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Python for Portfolio Optimization: The Ascent! First working lessons to ascend the hilly terrain of Portfolio Optimization in seven strides (Lessons), beginning with the fundamentals (Lesson 1) and climbing slope after slope (Lessons 2-6), to reach the first peak of constrained portfolio optimization models (Lesson 7)

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Python for Portfolio Optimization: The Ascent!

Python for Portfolio Optimization: The Ascent! First working lessons to ascend the hilly terrain of Portfolio Optimization in seven strides (Lessons), beginning with the fundamentals (Lesson 1) and climbing slope after slope (Lessons 2-6), to reach the first peak of constrained portfolio optimization models (Lesson 7), amongst a range of peaks waiting beyond!


Lesson1: Fundamentals of Risk and Return of a Portfolio (Goal: How does one invest in a portfolio of stocks and know about the returns and risks involved?) Jupyter Notebook: Lesson1_MainContent.ipynb

Lesson2: Some glimpses of Financial Data Wrangling (Goal: Why is it essential to clean and transform raw financial data before they are used to make investment decisions?) Jupyter Notebook: Lesson2_MainContent.ipynb

Lesson3: Heuristic Portfolio Selection (Goal: Given the vast and variegated universe of securities, how can one make a prudent and efficient choice of securities for one's portfolio?) Jupyter Notebook: Lesson3_MainContent.ipynb

Lesson 4: Traditional Methods for Portfolio Construction (Goal: How would an investor know how much to invest in each one of the assets in the portfolio?) Jupyter Notebook: Lesson4_MainContent.ipynb

Lesson 5: Mean-Variance Optimization of Portfolios (Goal: How would one determine the optimal weights which will ensure maximum return and minimum risk for the portfolio that one is invested in?) Jupyter Notebook: Lesson5_MainContent.ipynb

Lesson 6: Sharpe Ratio based Portfolio Optimization (Goal: If a portfolio with higher Sharpe Ratio than its counterparts, is considered superior to them, then how does one invest in the assets of the portfolio, to ensure maximal Sharpe Ratio?) Jupyter Notebook: Lesson6_MainContent.ipynb

Lesson 7: Constrained Portfolio Optimization (Goal: How can an investor know how much to invest in a portfolio of the investor's choice, which besides the objectives of maximizing return and minimizing risk, is constrained by the investor's preference for certain asset classes or assets, or imposition of capital budgets over selective assets in the portfolio?) Jupyter Notebook: Lesson7_MainContent.ipynb

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Python for Portfolio Optimization: The Ascent! First working lessons to ascend the hilly terrain of Portfolio Optimization in seven strides (Lessons), beginning with the fundamentals (Lesson 1) and climbing slope after slope (Lessons 2-6), to reach the first peak of constrained portfolio optimization models (Lesson 7)

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