Creation Date: October 7, 2023
Author: [Author Name]
1. Introduction
Due to the complexity and non-linearity of financial markets, it is difficult to build efficient trading strategies
using only traditional analytical methods. As a result, modern data analysis techniques like machine learning and
deep learning are gaining prominence. These technologies demonstrate powerful capabilities in processing vast
amounts of data and identifying specific patterns. This article will discuss the concepts of trading using
machine learning and deep learning algorithms, as well as the criteria for evaluating alternative data.
2. Understanding Machine Learning and Deep Learning
2.1 Machine Learning
Machine learning is a technology where algorithms learn from data to automatically build predictive models.
Generally, it is used to predict outputs based on given input data and is mainly classified into three
approaches: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
2.2 Deep Learning
Deep learning is a field of machine learning based on artificial neural networks (ANN).
It performs exceptionally well in solving non-linear problems through a multi-layer structure, and
is utilized in various fields such as image processing, natural language processing,
and time series forecasting.
3. Applications of Machine Learning and Deep Learning in Quantitative Trading
3.1 Data Collection and Processing
The first step in quantitative trading is to collect and process appropriate data.
The data that can be utilized includes price data, trading volume, financial statements, and alternative data.
Alternative data includes unstructured data forms such as news, social media feeds, and web crawling data.
3.2 Algorithm Modeling
Once the data is prepared, the next step is to select and train a machine learning or deep learning model.
Common machine learning algorithms include regression analysis, decision trees, random forests, and SVM,
while deep learning employs models such as LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network).
3.3 Model Evaluation and Tuning
To evaluate the performance of trading algorithms, various metrics must be utilized.
By analyzing metrics such as return, Sharpe ratio, and maximum drawdown, the effectiveness of the model can be assessed,
and optimization can be pursued through hyperparameter tuning.
4. Importance of Alternative Data and Evaluation Criteria
4.1 Definition and Importance of Alternative Data
Compared to traditional financial data, alternative data has become an essential resource that can aid in
predicting future market movements. For example, sentiment analysis of social media can help understand
investors’ moods, or web traffic data can provide signals of corporate growth.
4.2 Evaluation Criteria for Alternative Data
It is important to establish criteria for evaluating alternative data as follows:
- Reliability: Assess whether the data can be trusted by verifying its source and accuracy.
- Utility: Analyze whether the data can contribute to actual investment decisions.
- Timeliness: Since the response speed to market changes is crucial, evaluate how quickly the data is provided.
- Cost: Consider the impact of the costs incurred for data collection and processing on the final returns.
5. Case Studies: Trading Strategies Based on Machine Learning and Deep Learning
5.1 Case 1: Stock Price Prediction Using LSTM
The LSTM (Long Short-Term Memory) network is a deep learning model that shows strong performance in
time series data prediction. It can take multiple periods of stock price data as input and predict the next day’s stock price.
For example, to predict copper prices, the model can be trained using one year of data, then the model’s performance
can be evaluated using validation data and applied to actual trading.
5.2 Case 2: Pattern Recognition Using CNN
CNN (Convolutional Neural Network) is primarily used for image recognition, but it can also be applied
by converting time series data like stock price charts into images.
It can recognize chart patterns and generate trading signals based on those patterns.
6. Conclusion
Algorithmic trading utilizing machine learning and deep learning is a promising approach to enhance
competitiveness in the market. However, to build successful trading strategies, the quality of data,
the selection of algorithms, and appropriate evaluation criteria for alternative data are crucial.
Investors can leverage modern technologies to devise more sophisticated strategies and discover advantages
in the market.