Merge pull request #888 from AlexCatarino/sklearn

Adds python sklearn package

Algorithm.Python/PythonPackageTestAlgorithm.py

@@ -0,0 +1,137 @@
+# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License"); 
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from clr import AddReference
+AddReference("System")
+AddReference("QuantConnect.Algorithm")
+AddReference("QuantConnect.Common")
+
+from System import *
+from QuantConnect import *
+from QuantConnect.Algorithm import *
+
+# Libraries included with basic python install
+from bisect import bisect
+import cmath
+import collections
+import copy
+import functools
+import heapq
+import itertools
+import math
+import operator
+import pytz
+import Queue
+import re
+import time
+import zlib
+
+# Third party libraries added with pip
+from sklearn.ensemble import RandomForestClassifier
+import blaze   # includes sqlalchemy, odo 
+import numpy
+import scipy
+import cvxopt
+import cvxpy
+from pykalman import KalmanFilter
+import statsmodels.api as sm
+import talib
+
+class PythonPackageTestAlgorithm(QCAlgorithm):
+    '''Basic template algorithm simply initializes the date range and cash'''
+
+
+    def Initialize(self):
+        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+        
+        self.SetStartDate(2013, 10, 7)   #Set Start Date
+        self.SetStartDate(2013, 10, 11)  #Set End Date
+        self.AddEquity("SPY", Resolution.Daily)
+
+        # numpy test
+        print "numpy test >>> print numpy.pi: " , numpy.pi
+        
+        # scipy test: 
+        print "scipy test >>> print mean of 1 2 3 4 5:", scipy.mean(numpy.array([1, 2, 3, 4, 5]))
+
+        #sklearn test
+        print "sklearn test >>> default RandomForestClassifier:", RandomForestClassifier()
+        
+        # cvxopt matrix test
+        print "cvxopt >>>", cvxopt.matrix([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], (2,3))
+
+        # blaze test
+        blaze_test()
+        
+        # cvxpy test
+        cvxpy_test()
+
+        # statsmodels test
+        statsmodels_test()
+
+        # pykalman test
+        pykalman_test()
+
+        # talib test
+        print "talib test >>>", talib.SMA(numpy.random.random(100))
+
+    def OnData(self, data): pass
+
+def blaze_test():
+    accounts = blaze.symbol('accounts', 'var * {id: int, name: string, amount: int}')
+    deadbeats = accounts[accounts.amount < 0].name
+    L = [[1, 'Alice',   100],
+         [2, 'Bob',    -200],
+         [3, 'Charlie', 300],
+         [4, 'Denis',   400],
+         [5, 'Edith',  -500]]
+    print "blaze test >>>", list(blaze.compute(deadbeats, L))
+
+def grade(score, breakpoints=[60, 70, 80, 90], grades='FDCBA'):
+    i = bisect(breakpoints, score)
+    return grades[i]
+
+def cvxpy_test():
+    numpy.random.seed(1)
+    n = 10
+    mu = numpy.abs(numpy.random.randn(n, 1))
+    Sigma = numpy.random.randn(n, n)
+    Sigma = Sigma.T.dot(Sigma)
+
+    w = cvxpy.Variable(n)
+    gamma = cvxpy.Parameter(sign='positive')
+    ret = mu.T*w 
+    risk = cvxpy.quad_form(w, Sigma)
+    print "csvpy test >>> ", cvxpy.Problem(cvxpy.Maximize(ret - gamma*risk), 
+               [cvxpy.sum_entries(w) == 1, 
+                w >= 0])
+
+def statsmodels_test():
+    nsample = 100
+    x = numpy.linspace(0, 10, 100)
+    X = numpy.column_stack((x, x**2))
+    beta = numpy.array([1, 0.1, 10])
+    e = numpy.random.normal(size=nsample)
+
+    X = sm.add_constant(X)
+    y = numpy.dot(X, beta) + e
+
+    model = sm.OLS(y, X)
+    results = model.fit()
+    print "statsmodels tests >>>", results.summary()
+
+def pykalman_test():
+    kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])
+    measurements = numpy.asarray([[1,0], [0,0], [0,1]])  # 3 observations
+    kf = kf.em(measurements, n_iter=5)
+    print "pykalman test >>>", kf.filter(measurements)