From 24bca6588bf44b6d8621420253953f55ad8bf4a3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Franz=20Kir=C3=A1ly?= Date: Wed, 26 Nov 2025 00:14:22 +0100 Subject: [PATCH] Update README.md --- README.md | 9 --------- 1 file changed, 9 deletions(-) diff --git a/README.md b/README.md index 94ee1e66..3217a0e6 100755 --- a/README.md +++ b/README.md @@ -271,9 +271,6 @@ The covariance matrix encodes not just the volatility of an asset, but also how ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1)) ``` -```result -``` - - Market neutrality: for the `efficient_risk` and `efficient_return` methods, PyPortfolioOpt provides an option to form a market-neutral portfolio (i.e weights sum to zero). This is not possible for the max Sharpe portfolio and the min volatility portfolio because in those cases because they are not invariant with respect to leverage. Market neutrality requires negative weights: ```python @@ -311,9 +308,6 @@ SBUX: 0.0330 ef = EfficientFrontier(mu, S, weight_bounds=(0, 0.1)) ``` -```result -``` - One issue with mean-variance optimization is that it leads to many zero-weights. While these are "optimal" in-sample, there is a large body of research showing that this characteristic leads mean-variance portfolios to underperform out-of-sample. To that end, I have introduced an @@ -439,9 +433,6 @@ PyPortfolioOpt provides a test dataset of daily returns for 20 tickers: ['GOOG', 'AAPL', 'FB', 'BABA', 'AMZN', 'GE', 'AMD', 'WMT', 'BAC', 'GM', 'T', 'UAA', 'SHLD', 'XOM', 'RRC', 'BBY', 'MA', 'PFE', 'JPM', 'SBUX'] ``` -```result -``` - These tickers have been informally selected to meet several criteria: - reasonably liquid