def test_binary_class():
stock_d = testdata()
ti = TechnicalIndicators(stock_d)
ti.calc_ret_index()
ret_index = ti.stock['ret_index']
f = Features()
train_X, train_y = f.binary_class(ret_index, range=90)
expected = [
1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0,
1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0,
0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,
1, 1, 0
]
for r, e in zip(train_y, expected):
eq_(r, e)
r = round(train_X[-1][-1], 5)
expected = 1.35486
eq_(r, expected)
r = round(train_X[0][0], 5)
expected = 1.19213
eq_(r, expected)
expected = 14
r = len(train_X[0])
eq_(r, expected)
expected = 75
r = len(train_X)
eq_(r, expected)
train_X, train_y = f.binary_class(ret_index)
expected = 0
eq_(train_y[0], expected)
expected = 1
eq_(len(train_y), expected)
r = round(train_X[0][0], 5)
expected = 1.30311
eq_(r, expected)
expected = 14
r = len(train_X[0])
eq_(r, expected)
expected = 1
r = len(train_X)
eq_(r, expected)
def test_binary_class():
stock_d = testdata()
ti = TechnicalIndicators(stock_d)
ti.calc_ret_index()
ret_index = ti.stock['ret_index']
f = Features()
train_X, train_y = f.binary_class(ret_index, range=90)
expected = [1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1,
0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1,
0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1,
1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,
1, 1, 0]
for r, e in zip(train_y, expected):
eq_(r, e)
r = round(train_X[-1][-1], 5)
expected = 1.35486
eq_(r, expected)
r = round(train_X[0][0], 5)
expected = 1.19213
eq_(r, expected)
expected = 14
r = len(train_X[0])
eq_(r, expected)
expected = 75
r = len(train_X)
eq_(r, expected)
train_X, train_y = f.binary_class(ret_index)
expected = 0
eq_(train_y[0], expected)
expected = 1
eq_(len(train_y), expected)
r = round(train_X[0][0], 5)
expected = 1.30311
eq_(r, expected)
expected = 14
r = len(train_X[0])
eq_(r, expected)
expected = 1
r = len(train_X)
eq_(r, expected)
def train(self, arr, remember=True, classifier="Decision Tree"):
f = Features()
if os.path.exists(self.filename):
self.clf = self._load_clf()
train_X, train_y = f.binary_class(arr)
else:
self.clf = self.new_clf(classifier=classifier)
train_X, train_y = f.binary_class(arr, len(arr))
self.clf.fit(train_X, train_y)
if remember:
self._save_clf()
return train_X, train_y
def train(self, arr, remember=True,
classifier="Decision Tree"):
f = Features()
if os.path.exists(self.filename):
self.clf = self._load_clf()
train_X, train_y = f.binary_class(arr)
else:
self.clf = self.new_clf(classifier=classifier)
train_X, train_y = f.binary_class(arr, len(arr))
self.clf.fit(train_X, train_y)
if remember:
self._save_clf()
return train_X, train_y
