def test_predict(self):
import io
technical = TAlibWrapper().get_technicals_of_series(self._data)
pre = self.rnn_wrapper.preprocess(technical)
pre = TAlibWrapper().add_high_low_data(pre)
Xall, yall = self.rnn_wrapper.make_data_and_label(pre)
model = self.rnn_wrapper.loadModelFfile()
prediction = model.predict(Xall)
import matplotlib.pyplot as plt
x = range(100)
y = yall[-100:, 5]
pred_y = prediction[-100:, 5]
plt.ylim(-0.1, 1.1)
plt.plot(x, [0.5] * len(y), label='border')
plt.plot(x, y, label='answer')
plt.plot(x, pred_y, label='prediction_prob')
plt.plot(x, yall[-100:, 0], label='price')
plt.legend()
buffer = io.BytesIO()
plt.savefig(buffer, format='png')
buffer.seek(0)
image = self.rnn_wrapper.plot2image(buffer, 0)
self.rnn_wrapper.add_image2board("image", image)
plt.show()
def test_make_data_and_label(self):
technical = TAlibWrapper().get_technicals_of_series(self._data)
pre = self.rnn_wrapper.preprocess(technical)
pre = TAlibWrapper().add_high_low_data(pre)
X, y = self.rnn_wrapper.make_data_and_label(pre)
print(np.shape(X), np.shape(y))
return True
def test_high_low_price(self):
technical = TAlibWrapper().get_technicals_of_series(self._data)
pre = self.rnn_wrapper.preprocess(technical)
pre = TAlibWrapper().add_high_low_data(pre)
Xall, yall = self.rnn_wrapper.make_data_and_label(pre)
model = self.rnn_wrapper.loadModelFfile()
prediction = model.predict(Xall)
match = self.rnn_wrapper.high_low_score(yall, prediction)
print(f"{match} / {len(yall)}")
def test_fit(self):
technical = TAlibWrapper().get_technicals_of_series(self._data)
pre = self.rnn_wrapper.preprocess(technical)
pre = TAlibWrapper().add_high_low_data(pre)
Xall, yall = self.rnn_wrapper.make_data_and_label(pre)
partition = round(len(pre) * 0.7)
Xtrain, ytrain = Xall[:partition], yall[:partition]
model = self.rnn_wrapper.create_basic_lstm_model(Xtrain)
result = self.rnn_wrapper.fit(model, Xtrain, ytrain)
self.rnn_wrapper.save2file(result)
def test_test_sprit(self):
technical = TAlibWrapper().get_technicals_of_series(self._data)
pre = self.rnn_wrapper.preprocess(technical)
pre = TAlibWrapper().add_high_low_data(pre)
Xall, yall = self.rnn_wrapper.make_data_and_label(pre)
partition = round(len(pre) * 0.7)
Xtrain, ytrain = Xall[:partition], yall[:partition]
print(np.shape(Xall), np.shape(yall), np.shape(Xtrain),
np.shape(ytrain))
self.assertEqual(partition, len(Xtrain))
def test_high_low_probability_with_border(self):
technical = TAlibWrapper().get_technicals_of_series(self._data)
pre = self.rnn_wrapper.preprocess(technical)
pre = TAlibWrapper().add_high_low_data(pre)
Xall, yall = self.rnn_wrapper.make_data_and_label(pre)
model = self.rnn_wrapper.loadModelFfile()
prediction = model.predict(Xall)
match, total = self.rnn_wrapper.high_low_probavility_score_with_border(
yall, prediction, 19)
print(f"all data count:{len(yall)}")
print(f"{match} / {total} {match/total}%")
def test_rmse(self):
technical = TAlibWrapper().get_technicals_of_series(self._data)
pre = self.rnn_wrapper.preprocess(technical)
pre = TAlibWrapper().add_high_low_data(pre)
Xall, yall = self.rnn_wrapper.make_data_and_label(pre)
partition = round(len(pre) * 0.7)
Xtrain, ytrain = Xall[:partition], yall[:partition]
Xtest, ytest = Xall[partition:], yall[partition:]
model = self.rnn_wrapper.loadModelFfile()
rmse_train, rmse_test = self.rnn_wrapper.rmse_score(
model, Xtrain, Xtest, ytrain, ytest, 0)
print(f"RMSE train: {rmse_train} RMSE test: {rmse_test}")
def test_preprocess(self):
answer = TAlibWrapper().get_technicals_of_series(self._data)
before = np.shape(answer)
answer = self.rnn_wrapper.preprocess(answer)
answer = TAlibWrapper().add_high_low_data(answer)
after = np.shape(answer)
print(before, after)
# self.assertEqual(before, after)
# print(answer)
print(f'min={np.min(answer)} max={np.max(answer)}')
self.assertTrue(np.round(np.min(answer), 5) >= 0.)
self.assertTrue(np.round(np.max(answer), 5) <= 1.)
frame = pd.DataFrame(answer).iloc[:, :5]
print(np.shape(frame))
plotting.scatter_matrix(frame)
import matplotlib.pyplot as plt
plt.show()
return True
def create_data(self):
api = TAlib_API(self._context)
data = api.get_technicals()
data = self.rnn_wrapper.preprocess(data)
data = TAlibWrapper().add_high_low_data(data)
Xall, yall = self.rnn_wrapper.make_data_and_label(data)
partition = round(len(yall) * 0.7)
Xtrain, ytrain = Xall[:partition], yall[:partition]
Xtest, ytest = Xall[partition:], yall[:partition]
return Xall, yall, Xtrain, ytrain, Xtest, ytest
class TAlib_API:
def __init__(self, context):
self.context = context
self.wrapper = TAlibWrapper()
def get_technicals(self):
df =IndicativeDocument().docs2df()
result = None
for idx1, item in enumerate(df.columns.values[1:]):
answer = self.wrapper.get_technicals_of_series(df.loc[:, item])
if result is None:
result = answer
else:
result = np.hstack((result, answer))
# print(np.shape(result))
return result
def test_technicals(self):
answer = TAlibWrapper().get_technicals_of_series(self._data)
print(answer)
def __init__(self, context):
self.context = context
self.wrapper = TAlibWrapper()