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[DOC] remove empty boxes in README #683

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fkiraly merged 1 commit into from
Nov 25, 2025
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[DOC] remove empty boxes in README #683

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9 changes: 0 additions & 9 deletions README.md
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Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down
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