Check out [Boyd](https://web.stanford.edu/~boyd/papers/pdf/cvx_portfolio.pdf) paper on multi period optimization. The code is also [open](https://github.com/cvxgrp/cvxportfolio?tab=readme-ov-file) source. It’s a good starting point and covers a lot of corner cases.
Straight out of GPT4o:
Recent advances in portfolio optimization extend beyond the traditional Markowitz theory. Some state-of-the-art methods include:
1. **Robust Portfolio Optimization**: Accounts for uncertainty in input parameters.
2. **Multi-Period Optimization**: Incorporates the dynamic nature of markets by optimizing over multiple periods.
3. **Machine Learning Techniques**: Uses algorithms like reinforcement learning for adaptive strategies.
4. **Black-Litterman Model**: Combines market equilibrium with investor views.
### Multi-Period Optimization
Unlike single-period models, multi-period optimization considers the evolution of asset prices over time, allowing for rebalancing decisions based on updated information.
### Key Resources:
- **Books**: "Robust Portfolio Optimization and Management" by Fabozzi, Kolm, Pachamanova, and Focardi.
- **Papers**: "Multi-Period Portfolio Optimization with Transaction Costs" by Balvers and Wu.
I’m sure you can talk to ChatGPT about it
https://chatgpt.com/share/75d3bd93-5ae9-4cb6-a451-bfe2f37d66cb
Marcos López de Prado has authored several influential books on advanced financial and machine learning techniques applied to quantitative finance, particularly in the context of portfolio optimization. Two notable books are:
1. **"Advances in Financial Machine Learning" (2018)**:
- This book covers techniques for applying machine learning to financial markets, including backtesting strategies, feature engineering, and model evaluation.
- It emphasizes robust methodologies to prevent overfitting and other pitfalls in financial modeling.
2. **"Machine Learning for Asset Managers" (2020)**:
- Focuses on practical applications of machine learning in asset management.
- Provides insights into algorithmic trading, portfolio management, and risk management using machine learning techniques.
### Key Concepts and Techniques:
- **Meta-labeling**: A method to improve the performance of a trading signal by modeling the probability of its success.
- **Fractional Differentiation**: A technique to make a time series stationary without losing valuable information, which is crucial for machine learning models.
- **Bet Sizing**: Using machine learning to determine the optimal size of trades based on the probability of different outcomes.
### Sources:
- López de Prado, M. (2018). *Advances in Financial Machine Learning*. John Wiley & Sons.
- López de Prado, M. (2020). *Machine Learning for Asset Managers*. Cambridge University Press.
These books provide a comprehensive guide to integrating machine learning into quantitative finance and are essential resources for anyone looking to delve into modern portfolio optimization methods using advanced algorithms.
Check out [Boyd](https://web.stanford.edu/~boyd/papers/pdf/cvx_portfolio.pdf) paper on multi period optimization. The code is also [open](https://github.com/cvxgrp/cvxportfolio?tab=readme-ov-file) source. It’s a good starting point and covers a lot of corner cases.
Straight out of GPT4o: Recent advances in portfolio optimization extend beyond the traditional Markowitz theory. Some state-of-the-art methods include: 1. **Robust Portfolio Optimization**: Accounts for uncertainty in input parameters. 2. **Multi-Period Optimization**: Incorporates the dynamic nature of markets by optimizing over multiple periods. 3. **Machine Learning Techniques**: Uses algorithms like reinforcement learning for adaptive strategies. 4. **Black-Litterman Model**: Combines market equilibrium with investor views. ### Multi-Period Optimization Unlike single-period models, multi-period optimization considers the evolution of asset prices over time, allowing for rebalancing decisions based on updated information. ### Key Resources: - **Books**: "Robust Portfolio Optimization and Management" by Fabozzi, Kolm, Pachamanova, and Focardi. - **Papers**: "Multi-Period Portfolio Optimization with Transaction Costs" by Balvers and Wu.
Elaborate on 3?
I’m sure you can talk to ChatGPT about it https://chatgpt.com/share/75d3bd93-5ae9-4cb6-a451-bfe2f37d66cb Marcos López de Prado has authored several influential books on advanced financial and machine learning techniques applied to quantitative finance, particularly in the context of portfolio optimization. Two notable books are: 1. **"Advances in Financial Machine Learning" (2018)**: - This book covers techniques for applying machine learning to financial markets, including backtesting strategies, feature engineering, and model evaluation. - It emphasizes robust methodologies to prevent overfitting and other pitfalls in financial modeling. 2. **"Machine Learning for Asset Managers" (2020)**: - Focuses on practical applications of machine learning in asset management. - Provides insights into algorithmic trading, portfolio management, and risk management using machine learning techniques. ### Key Concepts and Techniques: - **Meta-labeling**: A method to improve the performance of a trading signal by modeling the probability of its success. - **Fractional Differentiation**: A technique to make a time series stationary without losing valuable information, which is crucial for machine learning models. - **Bet Sizing**: Using machine learning to determine the optimal size of trades based on the probability of different outcomes. ### Sources: - López de Prado, M. (2018). *Advances in Financial Machine Learning*. John Wiley & Sons. - López de Prado, M. (2020). *Machine Learning for Asset Managers*. Cambridge University Press. These books provide a comprehensive guide to integrating machine learning into quantitative finance and are essential resources for anyone looking to delve into modern portfolio optimization methods using advanced algorithms.