AlexCatarino
e2b9ef0313
Adds blaze, cvxopt, cvxpy, pykalman and statsmodels to foundation docker image. Adds an algorithm for testing python packages that are currently whitelisted
134 lines
4.1 KiB
Python
134 lines
4.1 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from clr import AddReference
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AddReference("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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# Libraries included with basic python install
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from bisect import bisect
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import cmath
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import collections
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import copy
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import functools
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import heapq
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import itertools
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import math
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import operator
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import pytz
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import Queue
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import re
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import time
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import zlib
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# Third party libraries added with pip
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from sklearn.ensemble import RandomForestClassifier
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import blaze # includes sqlalchemy, odo
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import numpy
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import scipy
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import cvxopt
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import cvxpy
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from pykalman import KalmanFilter
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import statsmodels.api as sm
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class PythonPackageTestAlgorithm(QCAlgorithm):
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'''Basic template algorithm simply initializes the date range and cash'''
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def Initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.SetStartDate(2013, 10, 7) #Set Start Date
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self.SetStartDate(2013, 10, 11) #Set End Date
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self.AddEquity("SPY", Resolution.Daily)
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# numpy test
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print "numpy test >>> print numpy.pi: " , numpy.pi
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# scipy test:
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print "scipy test >>> print mean of 1 2 3 4 5:", scipy.mean(numpy.array([1, 2, 3, 4, 5]))
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#sklearn test
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print "sklearn test >>> default RandomForestClassifier:", RandomForestClassifier()
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# cvxopt matrix test
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print "cvxopt >>>", cvxopt.matrix([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], (2,3))
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# blaze test
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blaze_test()
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# cvxpy test
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cvxpy_test()
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# statsmodels test
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statsmodels_test()
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# pykalman test
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pykalman_test()
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def OnData(self, data): pass
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def blaze_test():
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accounts = blaze.symbol('accounts', 'var * {id: int, name: string, amount: int}')
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deadbeats = accounts[accounts.amount < 0].name
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L = [[1, 'Alice', 100],
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[2, 'Bob', -200],
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[3, 'Charlie', 300],
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[4, 'Denis', 400],
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[5, 'Edith', -500]]
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print "blaze test >>>", list(blaze.compute(deadbeats, L))
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def grade(score, breakpoints=[60, 70, 80, 90], grades='FDCBA'):
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i = bisect(breakpoints, score)
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return grades[i]
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def cvxpy_test():
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numpy.random.seed(1)
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n = 10
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mu = numpy.abs(numpy.random.randn(n, 1))
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Sigma = numpy.random.randn(n, n)
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Sigma = Sigma.T.dot(Sigma)
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w = cvxpy.Variable(n)
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gamma = cvxpy.Parameter(sign='positive')
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ret = mu.T*w
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risk = cvxpy.quad_form(w, Sigma)
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print "csvpy test >>> ", cvxpy.Problem(cvxpy.Maximize(ret - gamma*risk),
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[cvxpy.sum_entries(w) == 1,
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w >= 0])
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def statsmodels_test():
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nsample = 100
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x = numpy.linspace(0, 10, 100)
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X = numpy.column_stack((x, x**2))
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beta = numpy.array([1, 0.1, 10])
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e = numpy.random.normal(size=nsample)
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X = sm.add_constant(X)
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y = numpy.dot(X, beta) + e
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model = sm.OLS(y, X)
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results = model.fit()
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print "statsmodels tests >>>", results.summary()
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def pykalman_test():
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kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])
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measurements = numpy.asarray([[1,0], [0,0], [0,1]]) # 3 observations
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kf = kf.em(measurements, n_iter=5)
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print "pykalman test >>>", kf.filter(measurements) |