towards testing README.md

Author: tschm

README.md

@@ -256,20 +256,24 @@ The covariance matrix encodes not just the volatility of an asset, but also how
 - Long/short: by default all of the mean-variance optimization methods in PyPortfolioOpt are long-only, but they can be initialised to allow for short positions by changing the weight bounds:
 
 ```python
-ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
+>>> ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
+
 ```
 
 - 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
-ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
-ef.efficient_return(target_return=0.2, market_neutral=True)
+>>> ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
+>>> ef.efficient_return(target_return=0.2, market_neutral=True)
+OrderedDict({'GOOG': 0.0747287764570896, 'AAPL': 0.0532061998403115, 'FB': 0.0663647763595121, 'BABA': 0.0115771487708806, 'AMZN': 0.051794511454659, 'GE': -0.0594560621731438, 'AMD': -0.0678975317682523, 'WMT': -0.0817205719345985, 'BAC': -0.1413007724407138, 'GM': -0.1402101962690842, 'T': -0.13713261204016, 'UAA': 0.0002656163909862, 'SHLD': -0.0705951831340284, 'XOM': -0.0775452287164678, 'RRC': -0.0510171940919588, 'BBY': 0.0349455362769414, 'MA': 0.375760614087238, 'PFE': 0.1111984245745791, 'JPM': 0.0140774027288155, 'SBUX': 0.0329563456273947})
+
 ```
 
 - Minimum/maximum position size: it may be the case that you want no security to form more than 10% of your portfolio. This is easy to encode:
 
 ```python
-ef = EfficientFrontier(mu, S, weight_bounds=(0, 0.1))
+>>> ef = EfficientFrontier(mu, S, weight_bounds=(0, 0.1))
+
 ```
 
 One issue with mean-variance optimization is that it leads to many zero-weights. While these are
@@ -278,9 +282,12 @@ mean-variance portfolios to underperform out-of-sample. To that end, I have intr
 objective function that can reduce the number of negligible weights for any of the objective functions. Essentially, it adds a penalty (parameterised by `gamma`) on small weights, with a term that looks just like L2 regularisation in machine learning. It may be necessary to try several `gamma` values to achieve the desired number of non-negligible weights. For the test portfolio of 20 securities, `gamma ~ 1` is sufficient
 
 ```python
-ef = EfficientFrontier(mu, S)
-ef.add_objective(objective_functions.L2_reg, gamma=1)
-ef.max_sharpe()
+>>> from pypfopt import objective_functions
+>>> ef = EfficientFrontier(mu, S)
+>>> ef.add_objective(objective_functions.L2_reg, gamma=1)
+>>> ef.max_sharpe()
+OrderedDict({'GOOG': 0.0819942016928946, 'AAPL': 0.0918509031802692, 'FB': 0.1073667333688086, 'BABA': 0.0680482478876387, 'AMZN': 0.1010796289877925, 'GE': 0.0309429468523964, 'AMD': 0.0, 'WMT': 0.0353042020828323, 'BAC': 0.0001739443220274, 'GM': 0.0, 'T': 0.0274224141523135, 'UAA': 0.0182927430888646, 'SHLD': 0.0, 'XOM': 0.0465545659178931, 'RRC': 0.0023903728743853, 'BBY': 0.0644567269626333, 'MA': 0.1426239959760212, 'PFE': 0.0840602539751452, 'JPM': 0.0279123528004041, 'SBUX': 0.0695257658776802})
+
 ```
 
 ### Black-Litterman allocation

tests/test_docs.py

@@ -47,5 +47,5 @@ def readme_path() -> Path:
 
 def test_doc(readme_path):
     """Test the README file with doctest."""
-    result = doctest.testfile(str(readme_path), module_relative=False, verbose=True, optionflags=doctest.ELLIPSIS)
+    result = doctest.testfile(str(readme_path), module_relative=False, verbose=False, optionflags=doctest.ELLIPSIS)
     assert result.failed == 0