Python Tutorial: Portfolio returns

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-portfolio-analysis-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Let's discuss portfolio weights and returns. The portfolio weight of an asset in your portfolio, is the percentage of the total value invested in that particular asset. The portfolio weights summed together add up to 100%. By setting many relatively small weights, you can diversify your portfolio. The larger the weights to individual stocks, the more exposed you are to fluctuations of that particular stock. The easiest way to calculate a portfolio weight, is to divide the dollar value of a security by the total dollar value of the portfolio, which gives you the percentage. Portfolio weights are key in expressing your investment strategy. I already mentioned the equal weighted and market cap weighted portfolios, which are created by setting the weights a certain way. The portfolio manager's job is to determine the optimal portfolio weights, given certain risk and return constraints, and change those as market conditions change. Portfolio returns are changes in value over time. In this example, you see how a portfolio's value in red changes over the time span of a year. You can also compare it to the line in blue, which is the S&P500 return over that year. Returns are an indication of how well a portfolio performed over time. Returns can be calculated, as stated by this equation by taking the difference in value over a time period, say from time t-1 to t. Divide the change in value over the initial value at t-1, which gives you the return as a percentage change. The historic returns are also used to calculate a portfolio's expected return for the future. Always take into consideration the probability that an asset will achieve its historical return given the current investing environment. Some assets, like bonds, are more l