Chore/pylint improvements (#100)

Only code quality changes made

.pylintrc

@@ -1,23 +1,24 @@
 [MASTER]
+# Ignore certain files or directories during analysis
+ignore-paths=tests/,docs/,example/
+
+[REPORTS]
 # Set the output format for `pylint` messages (text, colorized, json, etc.)
 output-format=text
 
-# Ignore certain files or directories during analysis
-ignore-patterns=tests/, docs/
-
 [MESSAGES CONTROL]
 # Specify the maximum allowed line length
 max-line-length=120
 
 # Disable some pylint messages that may be too strict or not relevant for your project
 disable=
-    C0103,  # Naming convention violation
-    # C0114,  # Missing module, function, class docstring
+    C0114,  # Missing module docstring
+    C0116,  # Missing function docstring
+    R0903,  # Too few public methods
     R0913,  # Too many arguments
     R0914,  # Too many local variables
-    # R0903,  # Too few public methods
 
 # Include additional pylint messages or message categories
-enable=
-    C0114,  # Missing module, function, class docstring
-    R0903,  # Too few public methods
+#enable=
+#    C0114,  # Missing module, function, class docstring
+#    R0903,  # Too few public methods

finquant/efficient_frontier.py

@@ -12,7 +12,7 @@
 from finquant.quants import annualised_portfolio_quantities
 
 
-class EfficientFrontier(object):
+class EfficientFrontier:
     """An object designed to perform optimisations based on minimising a cost/objective function.
     It can find parameters for portfolios with
 
@@ -126,8 +126,10 @@ def minimum_volatility(self, save_weights=True):
         """
         if not isinstance(save_weights, bool):
             raise ValueError("save_weights is expected to be a boolean.")
+
         args = (self.mean_returns.values, self.cov_matrix.values)
-        # optimisation
+
+        # Optimization
         result = sco.minimize(
             min_fun.portfolio_volatility,
             args=args,
@@ -136,9 +138,11 @@ def minimum_volatility(self, save_weights=True):
             bounds=self.bounds,
             constraints=self.constraints,
         )
-        # if successful, set self.last_optimisation
+
+        # Set the last optimization
         self.last_optimisation = "Minimum Volatility"
-        # set optimal weights
+
+        # Set optimal weights
         if save_weights:
             self.weights = result["x"]
             self.df_weights = self._dataframe_weights(self.weights)
@@ -307,7 +311,7 @@ def efficient_frontier(self, targets=None):
         """
         if targets is not None and not isinstance(targets, (list, np.ndarray)):
             raise ValueError("targets is expected to be a list or numpy.ndarray")
-        elif targets is None:
+        if targets is None:
             # set range of target returns from the individual expected
             # returns of the stocks in the portfolio.
             min_return = self.mean_returns.min() * self.freq
@@ -420,14 +424,14 @@ def properties(self, verbose=False):
         )
         if verbose:
             string = "-" * 70
-            string += "\nOptimised portfolio for {}".format(self.last_optimisation)
-            string += "\n\nTime window/frequency: {}".format(self.freq)
-            string += "\nRisk free rate: {}".format(self.risk_free_rate)
-            string += "\nExpected annual Return: {:.3f}".format(expected_return)
-            string += "\nAnnual Volatility: {:.3f}".format(volatility)
-            string += "\nSharpe Ratio: {:.3f}".format(sharpe)
+            string += f"\nOptimised portfolio for {self.last_optimisation}"
+            string += f"\n\nTime window/frequency: {self.freq}"
+            string += f"\nRisk free rate: {self.risk_free_rate}"
+            string += f"\nExpected annual Return: {expected_return:.3f}"
+            string += f"\nAnnual Volatility: {volatility:.3f}"
+            string += f"\nSharpe Ratio: {sharpe:.3f}"
             string += "\n\nOptimal weights:"
-            string += "\n" + str(self.df_weights.transpose())
+            string += f"\n{str(self.df_weights.transpose())}"
             string += "\n"
             string += "-" * 70
             print(string)

finquant/market.py

@@ -6,7 +6,7 @@
 from finquant.returns import daily_returns, historical_mean_return
 
 
-class Market(object):
+class Market:
     """Object that contains information about a market index.
     To initialise the object, it requires a name and information about
     the index given as ``pandas.Series`` data structure.
@@ -72,11 +72,11 @@ def properties(self):
         """
         # nicely printing out information and quantities of market index
         string = "-" * 50
-        string += "\Market index: {}".format(self.name)
-        string += "\nExpected Return:{:0.3f}".format(self.expected_return)
-        string += "\nVolatility: {:0.3f}".format(self.volatility)
-        string += "\nSkewness: {:0.5f}".format(self.skew)
-        string += "\nKurtosis: {:0.5f}".format(self.kurtosis)
+        string += f"\nMarket index: {self.name}"
+        string += f"\nExpected Return:{self.expected_return:0.3f}"
+        string += f"\nVolatility: {self.volatility:0.3f}"
+        string += f"\nSkewness: {self.skew:0.5f}"
+        string += f"\nKurtosis: {self.kurtosis:0.5f}"
         string += "-" * 50
         print(string)
 

finquant/monte_carlo.py

@@ -11,7 +11,7 @@
 from finquant.quants import annualised_portfolio_quantities
 
 
-class MonteCarlo(object):
+class MonteCarlo:
     """An object to perform a Monte Carlo run/simulation."""
 
     def __init__(self, num_trials=1000):
@@ -32,7 +32,7 @@ def run(self, fun, **kwargs):
          :result: List of quantities returned from `fun` at each iteration.
         """
         result = []
-        for i in range(self.num_trials):
+        for _ in range(self.num_trials):
             res = fun(**kwargs)
             result.append(res)
         return np.asarray(result, dtype=object)
@@ -93,7 +93,7 @@ def __init__(
         self.freq = freq
         self.initial_weights = initial_weights
         # initiate super class
-        super(MonteCarloOpt, self).__init__(num_trials=self.num_trials)
+        super().__init__(num_trials=self.num_trials)
         # setting additional variables
         self.num_stocks = len(self.returns.columns)
         self.return_means = self.returns.mean()
@@ -113,14 +113,14 @@ def _random_weights(self):
              list of [expected return, volatility, sharpe ratio].
         """
         # select random weights for portfolio
-        w = np.array(np.random.random(self.num_stocks))
+        weights = np.array(np.random.random(self.num_stocks))
         # rebalance weights
-        w = w / np.sum(w)
+        weights = weights / np.sum(weights)
         # compute portfolio return and volatility
         portfolio_values = annualised_portfolio_quantities(
-            w, self.return_means, self.cov_matrix, self.risk_free_rate, self.freq
+            weights, self.return_means, self.cov_matrix, self.risk_free_rate, self.freq
         )
-        return (w, np.array(portfolio_values))
+        return (weights, np.array(portfolio_values))
 
     def _random_portfolios(self):
         """Performs a Monte Carlo run and gets a list of random portfolios
@@ -186,7 +186,7 @@ def plot_results(self):
             or self.opt_weights is None
             or self.opt_results is None
         ):
-            raise Exception(
+            raise ValueError(
                 "Error: Cannot plot, run the Monte Carlo " + "optimisation first."
             )
         # create scatter plot coloured by Sharpe Ratio
@@ -253,18 +253,12 @@ def properties(self):
         string = ""
         for val in opt_vals:
             string += "-" * 70
-            string += "\nOptimised portfolio for {}".format(
-                val.replace("Min", "Minimum").replace("Max", "Maximum")
-            )
-            string += "\n\nTime period: {} days".format(self.freq)
-            string += "\nExpected return: {0:0.3f}".format(
-                self.opt_results.loc[val]["Expected Return"]
-            )
-            string += "\nVolatility: {:0.3f}".format(
-                self.opt_results.loc[val]["Volatility"]
-            )
-            string += "\nSharpe Ratio: {:0.3f}".format(
-                self.opt_results.loc[val]["Sharpe Ratio"]
+            string += f"\nOptimised portfolio for {val.replace('Min', 'Minimum').replace('Max', 'Maximum')}"
+            string += f"\n\nTime period: {self.freq} days"
+            string += f"\nExpected return: {self.opt_results.loc[val]['Expected Return']:0.3f}"
+            string += f"\nVolatility: {self.opt_results.loc[val]['Volatility']:0.3f}"
+            string += (
+                f"\nSharpe Ratio: {self.opt_results.loc[val]['Sharpe Ratio']:0.3f}"
             )
             string += "\n\nOptimal weights:"
             string += "\n" + str(