Pattern Recognition-Based Trading Strategy Using CNN
Due to the rapid price fluctuations and high trading volumes in cryptocurrencies, Bitcoin trading has become an attractive market for many investors and trading algorithms. In particular, algorithmic trading strategies that analyze past price patterns and predict future price movements using machine learning and deep learning technologies are gaining attention. This course will explain a trading strategy based on pattern recognition using Convolutional Neural Networks (CNN) and implement it through practical example code.
1. Understanding CNN and Deep Learning
Convolutional Neural Networks (CNN) are a deep learning architecture that demonstrates excellent performance in image recognition and vision-related tasks. CNNs can analyze multiple images using filters (or kernels) with the same weights to learn important features. Thanks to these characteristics, they can recognize patterns in chart images and support trading decisions based on them.
2. Use Cases of Deep Learning in Bitcoin Trading
Deep learning can be effectively used for data analysis and predictions in Bitcoin trading. It helps in making trading decisions through automatic exploration of data, pattern recognition, and predictive algorithms. CNN converts time-series data of Bitcoin price fluctuations (e.g., price recorded every hour) into images for training.
2.1 Data Collection
Bitcoin price data can be collected through various public APIs, among which the Binance API is widely utilized. The following example shows how to collect Bitcoin price data using Python from the Binance API.
import requests
import pandas as pd
import datetime
def fetch_binance_data(symbol='BTCUSDT', interval='1h', limit=1000):
url = f'https://api.binance.com/api/v3/klines?symbol={symbol}&interval={interval}&limit={limit}'
response = requests.get(url)
data = response.json()
df = pd.DataFrame(data, columns=['open_time', 'open', 'high', 'low', 'close', 'volume',
'close_time', 'quote_asset_volume', 'number_of_trades',
'taker_buy_volume', 'taker_buy_quote_asset_volume', 'ignore'])
df['open_time'] = pd.to_datetime(df['open_time'], unit='ms')
df['close'] = df['close'].astype(float)
return df[['open_time', 'close']]
btc_data = fetch_binance_data()
print(btc_data.head())2.2 Data Preprocessing and Image Generation
The collected price data needs to be transformed into a form suitable for CNN through a data preprocessing process. For example, additional features can be generated by calculating technical indicators like moving averages or Bollinger bands. Subsequently, the transformed data can be visualized as charts and saved as image files for use as input data for the CNN.
import matplotlib.pyplot as plt
import numpy as np
def plot_price_chart(data):
plt.figure(figsize=(10, 5))
plt.plot(data['open_time'], data['close'], label='Close Price', color='blue')
plt.title('Bitcoin Price Chart')
plt.xlabel('Time')
plt.ylabel('Price (USDT)')
plt.legend()
plt.grid()
plt.savefig('btc_price_chart.png')
plt.close()
plot_price_chart(btc_data)3. Building and Training the CNN Model
Now, the data needs to be structured for input into the CNN model. The TensorFlow/Keras library can be utilized to build and train the CNN model.
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# Defining the CNN model
def create_cnn_model():
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(64, 64, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(2, activation='softmax')) # Classifying into two classes (Buy/Sell)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
return model
cnn_model = create_cnn_model()
cnn_model.summary()3.1 Model Training
To train the images, data augmentation can be performed using ImageDataGenerator, and the model can be trained.
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical
# Custom function to load image data (assumption)
def load_images_and_labels():
# Logic to load images and labels
return images, labels
images, labels = load_images_and_labels()
X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2)
# One-hot encoding the labels
y_train = to_categorical(y_train, num_classes=2)
y_test = to_categorical(y_test, num_classes=2)
# Data augmentation setup
datagen = ImageDataGenerator(
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode='nearest')
# Training the model
cnn_model.fit(datagen.flow(X_train, y_train, batch_size=32),
validation_data=(X_test, y_test),
epochs=50) 4. Trading Decision and Strategy Implementation
Once the model has finished training, a strategy can be implemented to make Bitcoin trading decisions. Predictions can be made on new data, and buy or sell signals can be generated if they exceed or fall below a certain threshold.
def make_trade_decision(image):
# Transforming the image into the input shape for CNN
processed_image = preprocess_image(image)
prediction = cnn_model.predict(np.expand_dims(processed_image, axis=0))
return 'Buy' if prediction[0][0] > 0.5 else 'Sell'
latest_chart_image = 'latest_btc_price_chart.png'
decision = make_trade_decision(latest_chart_image)
print(f'Trade Decision: {decision}') 5. Conclusion
In this course, we explored how to implement an automatic Bitcoin trading strategy using deep learning and CNNs. Through the processes of data collection, preprocessing, and image generation, building and training the CNN model, and making trading decisions, we were able to execute the application of machine learning in trading. This process can be further developed into more sophisticated strategies by integrating various data and technical indicators.
Finally, there are always risks associated with Bitcoin trading, and as the model’s predictions are based on past data, a cautious approach is necessary.