one result per python block

Author: tschm

README.md

@@ -297,6 +297,9 @@ 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:
 
 <!--pytest-codeblocks:cont-->
@@ -338,6 +341,8 @@ 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
@@ -463,10 +468,13 @@ Tests are written in pytest (much more intuitive than `unittest` and the variant
 
 PyPortfolioOpt provides a test dataset of daily returns for 20 tickers:
 
-```
+```python
 ['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

tests/test_discrete_allocation.py

@@ -1,7 +1,7 @@
+from cvxpy.error import SolverError
 import numpy as np
 import pandas as pd
 import pytest
-from cvxpy.error import SolverError
 
 from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
 from tests.utilities_for_tests import get_data, setup_efficient_frontier

tests/test_readme.py

@@ -19,8 +19,12 @@
 
 def test_readme_runs():
     """Execute README code blocks and compare output to documented results."""
-    code_blocks = CODE_BLOCK.findall(README.read_text())
-    result_blocks = RESULT.findall(README.read_text())
+    readme_text = README.read_text(encoding="utf-8")
+    code_blocks = CODE_BLOCK.findall(readme_text)
+    result_blocks = RESULT.findall(readme_text)
+
+    # Optional: keep docs and expectations in sync.
+    assert len(code_blocks) == len(result_blocks), "Mismatch between python and result blocks in README.md"
 
     code = "".join(code_blocks)  # merged code
     expected = "".join(result_blocks)