Algorithmic trading in today’s financial markets is rapidly evolving, with machine learning and deep learning techniques receiving increasing attention. In particular, one-dimensional convolutional neural networks (1D CNNs) are establishing themselves as a powerful tool suited for time series data. This article will take a detailed look at the process of developing a trading strategy using autoregressive CNN with 1D convolution.
1. Overview of Machine Learning and Deep Learning
Machine learning is a set of algorithms that learn from data to make predictions and decisions. In contrast, deep learning is a method within machine learning that learns complex structures and patterns using artificial neural networks. These two techniques can be utilized in the financial market for various applications such as price prediction, trading signal generation, and risk management.
1.1 Differences Between Machine Learning and Deep Learning
Machine learning generally excels at processing structured data and operates in high-dimensional feature spaces. On the other hand, deep learning excels at processing unstructured data such as images and text. 1D CNN is optimized for time series data, effectively handling information such as stock prices and trading volumes.
1.2 Overview of Convolutional Neural Networks (CNN)
CNN is a network architecture widely used for image classification and recognition. The main components of a CNN include convolutional layers, activation layers, and pooling layers. The 1D CNN is a variation of this structure adapted to temporal characteristics, primarily used for extracting patterns from time series data.
2. Autoregressive Models
Autoregressive models (AR) are statistical methods that predict current values based on past observations. They are primarily used in time series data analysis and typically predict future values based on mathematical modeling.
2.1 Mathematical Definition of Autoregressive Models
An autoregressive model is expressed in the following form:
Y(t) = c + α_1 Y(t-1) + α_2 Y(t-2) + ... + αp Y(t-p) + ε(t)
Here, Y(t) is the current value of the time series data, c is a constant, α represents the regression coefficients, and ε(t) is the error term. This model explains the value at a given time t using the past p values.
3. Overview of 1D CNN
The 1D CNN is a neural network structure optimized for pattern recognition in time series data. Unlike the 2D structure of images, time series data relies solely on one axis (time), making it suitable for processing.
3.1 Structure of 1D CNN
The 1D CNN consists of:
- Input Layer: Receives time series data.
- Convolution Layer: Extracts local patterns from the input data.
- Activation Layer: Adds non-linearity to enhance the model’s expressiveness.
- Pooling Layer: Reduces dimensions and computation through down-sampling.
- Fully Connected Layer: Lightweight for the final predictions via the output layer.
4. Data Preparation
Preparing data for algorithmic trading is essential for the successful implementation of the model. Time series data can be collected based on various factors.
4.1 Data Collection
Data such as stock price information, trading volumes, and external infrastructure-related data need to be collected. Data can be gathered through various APIs such as Yahoo Finance and Alpha Vantage.
4.2 Data Preprocessing
Collected data typically requires pre-processing steps such as handling missing values, normalization, and scaling. This maximizes the learning effect of the model. Below is a simple preprocessing example:
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
data = pd.read_csv('stock_data.csv')
data.fillna(method='ffill', inplace=True)
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(data[['Close']])
5. Model Construction
Once the data is prepared, the 1D CNN model must be constructed. The Keras library can be utilized to easily build models. Below is a simple model construction example.
from keras.models import Sequential
from keras.layers import Conv1D, MaxPooling1D, Flatten, Dense
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=2, activation='relu', input_shape=(timesteps, features)))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(units=64, activation='relu'))
model.add(Dense(units=1, activation='linear'))
model.compile(optimizer='adam', loss='mean_squared_error')
5.1 Training
To train the model, training and validation datasets need to be split, and appropriate validation procedures must be performed.
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)
model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=100, batch_size=32)
6. Model Evaluation
Various metrics can be used to evaluate the performance of the trained model. Typically, it is common to use indicators such as RMSE and MSE.
from sklearn.metrics import mean_squared_error
predictions = model.predict(X_val)
mse = mean_squared_error(y_val, predictions)
rmse = mse ** 0.5
print(f'RMSE: {rmse}')
7. Implementation of Trading Strategies
Based on the results predicted by the model, trading strategies are implemented. The simplest approach is to identify peak and valley points to generate buy and sell signals.
def generate_signals(predictions):
signals = []
for i in range(1, len(predictions)):
if predictions[i] > predictions[i - 1]:
signals.append(1) # Buy
else:
signals.append(0) # Hold or Sell
return signals
signals = generate_signals(predictions)
8. Transition to a Real Trading System
If the model and trading strategy operate successfully, they can be transitioned to a real trading system. To do this, one must set up a system that can automatically execute orders using trading APIs.
import alpaca_trade_api as tradeapi
api = tradeapi.REST('APCA_API_KEY_ID', 'APCA_API_SECRET_KEY', base_url='https://paper-api.alpaca.markets')
api.submit_order(
symbol='AAPL',
qty=1,
side='buy',
type='market',
time_in_force='gtc'
)
9. Conclusion
The autoregressive model using 1D CNN is a useful tool for price prediction and trading strategy development in the financial markets. Through the processes of data preparation, model construction, model evaluation, and trading strategy implementation, one can build a more sophisticated and efficient trading system. However, since the market remains complex and uncertain, rigorous risk management and testing are always required.
Additionally, while this article has explained the basic concepts and implementation methods, it would also be beneficial to cover advanced topics for each stage in separate articles. This is because various elements such as data quality, hyperparameter tuning of the model, and diversification of trading strategies work together synergistically.