AlexCatarino
164 lines
4.9 KiB
Python
164 lines
4.9 KiB
Python
# 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
|
|
from copulalib.copulalib import Copula
|
|
import theano
|
|
import xgboost
|
|
|
|
class PythonPackageTestAlgorithm(QCAlgorithm):
|
|
'''Algorithm to test third party libraries'''
|
|
|
|
def Initialize(self):
|
|
self.SetStartDate(2013, 10, 7) #Set Start Date
|
|
self.SetStartDate(2013, 10, 7) #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))
|
|
|
|
# talib test
|
|
print "talib test >>>", talib.SMA(numpy.random.random(100))
|
|
|
|
# blaze test
|
|
blaze_test()
|
|
|
|
# cvxpy test
|
|
cvxpy_test()
|
|
|
|
# statsmodels test
|
|
statsmodels_test()
|
|
|
|
# pykalman test
|
|
pykalman_test()
|
|
|
|
# copulalib test
|
|
copulalib_test()
|
|
|
|
# theano test
|
|
theano_test()
|
|
|
|
# xgboost test
|
|
xgboost_test()
|
|
|
|
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)
|
|
|
|
def copulalib_test():
|
|
x = numpy.random.normal(size=100)
|
|
y = 2.5 * x + numpy.random.normal(size=100)
|
|
|
|
#Make the instance of Copula class with x, y and clayton family::
|
|
print "copulalib test >>>", Copula(x, y, family='clayton')
|
|
|
|
def theano_test():
|
|
a = theano.tensor.vector() # declare variable
|
|
out = a + a ** 10 # build symbolic expression
|
|
f = theano.function([a], out) # compile function
|
|
print "theano test >>>", f([0, 1, 2])
|
|
|
|
def xgboost_test():
|
|
data = numpy.random.rand(5,10) # 5 entities, each contains 10 features
|
|
label = numpy.random.randint(2, size=5) # binary target
|
|
print "xgboost test >>>", xgboost.DMatrix( data, label=label) |