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Kalyan Prasad - Python-Driven Portfolios: Bridging Theory and Practice for Efficient Investments
Discover how Python-driven portfolios can bridge the gap between theory and practice for efficient investments, with a focus on diversification, risk measurement, and optimization techniques.
- Python-driven portfolios can help bridge the gap between theory and practice for efficient investments.
- Diversification is crucial in a portfolio, as it reduces risk and increases expected returns.
- The idea of investing in a diversified portfolio is to spread risk across different assets, so that if one asset performs poorly, the others can help offset the losses.
- The concept of diversification assumes that different assets have low or negative correlation with each other.
- In a portfolio, risk can be measured using standard deviation, and return can be measured using the proportion of the portfolio’s value that is returned.
- The efficient frontier is a line that shows the optimal portfolio composition for a given level of expected return and risk.
- To create an efficient frontier, a portfolio optimizer is used to find the optimal weights for each asset.
- The optimal portfolio is the one that provides the highest expected return for a given level of risk.
- Real-time data can be used to update andrefine the portfolio, allowing for dynamic changes in asset weights and expected returns.
- Python can be used to implement a range of portfolio optimization techniques, including mean-variance optimization and risk parity.
- The Python code used in this talk generates a portfolio with five different assets, and uses the Sharpe ratio to evaluate the performance of the portfolio.
- The code also plots the expected return and risk of the portfolio, as well as the efficient frontier.
- The talk concludes with a demonstration of how to use the Python code to create a portfolio and evaluate its performance.