formatting with black

finquant/portfolio.py

@@ -60,6 +60,13 @@
 from finquant.monte_carlo import MonteCarloOpt
 from finquant.quants import sharpe_ratio, value_at_risk, weighted_mean, weighted_std
 from finquant.quants import sharpe_ratio, weighted_mean, weighted_std, sortino_ratio, downside_risk
+from finquant.quants import (
+    sharpe_ratio,
+    weighted_mean,
+    weighted_std,
+    sortino_ratio,
+    downside_risk,
+)
 from finquant.returns import (
     cumulative_returns,
     daily_log_returns,
@@ -363,8 +370,9 @@ def comp_downside_risk(self, freq=252):
         :Output:
          :volatility: Downside risk of stock.
         """
-        return downside_risk(self.data, self.comp_weights(), self.risk_free_rate) * np.sqrt(freq)
-
+        return downside_risk(
+            self.data, self.comp_weights(), self.risk_free_rate
+        ) * np.sqrt(freq)
 
     def comp_cov(self):
         """Compute and return a ``pandas.DataFrame`` of the covariance matrix
@@ -427,7 +435,9 @@ def comp_sortino(self, freq=252):
          :sortino: ``float``, the Sortino Ratio of the portfolio
          May be NaN if the portoflio outperformed the risk free rate at every point
         """
-        return sortino_ratio(self.expected_return, self.downside_risk, self.risk_free_rate)
+        return sortino_ratio(
+            self.expected_return, self.downside_risk, self.risk_free_rate
+        )
 
     def _comp_skew(self):
         """Computes and returns the skewness of the stocks in the portfolio."""

finquant/quants.py

@@ -75,6 +75,7 @@ def sharpe_ratio(exp_return, volatility, risk_free_rate=0.005):
         raise ValueError("risk_free_rate is expected to be an integer or float.")
     return (exp_return - risk_free_rate) / float(volatility)
 
+
 def sortino_ratio(exp_return, downside_risk, risk_free_rate=0.005):
     """Computes the Sharpe Ratio
 
@@ -101,13 +102,14 @@ def sortino_ratio(exp_return, downside_risk, risk_free_rate=0.005):
         raise ValueError("risk_free_rate is expected to be an integer or float.")
     return (exp_return - risk_free_rate) / float(downside_risk)
 
+
 def downside_risk(data, weights, risk_free_rate=0.005):
     """Computes the downside risk (target semideviation of returns, given a risk free rate)
     :Input:
       :data: ``pandas.DataFrame`` with daily stock prices
       :weights: ``numpy.ndarray``/``pd.Series`` of weights
       :risk_free_rate: ``int``/``float`` (default=``0.005``), risk free rate
-    
+
     :Output:
       :downside_risk: ``float`` (can be NaN if all returns outperform the risk free rate)
     """
@@ -120,7 +122,6 @@ def downside_risk(data, weights, risk_free_rate=0.005):
     return np.sqrt((downside * downside).mean())
 
 
-
 def value_at_risk(investment, mu, sigma, conf_level=0.95) -> float:
     """Computes and returns the expected value at risk of an investment/assets.
 

finquant/returns.py

@@ -35,6 +35,7 @@ def daily_returns(data):
     """
     return data.pct_change().dropna(how="all").replace([np.inf, -np.inf], np.nan)
 
+
 def weighted_mean_daily_returns(data, weights):
     """Returns DataFrame with the daily weighted mean returns
 
@@ -47,6 +48,7 @@ def weighted_mean_daily_returns(data, weights):
     """
     return np.dot(daily_returns(data), weights)
 
+
 def daily_log_returns(data):
     """
     Returns DataFrame with daily log returns

tests/test_quants.py

@@ -14,6 +14,7 @@
 from finquant.quants import sharpe_ratio, annualised_portfolio_quantities
 import pdb
 
+
 def test_weighted_mean():
     means = np.array([1])
     weights = np.array([1])
@@ -82,17 +83,17 @@ def test_annualised_portfolio_quantities():
     for i in range(len(res)):
         assert abs(res[i] - orig[i]) <= 1e-15
 
+
 def test_downside_risk():
-    data1 = pd.DataFrame({ "1": [1, 2, 4, 8], "2" : [1, 2, 3, 4] })
+    data1 = pd.DataFrame({"1": [1, 2, 4, 8], "2": [1, 2, 3, 4]})
     weights = np.array([0.25, 0.75])
     rf_rate = 0.005
     dr1 = downside_risk(data1, weights, rf_rate)
     assert np.isnan(dr1)
 
     data2 = pd.DataFrame({"1": [10, 7, 10, 6, 11]})
     weights = np.array([1])
-    rf_rate = 0.
+    rf_rate = 0.0
     dr2 = downside_risk(data2, weights, rf_rate)
     print(dr2)
-    assert abs(dr2 - .35355339059327376220) <= 1e-15
-
+    assert abs(dr2 - 0.35355339059327376220) <= 1e-15

tests/test_returns.py

@@ -8,9 +8,14 @@
 )
 
 import numpy as np
-from finquant.returns import cumulative_returns, daily_returns, weighted_mean_daily_returns
+from finquant.returns import (
+    cumulative_returns,
+    daily_returns,
+    weighted_mean_daily_returns,
+)
 from finquant.returns import daily_log_returns, historical_mean_return
 
+
 def test_cumulative_returns():
     orig = [
         list(range(10)),
@@ -43,22 +48,24 @@ def test_daily_returns():
     assert all(abs(ret["1"].values - orig[0]) <= 1e-15)
     assert all(abs(ret["2"].values - orig[1]) <= 1e-15)
 
+
 def test_weighted_daily_mean_returns():
-    l1 = [ 1., 1.5, 2.25, 3.375 ]
-    l2 = [ 1., 2., 4., 8. ]
-    expected = [ 0.5 * 0.25 + 1 * 0.75 for i in range(len(l1)-1) ]
-    weights = np.array([ 0.25, 0.75 ])
-    d = { "1": l1, "2": l2 }
+    l1 = [1.0, 1.5, 2.25, 3.375]
+    l2 = [1.0, 2.0, 4.0, 8.0]
+    expected = [0.5 * 0.25 + 1 * 0.75 for i in range(len(l1) - 1)]
+    weights = np.array([0.25, 0.75])
+    d = {"1": l1, "2": l2}
     df = pd.DataFrame(d)
     ret = weighted_mean_daily_returns(df, weights)
     assert all(abs(ret - expected) <= 1e-15)
 
-    d = { "1": l1 }
-    expected = [ 0.5 for i in range(len(l1)-1) ]
+    d = {"1": l1}
+    expected = [0.5 for i in range(len(l1) - 1)]
     df = pd.DataFrame(d)
     ret = weighted_mean_daily_returns(df, np.array([1]))
     assert all(abs(ret - expected) <= 1e-15)
 
+
 def test_daily_log_returns():
     orig = [
         [