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IV_simpleConvolutionnetwork.py
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IV_simpleConvolutionnetwork.py
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"""
Including features and labels engineering and imposing of simple convolution neutral network.
"""
import paddle
from paddle.nn import Conv2D, MaxPool2D, Linear
import paddle.nn.functional as F
import warnings
# Using gpu
paddle.device.set_device('gpu:0')
class simpleCNN(paddle.nn.Layer):
def __init__(self):
super(simpleCNN, self).__init__()
# Define convolution network with out_channels to be 20, kernel size to be 5...
self.conv1 = Conv2D(in_channels=1, out_channels=10, kernel_size=2, stride=1, padding=2)
self.max_pool1 = MaxPool2D(kernel_size=2, stride=2)
self.conv2 = Conv2D(in_channels=10, out_channels=10, kernel_size=2, stride=1, padding=2)
self.max_pool2 = MaxPool2D(kernel_size=2, stride=2)
self.fc = Linear(in_features=90, out_features=1)
# Define the net forward function
def forward(self, inputs):
x = self.conv1(inputs)
x = F.relu(x)
x = self.max_pool1(x)
x = self.conv2(x)
x = F.relu(x)
x = self.max_pool2(x)
x = paddle.reshape(x, [x.shape[0], -1])
x = self.fc(x)
return x
"""
First of all, the feature's date and label's date should not be in the same as my purpose is to predict trading
direction of the future. Thus I am adjusting them by lagging the feature by 2 days.
The feature and label are trying to be engineered by scikit-learn package sklearn's transformation functions.
"""
import numpy as np
import datetime as dt
import pandas as pd
from sklearn import preprocessing
class simpleConvolutionnetwork:
def __init__(self, indexWanted, emailFeatures, weiboFeatures, featuresYields, labels):
self.indexWanted = indexWanted
self.emailFeatures = emailFeatures
self.weiboFeatures = weiboFeatures
self.featuresYields = featuresYields
self.labels = labels
self.results = pd.DataFrame(index=['_'.join([self.indexWanted[0], 'scnn'])],
columns=['VOLATILITY', 'STD', 'T+1 RESULT', 'T+2 RESULT'])
self.fnl_fd = self.fnl_reProcessing()
self.scnn_modeling()
# print(self.results)
def datetimeProcessing(self, dataToproc):
# functions to reprocess the DATE formate
dataToproc['DATE_'] = ''
for i in range(len(dataToproc)):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
dataToproc['DATE_'].iloc[i] = dataToproc['DATE'].iloc[i].to_pydatetime().date()
dataToproc.index = dataToproc['DATE_']
return dataToproc
def fnl_reProcessing(self):
# function to combine the features and labels.
self.emailFeatures = self.datetimeProcessing(self.emailFeatures)
self.weiboFeatures = self.datetimeProcessing(self.weiboFeatures)
self.featuresYields = self.datetimeProcessing(self.featuresYields)
self.labels = self.datetimeProcessing(self.labels)
# join the emailFeatures and labels into one dataframe
fnl_fd = self.labels.join(self.emailFeatures, rsuffix = '_other')
# remove NAs
fnl_fd = fnl_fd.dropna()
fnl_fd = fnl_fd.drop(['DATE', 'DATE_other', 'DATE__other'], axis = "columns")
# join the weiboFeatures
fnl_fd = fnl_fd.join(self.weiboFeatures, rsuffix='_weibo')
fnl_fd = fnl_fd.dropna()
fnl_fd = fnl_fd.drop(['DATE', 'DATE__weibo'], axis="columns")
# more features yields
fnl_fd = fnl_fd.join(self.featuresYields, rsuffix = '_other')
fnl_fd = fnl_fd.dropna()
fnl_fd = fnl_fd.drop(['DATE', 'DATE__other'], axis = "columns")
return fnl_fd
def scnn_train(self, datas): # features and labels
scnn_model = simpleCNN()
opt = paddle.optimizer.SGD(learning_rate=0.001, parameters=scnn_model.parameters()) # 0.001
EPOCH_NUM = 250 #250
features = datas[0]
labels = datas[1]
for epoch in range(EPOCH_NUM):
for i in range(labels.shape[0]):
feature = features[i].reshape((1, 1, 3, 4)).astype('float32')
label = labels[i].reshape((1, 1)).astype('float32')
# calculate forward
predict = scnn_model(feature)
# calculate loss
loss = F.square_error_cost(predict, label)
avg_loss = paddle.mean(loss)
# backwarding
avg_loss.backward()
opt.step()
opt.clear_grad()
# print("EPOCH_NUM: {}, loss is: {}".format(epoch, avg_loss.numpy()))
return scnn_model
def scnn_predict(self, datas):
# for day t+1
feature_t_1 = datas[0]
feature_t_1_tensor = paddle.to_tensor(feature_t_1).reshape((1, 1, 3, 4)).astype('float32')
result_t_1 = self.scnn_model(feature_t_1_tensor)
# print('Prediction for DAY T + 1:', result_t_1.numpy())
self.results['T+1 RESULT'] = result_t_1.numpy()
# for day t+2
feature_t_2 = datas[1]
feature_t_2_tensor = paddle.to_tensor(feature_t_2).reshape((1, 1, 3, 4)).astype('float32')
result_t_2 = self.scnn_model(feature_t_2_tensor)
# print('Prediction for DAY T + 2:', result_t_2.numpy())
self.results['T+2 RESULT'] = result_t_2.numpy()
def scnn_modeling(self):
fnl_fd = self.fnl_fd
# transform the dataset into more desired features and labels.
labels = self.fnl_fd['LABELS']
features = self.fnl_fd.drop(['DATE_', 'LABELS'], axis="columns")
self.X_predict_date = features.index[-1]
labels_scaled = labels
# using StandardScaler of preprocessing in sklearn to scale the features
self.scaler = preprocessing.StandardScaler().fit(features)
features_scaled = self.scaler.transform(features)
# in order to predict result in 2 days, I am lagging the features by 2.
labels_train = labels_scaled[2:]
features_train = features_scaled[:-2:]
# prepare the labels and features for Deep Learning.
labels_array = np.array(labels_train).reshape(len(labels_train), 1)
features_array = np.array(features_train)
labels_tensor = paddle.to_tensor(labels_array)
features_tensor = paddle.to_tensor(features_array)
# implement the training function.
datas = [features_tensor, labels_tensor]
self.scnn_model = self.scnn_train(datas)
# paddle.save(self.scnn_model.state_dict(), './scnn_model.pdparams')
# Try to predict from new features.
features_predict = features_scaled[-2:]
self.scnn_predict(features_predict)
# Other results
self.results['VOLATILITY'] = abs(labels).mean()
self.results['STD'] = labels.std()