Quantitative trading, or algorithm-based investing strategies, has rapidly developed in recent years, and machine learning (ML) and deep learning (DL) technologies are further accelerating this progress. However, the success of algorithmic trading largely depends on the characteristics of the data, particularly whether the data is stationary. This article will delve deeply into algorithmic trading using machine learning and deep learning, covering the basics, stationarity diagnosis, and methods for recovering from non-stationarity.
1. Difference between Machine Learning and Deep Learning
First, it is important to understand the basic concepts of machine learning and deep learning. Machine learning is a set of algorithms that analyze data and learn patterns. In contrast, deep learning is a subset of machine learning that can learn more complex patterns in data through artificial neural networks. Deep learning has particularly stood out in areas such as image recognition, speech recognition, and natural language processing, and its applicability in algorithmic trading is increasing.
2. Basic Concept of Algorithmic Trading
Algorithmic trading is the automation of the investment decision-making process. This involves collecting market data, generating trading signals based on this data, and then executing orders automatically, primarily consisting of the following elements:
- Data Collection: Various data such as stock prices, trading volume, and news are collected.
- Signal Generation: Trading signals are generated based on the collected data.
- Order Execution: Orders are executed automatically according to the generated signals.
3. Stationarity and Non-stationarity of Data
Stationarity and non-stationarity are concepts that describe the statistical properties of data over time. Stationarity refers to a state where the mean and variance remain constant over time. In contrast, non-stationarity refers to a state where the mean or variance changes over time. In algorithmic trading, non-stationary data often occurs, and failure to account for this can result in generating erroneous trading signals. Therefore, diagnosing and recovering stationarity is essential.
4. Methods for Diagnosing Stationarity
Several statistical methods are used to diagnose stationarity. The most widely used methods are as follows:
4.1. Visual Diagnosis
Visually inspecting the data is the first step in diagnosing its stationarity. Time series data is plotted to observe changes in mean and variance. Stationary data generally maintains a constant mean and variance without clear patterns.
4.2. ADF Test
The Augmented Dickey-Fuller (ADF) test is a statistical method to verify stationarity. This test helps determine whether a given time series data is stationary. The basic method for performing the ADF test is as follows:
from statsmodels.tsa.stattools import adfuller
result = adfuller(data['price'])
print('ADF Statistic:', result[0])
print('p-value:', result[1])4.3. KPSS Test
The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test is another method for checking the stationarity of time series data. In contrast to the ADF test, the KPSS test verifies the hypothesis that the data is non-stationary. The method for conducting the KPSS test is as follows:
from statsmodels.tsa.stattools import kpss
result = kpss(data['price'])
print('KPSS Statistic:', result[0])
print('p-value:', result[1])5. Recovering from Non-stationarity
Several techniques are necessary to revert non-stationary data to stationary. This process typically involves data transformations.
5.1. Differencing
Differencing is a fundamental method generally used to remove non-stationarity. It involves subtracting the previous value from the current value, resulting in the differenced data which may be stationary. The first difference is expressed as follows:
data['price_diff'] = data['price'].diff()5.2. Log Transformation
Log transformation is useful for stabilizing the variance of the data. When the data increases or decreases exponentially, log transformation can help address stationarity issues:
data['price_log'] = np.log(data['price'])5.3. Square Root Transformation
Square root transformation is also useful in reducing variance imbalance, especially effective when the values of the data are large:
data['price_sqrt'] = np.sqrt(data['price'])6. Utilizing Machine Learning and Deep Learning Models
Once the stationarity diagnosis and recovery processes are completed, trading strategies can be built using machine learning and deep learning algorithms. Among various algorithms, we will highlight Random Forest, SVM, and LSTM.
6.1. Random Forest
Random Forest is an ensemble learning algorithm that combines multiple decision trees, useful for handling non-stationary datasets. The final prediction value is generated by averaging the prediction results of each tree.
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
predictions = model.predict(X_test)6.2. Support Vector Machine (SVM)
SVM is a model that uses hyperplanes to classify by finding patterns in the data, particularly advantageous for linear separations.
from sklearn.svm import SVC
model = SVC(kernel='linear')
model.fit(X_train, y_train)
predictions = model.predict(X_test)6.3. Long Short-Term Memory (LSTM)
LSTM is a type of RNN that is suitable for time series data prediction architecture. LSTM stores past data in memory cells and predicts future values based on this.
from keras.models import Sequential
from keras.layers import LSTM, Dense
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mean_squared_error')7. Conclusion
Machine learning and deep learning have the potential to revolutionize current algorithmic trading. The processes of diagnosing stationarity and recovering from non-stationarity form the basis of it all, allowing for the development of more stable and reliable trading strategies. I hope this article assists you on your quantitative trading journey.