1. Introduction
In recent years, algorithmic trading has seen explosive growth in financial markets. Systems that automatically make trading decisions using machine learning and deep learning techniques are gaining much attention. This article will focus particularly on the noise reduction technique through autoencoders.
2. Basic Concepts of Machine Learning and Deep Learning
2.1 What is Machine Learning?
Machine learning is a technology that allows computers to learn from data to make predictions and decisions. This process is based on statistical methods and includes various types of learning methods such as supervised learning, unsupervised learning, and reinforcement learning.
2.2 Concept of Deep Learning
Deep learning is a field of machine learning that utilizes artificial neural networks, using a deep network structure with several layers to extract features from complex data. It has achieved success in various fields such as image recognition and natural language processing.
3. Basic Principles of Algorithmic Trading
Algorithmic trading is a method where automated computer programs execute trades according to specific algorithms (rules). It provides the advantage of quickly making investment decisions by recognizing market patterns through data analysis.
3.1 Algorithm Development Process
To develop an algorithm, it goes through several stages including data collection, model selection, training process, and monitoring. These processes are essential for successful trading.
4. What is an Autoencoder?
An autoencoder is an unsupervised learning model that learns to encode input data to a lower-dimensional representation and then reconstructs it. It is mainly used for data compression, feature learning, and noise reduction.
4.1 Structure of Autoencoder
An autoencoder consists of an encoder and a decoder, where the encoder compresses the input data, and the decoder reconstructs the compressed data into its original form.
4.2 Using Autoencoders for Noise Reduction
Financial data often contains noise, making it important to remove it. By using autoencoders to train on noisy data, clean data can be obtained through the reconstruction process.
5. Methodology for Noise Reduction Using Autoencoders
5.1 Data Preprocessing
To remove noise, data must first be collected and preprocessed as necessary.
5.2 Model Configuration
# Python Code Example
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, InputLayer
# Define Autoencoder Model
model = Sequential()
model.add(InputLayer(input_shape=(input_dim,)))
model.add(Dense(encoding_dim, activation='relu'))
model.add(Dense(input_dim, activation='sigmoid'))
model.compile(optimizer='adam', loss='mean_squared_error')
5.3 Model Training and Evaluation
After training the model, data is input to evaluate its quality. This allows for the assessment of noise reduction performance.
6. Conclusion
Machine learning and deep learning algorithms are very useful for automated trading in financial markets. Especially, the use of autoencoders for noise reduction enables more accurate predictions. Compared to previous methods, autoencoders can provide improved performance.
7. References
1. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning,” MIT Press, 2016.
2. Christopher M. Bishop, “Pattern Recognition and Machine Learning,” Springer, 2006.
3. “Deep Learning for Finance,” ResearchGate.