With the development of algorithmic trading, machine learning and deep learning techniques are also being widely used in financial markets. In particular, as the importance of processing real-time data, such as tick data, increases, the need for data preprocessing, especially normalization, has become more pronounced. This article will explain in detail the methods of normalizing market data in trading utilizing machine learning and deep learning.
1. The Need for Normalization
Normalization plays a crucial role in adjusting the scale of the data to improve the learning and performance of machine learning algorithms. Financial data typically consists of various indicators such as prices, trading volumes, and returns, which can have different ranges.
For example, stock prices can range from thousands to hundreds of thousands, while trading volumes can differ from hundreds to thousands. Such differences can cause the model to overreact to specific features or ignore them entirely. Therefore, it is essential to normalize the data so that all input data can have the same scale.
2. Characteristics of Market Data
Market data is a dynamic system that changes over time. In particular, tick data is collected every time a transaction occurs for financial assets and includes information such as prices, execution times, and trading volumes. This data is used in the trading of various assets such as stocks, futures, and options and exhibits high volatility over time, as well as distinct seasonality or patterns.
Tick data typically possesses the following characteristics:
- Time Series Data: Tick data has a time-ordered time series format and possesses its own temporal correlation.
- Non-linearity: Asset prices are affected by various factors and may show non-linear changes.
- Autocorrelation: Past price data tends to influence future prices.
- High Frequency: Tick data is collected on a per-second basis, containing events that occur at a high frequency.
3. Data Normalization Techniques
The main techniques used for normalizing market data are as follows:
3.1. Min-Max Normalization
Min-Max normalization is a method that adjusts the range of the data to [0, 1] using the minimum (min) and maximum (max) values of the data. This method is effective when the data falls within a specific range. The formula is as follows:
X' = (X - min(X)) / (max(X) - min(X))For example, if the normalization of stock price data is performed using the Min-Max method, all values of the stock prices are converted to a range between 0 and 1, preventing the model from depending on specific values.
3.2. Z-score Normalization
Z-score normalization is a method that transforms data based on the mean and standard deviation of the data. This technique is useful when the distribution of the data follows a normal distribution. The formula is as follows:
X' = (X - μ) / σHere, μ is the mean of the data, and σ is the standard deviation. This method transforms the mean of the data to 0 and the standard deviation to 1, making all data comparable.
3.3. Robust Scaling
Robust scaling is a method for normalizing data using the median and interquartile range of each data point. This technique is particularly useful when there are outliers in the data. The formula is as follows:
X' = (X - median(X)) / IQR(X)Here, IQR refers to the difference between the 1st quartile (25%) and the 3rd quartile (75%). This method adjusts the scale of the data while minimizing the influence of outliers.
3.4. Log Transformation
Log transformation is a useful technique for reducing the scale of the data. It is applied to proportional data such as stock prices to make the distribution of the data closer to a normal distribution. The formula is as follows:
X' = log(X + 1)Log transformation is particularly effective in reducing the asymmetry of price or return data.
3.5. Choosing a Normalization Method
The choice of normalization method may vary depending on the characteristics of the data, the requirements of the algorithm, and the goals of the model. Generally, Min-Max normalization is widely used in non-linear models such as neural networks, while Z-score normalization may be more effective in statistical methods such as linear regression. Robust scaling is useful for addressing problems sensitive to outliers.
4. Market Data Normalization Process
The process of normalizing market data includes the following steps:
4.1. Data Collection
The first thing to do is to collect the necessary tick data. This can be done by using APIs or directly requesting information from databases. The data is typically stored in a pandas DataFrame format.
4.2. Data Exploration and Preprocessing
Explore the collected data to check for missing values, outliers, and the distribution of the data. In this step, transformations may be performed as needed to match the scale of the data. Tasks such as removing unnecessary columns and converting date formats are conducted.
4.3. Applying Normalization
After selecting the normalization technique, apply it to the data. This transforms all data to the same scale, optimizing the performance of machine learning models. Typically, tools such as `MinMaxScaler`, `StandardScaler`, and `RobustScaler` from the sklearn library can be utilized.
from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
# Min-Max normalization
scaler = MinMaxScaler()
data_normalized = scaler.fit_transform(data)
# Z-score normalization
scaler = StandardScaler()
data_normalized = scaler.fit_transform(data)
# Robust scaler
scaler = RobustScaler()
data_normalized = scaler.fit_transform(data)
4.4. Model Training
Train the machine learning model using the normalized data. In this step, it is also important to conduct cross-validation to evaluate performance and adjust the model’s parameters.
4.5. Result Analysis and Improvement
After measuring model performance, analyze the results and adjust preprocessing methods or normalization techniques as needed. Data normalization can be an iterative process, and it is essential to continuously improve the model’s performance.
5. Conclusion and Future Research Directions
Normalizing tick data collected from the market is essential for improving the performance of machine learning and deep learning models. This article has described various normalization techniques and covered the data preprocessing process and model training methods through these techniques. Future research should explore normalization methods for more complex datasets and algorithms, aiming to enhance the model’s generalization ability.
Additionally, as data complexity increases, the development of automated data preprocessing and normalization solutions becomes essential. It is also worth considering normalization methods utilizing machine learning techniques. This approach can enhance efficiency in financial markets and optimize risk management and investment strategy design.