Modified tests and added annualized return calculation

PerformanceAnalytics/frequency.py

@@ -1,25 +1,7 @@
-class frequency:
-    second = 0
-    minutely = 1
-    hourly = 2
-    weekly = 3
-    monthly = 4
-    quarterly = 5
-    yearly = 6
+class Frequency:
+    def __init__(self, scale=None, label=None):
+        self.label = label
+        self.scale = scale
 
-    def to_str(self, d):
-        if d is frequency.second:
-            name = "S"
-        if d is frequency.minutely:
-            name = "T"
-        if d is frequency.hourly:
-            name = "H"
-        if d is frequency.weekly:
-            name = "W"
-        if d is frequency.monthly:
-            name = "M"
-        if d is frequency.quarterly:
-            name = "Q"
-        if d is frequency.yearly:
-            name = "A"
-        return name
+    def __str__(self):
+        return self.label

PerformanceAnalytics/returns.py

@@ -1,5 +1,7 @@
 import pandas as pd
 import numpy as np
+import math
+import utils
 
 
 def cumulative(R, geometric=True):
@@ -58,3 +60,15 @@ def rebalancing(R, weights):
     )
     portfolio_return.index.name = 'Date'
     return portfolio_return
+
+
+def annualized(R, scale=None, geometric=True):
+    n = len(R)
+    result = 0
+    if scale is None:
+        scale = utils.periodicity(R).scale
+    if geometric:
+        result = math.pow((1 + R).prod(), scale / n) - 1
+    else:
+        result = R.mean() * scale
+    return result

PerformanceAnalytics/utils.py

@@ -0,0 +1,36 @@
+import numpy as np
+from frequency import Frequency
+
+
+def periodicity(df):
+    """
+    Frequencies higher than daily will not be available
+    """
+    med = np.median(np.diff(df.index.values))
+    seconds = int(med.astype('timedelta64[s]').item().total_seconds())
+    freq = Frequency()
+    if seconds < 60:
+        freq.label = "S"
+        freq.scale = None
+    elif seconds < 3600:
+        freq.label = "T"
+        freq.scale = None
+    elif seconds < 86400:
+        freq.label = "H"
+        freq.scale = None
+    elif seconds == 86400:
+        freq.label = "D"
+        freq.scale = 252
+    elif seconds <= 604800:
+        freq.label = "W"
+        freq.scale = 52
+    elif seconds <= 2678400:
+        freq.label = "M"
+        freq.scale = 12
+    elif seconds <= 7948800:
+        freq.label = "Q"
+        freq.scale = 4
+    else:  # anything lower than year is deemed as yearly
+        freq.label = "A"
+        freq.scale = 1
+    return freq

conftest.py

@@ -0,0 +1,5 @@
+import os
+import sys
+
+
+sys.path.append(os.path.dirname(__file__))

tests/test_ratios.py

@@ -1,10 +1,5 @@
 from PerformanceAnalytics import ratios
-import pandas as pd
-
 
 class TestRatios:
     def test_kelly_ratio(self):
-        edhec = pd.read_csv("edhec.csv", parse_dates=[0])
-        edhec.index = edhec.ix[:, 0]
-        edhec = edhec.ix[:, 1:]
-        print ratios.returns(edhec.ix[:,0])
+        pass

tests/test_returns.py

@@ -3,11 +3,7 @@
 
 
 class TestReturns:
-    def test_rebalancing(self):
-        weights = pd.read_csv("weights.csv", parse_dates=[0])
-        edhec = pd.read_csv("edhec.csv", parse_dates=[0])
-        edhec.index = edhec.ix[:, 0]
-        weights.index = weights.ix[:, 0]
-        weights = weights.ix[:, 1:]
-        edhec = edhec.ix[:, 1:]
-        assert len(returns.rebalancing(edhec, weights)) == 116
+    def test_annualized(self):
+        dates = pd.date_range("1/1/2010", periods=12, freq="M")
+        df = pd.DataFrame([0.1 for i in range(12)], index=dates)
+        assert abs(returns.annualized(df) - 2.138428) < 0.0001

tests/test_utils.py

@@ -0,0 +1,10 @@
+import pandas as pd
+from PerformanceAnalytics import utils
+
+
+def test_utils():
+    dates = pd.date_range('1/1/2010', periods=12, freq="M")
+    df = pd.DataFrame([0 for i in range(12)], index=dates)
+    freq = utils.periodicity(df)
+    assert freq.scale == 12
+    assert freq.label == "M"