Steven Boyd at Stanford and his students / colleagues are probably the richest seam of up to date portfolio optimization wisdom. If you are using python you shoult probably be using CVXPY to build your portfolio. He has lots of good papers, e.g. see .
Of course you also need an "edge", that information about the future, and that's the jealously guarded part...
1. Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading (Published on 2020-01-07) - This paper discusses the use of Long-Short Term Memory (LSTM) networks in quantitative trading to minimize risk and maximize return based on historical performance. It highlights the benefits of quantitative trading, such as lower commissions, anonymity, control, discipline, transparency, access, competition, and reduced transaction costs.
2. A Markov-Switching VSTOXX Trading Algorithm for Enhancing EUR Stock Portfolio Performance (Published on 2021-05-02) - This paper presents a Markov-switching trading algorithm that uses the VSTOXX index to enhance the performance of a EUR stock portfolio. The algorithm is based on the mean-variance portfolio selection, which aims to maximize the Sharpe ratio.
3. Price discovery in the cryptocurrency option market: A univariate GARCH approach (Published on 2020-08-31) - This paper applies two different GARCH processes to Bitcoin and CRIX, showing that the GARCH(1,1) option pricing model provides realistic price discovery within the bid-ask prices suggested by the market.
4. The Capital Asset Pricing Model (Published on 2021-09-03) - This paper discusses the evolution of the Capital Asset Pricing Model (CAPM) and its connection to behavioral accounts of evolutionary asset pricing, segmented markets, multifractality, and the fractal market hypothesis. It highlights the importance of considering heterogeneity among investors and the implications for the efficient market hypothesis.
Without the above papers you cannot invest while claiming doing anything else than playing at a casino. But it's clearly not sufficient to design a profitable quantitative strategy in 2023.
This list was compiled in 2009 before I took a full time job in an algorithmic trading company, but it's still relevant :) If anything ML is more relevant than ever in trading, except perhaps Deep Neural Nets, Transformers, Large Language Models etc are the norm today.
With the growing popularity of passive strategy among institutional and retail investors, will EMH break down and create opportunities for active strategy again? As I understand it, active strategy is a borderline fools' errand on the timeline of ten or more years. But if everyone just buys the S&P, surely that means fewer eyeballs on price discovery and more pricing inefficiencies, no?
Snark aside, very decent bibliography for the intended audience: independent traders who are building automated trading programs for their personal accounts.
My personal experience is that you don't need to fully understand the Black Scholes Pricing model in order to trade profitable options.
As an example consider the public income trades, such as NetZero, Boxcar, M3, Theta Engine. Trading those doesn't require you to understand how Implied Volatility.
One can argue, however, that selling options and hedging them isn't the 'Quant way' of profiting from options.
He has written a book and published several articles on financial machine learning, including what to look for,how to avoid overfitting, in detail, and done so far better than I've seen elsewhere.