Fully Connected Layer:<\/strong> Positioned at the end to perform classification tasks.<\/li>\n<\/ul>\n3.1 How CNN Works<\/h3>\n
CNNs start from the input layer and process information gradually through multiple intermediate layers before reaching the output layer. Each convolutional layer uses multiple filters to extract features from the input data. This is akin to detecting patterns of color or shape in images; in financial data, it is used to recognize patterns or trends in price fluctuations.<\/p>\n
4. Modeling Grid Data with CNN<\/h2>\n
Grid data consists of data arranged over time, such as stock prices recorded at regular intervals. The process of modeling this data using CNN involves the following steps:<\/p>\n
4.1 Data Preparation<\/h3>\n
The first step is to prepare the dataset. It is necessary to collect the data and convert it into grid form, followed by processing it to be suitable for input into the CNN. Libraries such as pandas and numpy can be utilized for this.<\/p>\n
\nimport pandas as pd\nimport numpy as np\n\n# Load data\ndata = pd.read_csv('stock_data.csv')\n# Select necessary columns\ndata = data[['Date', 'Open', 'High', 'Low', 'Close', 'Volume']]\n# Sort the data\ndata['Date'] = pd.to_datetime(data['Date'])\ndata.set_index('Date', inplace=True)\n# Handle missing values\ndata.fillna(method='ffill', inplace=True)\n# Normalize\ndata = (data - data.mean()) \/ data.std()\n<\/code><\/pre>\n4.2 Building the CNN Model<\/h3>\n
Once the data is prepared, we can now build the CNN model. We can define a model structure using the Keras library as follows:<\/p>\n
\nfrom keras.models import Sequential\nfrom keras.layers import Conv1D, MaxPooling1D, Flatten, Dense\n\nmodel = Sequential()\nmodel.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(timesteps, features)))\nmodel.add(MaxPooling1D(pool_size=2))\nmodel.add(Flatten())\nmodel.add(Dense(units=1, activation='sigmoid'))\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n<\/code><\/pre>\n4.3 Training the Model<\/h3>\n
After defining the model, we proceed to train it using the actual data. Once training is completed, we evaluate the model’s performance:<\/p>\n
\nmodel.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_val, y_val))\nloss, accuracy = model.evaluate(X_test, y_test)\nprint(f\"Test Accuracy: {accuracy:.2f}\")\n<\/code><\/pre>\n5. Result Analysis and Visualization<\/h2>\n
If the model’s performance is satisfactory, we analyze and visualize the results before applying them to actual trading to derive insights. For example, we can compare the predicted results with actual prices:<\/p>\n
\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(14, 7))\nplt.plot(y_test, label='Actual Prices', color='blue')\nplt.plot(predictions, label='Predicted Prices', color='red')\nplt.title('Actual vs Predicted Prices')\nplt.legend()\nplt.show()\n<\/code><\/pre>\n6. Conclusion<\/h2>\n
Modeling grid data using CNN is a very useful approach in algorithmic trading. In this course, we introduced the basic concepts of CNN, prepared grid data, and built and trained a CNN model in a comprehensive manner. Based on this knowledge, we hope you advance your algorithmic trading strategies to the next level.<\/p>\n
In the next course, we will explore various techniques to further enhance performance. We wish you success in delving deeply into the world of machine learning and deep learning in the financial markets!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
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This course aims to delve into how CNN (Convolutional Neural Network) models data structured … \ub354 \ubcf4\uae30 “Machine Learning and Deep Learning Algorithm Trading, How CNN Models Data Like a Grid”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[121],"tags":[],"class_list":["post-35207","post","type-post","status-publish","format-standard","hentry","category-deep-learning-automated-trading"],"yoast_head":"\nMachine Learning and Deep Learning Algorithm Trading, How CNN Models Data Like a Grid - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/35207\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning and Deep Learning Algorithm Trading, How CNN Models Data Like a Grid - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"In recent years, machine learning and deep learning algorithms have made remarkable advancements in the financial markets. 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