[DOC] remove empty boxes in README (#683)

PR #676 added empty
boxes in the readme used for testing.

This is confusing for the user, as the empty blocks get displayed as
empty boxes, creating the impression of an error or misrendering.

This needs to be removed, and we should think separately about how the
testing can be achieved nonetheless.

FYI @tschm

Author: fkiraly

README.md

@@ -253,9 +253,6 @@ 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
@@ -293,9 +290,6 @@ 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
@@ -421,9 +415,6 @@ 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