trading tracking error. The tracking error is defined as a function of the current and target portfolios and proposed trades. The PTO method calculates the optimal trade size by finding where the marginal tracking error derivatives are equal. The study uses real-world data from a $1B portfolio and random portfolio samples to evaluate the PTO method. The results show that the PTO method significantly outperforms the baseline method in reducing tracking error. The PTO method also performs better than the modified-PTO method, which uses sector returns instead of individual equity returns. The PTO method is computationally efficient and can be applied to portfolios with up to 1,000 assets. However, the exponential growth in complexity may limit its use for larger portfolios without significant investment in computational hardware. The thesis suggests a combination of early PTO-optimized trading with a transition to sector-neutral trading for larger portfolios. The research also discusses potential future directions, including optimizing the current model and extending it to incorporate market impact estimation. The study highlights the importance of minimizing tracking error and the challenges of estimating market impact in portfolio transitions. The methodology is flexible and can support real-time trading optimization, pre-optimized trading strategies, and a combination of both.