Derek M Tishler
6c430f6ae1
Added the shortest deap example, onemax_short.py, to the PythonPackageTestAlgorithm script.
258 lines
7.8 KiB
Python
258 lines
7.8 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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import talib
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from copulalib.copulalib import Copula
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import theano
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import xgboost
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from arch import arch_model
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from keras.models import Sequential
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from keras.layers import Dense, Activation
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import tensorflow as tf
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from deap import algorithms, base, creator, tools
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### <summary>
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### Demonstration of all the packages you can import with the QuantConnect/LEAN trading engine.s
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="using quantconnect" />
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class PythonPackageTestAlgorithm(QCAlgorithm):
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'''Algorithm to test third party libraries'''
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def Initialize(self):
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self.SetStartDate(2013, 10, 7) #Set Start Date
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self.SetStartDate(2013, 10, 7) #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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# talib test
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print "talib test >>>", talib.SMA(numpy.random.random(100))
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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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# copulalib test
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copulalib_test()
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# theano test
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theano_test()
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# xgboost test
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xgboost_test()
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# arch test
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arch_test()
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# keras test
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keras_test()
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# tensorflow test
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tensorflow_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)
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def copulalib_test():
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x = numpy.random.normal(size=100)
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y = 2.5 * x + numpy.random.normal(size=100)
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#Make the instance of Copula class with x, y and clayton family::
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print "copulalib test >>>", Copula(x, y, family='clayton')
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def theano_test():
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a = theano.tensor.vector() # declare variable
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out = a + a ** 10 # build symbolic expression
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f = theano.function([a], out) # compile function
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print "theano test >>>", f([0, 1, 2])
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def xgboost_test():
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data = numpy.random.rand(5,10) # 5 entities, each contains 10 features
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label = numpy.random.randint(2, size=5) # binary target
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print "xgboost test >>>", xgboost.DMatrix( data, label=label)
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def arch_test():
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r = numpy.array([0.945532630498276,
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0.614772790142383,
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0.834417758890680,
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0.862344782601800,
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0.555858715401929,
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0.641058419842652,
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0.720118656981704,
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0.643948007732270,
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0.138790608092353,
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0.279264178231250,
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0.993836948076485,
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0.531967023876420,
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0.964455754192395,
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0.873171802181126,
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0.937828816793698])
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garch11 = arch_model(r, p=1, q=1)
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res = garch11.fit(update_freq=10)
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print "arch test >>>", res.summary()
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def keras_test():
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# Initialize the constructor
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model = Sequential()
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# Add an input layer
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model.add(Dense(12, activation='relu', input_shape=(11,)))
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# Add one hidden layer
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model.add(Dense(8, activation='relu'))
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# Add an output layer
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model.add(Dense(1, activation='sigmoid'))
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print "keras test >>>", model
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def tensorflow_test():
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node1 = tf.constant(3.0, tf.float32)
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node2 = tf.constant(4.0) # also tf.float32 implicitly
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sess = tf.Session()
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node3 = tf.add(node1, node2)
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print "tensorflow test >>>", "sess.run(node3): ", sess.run(node3)
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def deap_test():
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# onemax example evolves to print list of ones: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
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numpy.random.seed(1)
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def evalOneMax(individual):
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return sum(individual),
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creator.create("FitnessMax", base.Fitness, weights=(1.0,))
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creator.create("Individual", list, typecode='b', fitness=creator.FitnessMax)
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toolbox = base.Toolbox()
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toolbox.register("attr_bool", numpy.random.randint, 0, 1)
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toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, 10)
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toolbox.register("population", tools.initRepeat, list, toolbox.individual)
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toolbox.register("evaluate", evalOneMax)
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toolbox.register("mate", tools.cxTwoPoint)
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toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
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toolbox.register("select", tools.selTournament, tournsize=3)
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pop = toolbox.population(n=50)
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hof = tools.HallOfFame(1)
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stats = tools.Statistics(lambda ind: ind.fitness.values)
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stats.register("avg", numpy.mean)
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stats.register("std", numpy.std)
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stats.register("min", numpy.min)
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stats.register("max", numpy.max)
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pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=30,
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stats=stats, halloffame=hof, verbose=False) # change to verbose=True to see evolution table
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print "deap test >>>", hof[0]
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