Stock trading has gained great popularity for decades, and investors have been striving to gain an edge in the market through various analytical methods. In recent years, the advancements in artificial intelligence (AI) and data science have brought attention to algorithmic trading using machine learning and deep learning. This course will cover the machine learning and deep learning algorithmic trading approaches and the macro-fundamental forecasting methodologies based on these techniques in detail.
1. Overview of Algorithmic Trading
Algorithmic trading is a method of executing trades automatically using computer programs. In this process, trade strategies, order input, management, and execution are automated. The main advantages of algorithmic trading are:
- Accurate execution: Since there is no human intervention, emotional judgments can be excluded.
- High-speed trading: Trades are executed very quickly due to the computational speed of computers.
- Backtesting: The validity of strategies can be tested using historical data.
2. Basics of Machine Learning and Deep Learning
Machine learning is a field of computer science that analyzes data to recognize patterns and make predictions. A branch of machine learning, deep learning, uses artificial neural networks and performs better on large datasets.
2.1 Types of Machine Learning
Machine learning can be broadly divided into the following three types:
- Supervised Learning: Learning is based on data that is already labeled. For instance, in stock price forecasting, past stock price data can be used to predict future prices.
- Unsupervised Learning: This method finds inherent patterns or structures in data without labels. Clustering is a representative example.
- Reinforcement Learning: An agent learns by interacting with the environment to maximize rewards. It is effective in learning stock trading strategies.
2.2 Structure of Deep Learning
Deep learning processes data through multiple layers of neural networks. Each node (neuron) in a layer receives input, performs a nonlinear transformation, and passes it to the next layer. This enables complex pattern recognition and prediction. Representative deep learning models include CNN, RNN, and LSTM.
3. Macro Fundamental Forecasting
Macro fundamental forecasting is a method of predicting financial market trends based on overall economic trends and indicators. This can assist in making long-term investment decisions.
3.1 Macro Economic Indicators
Key indicators to pay attention to in macro fundamental forecasting include:
- GDP (Gross Domestic Product): An indicator of the overall health of the economy, with growth rate changes being crucial.
- Unemployment Rate: Indicates the state of the labor market and is closely linked to economic activity.
- Consumer Price Index (CPI): An indicator of inflation, which can inform about purchasing power and consumer trends.
- Interest Rates: Change according to central bank monetary policy and significantly impact asset values in the market.
3.2 Data Collection and Preprocessing
Data is the foundation for predictions. Data must be collected from various sources (e.g., economic reports, government statistics, financial data APIs, etc.). Collected data needs to undergo the following preprocessing:
- Handling Missing Values: Addresses cases where necessary data for model training is missing.
- Normalization: Changes data with various scales to a common scale.
- Feature Engineering: Creates new variables (features) to enhance model performance.
4. Model Selection and Training
Choosing the right model among machine learning and deep learning models for macro fundamental forecasting is important. Here we will explore cases using various algorithms.
4.1 Model Selection Criteria
Model selection should be based on the following criteria:
- Data Characteristics: If data changes over time, RNN or LSTM models may be suitable.
- Complexity of the Task: A linear regression model may be useful for simple regression problems.
- Execution Time and Resource Constraints: Complex deep learning models require large datasets and fast computation.
4.2 Model Training
The following processes are necessary to train a model:
- Separate Training Data and Test Data: Split the data into training and validation to prevent overfitting.
- Hyperparameter Tuning: Adjust various parameters to optimize model performance.
- Ensemble Methods: Combine multiple models to derive more accurate predictions.
5. Performance Evaluation
Evaluating model performance is very important. Commonly used metrics include:
- Accuracy: The ratio of correctly predicted samples.
- F1 Score: The harmonic mean of precision and recall, useful for imbalanced datasets.
- RMSE (Root Mean Square Error): Indicates the difference between actual and predicted values.
6. Implementation and Feedback
Finally, the trained model is applied to actual trading. During this period, feedback on the model should be periodically collected based on market trends and conditions, and the performance of the model should be continuously monitored.
6.1 Risk Management
Managing risks in trading is essential. Some methods include:
- Adjusting Position Size: A diversified investment strategy where only a portion of the investment amount is used.
- Setting Stop-Loss: Automatically executing a sell when a certain loss occurs.
- Diversifying Across Asset Classes: Investing in various assets to reduce portfolio volatility.
6.2 Continuous Improvement
It is also important to continuously improve the model. Since the market is always changing, the model should be updated regularly and new data added to enhance performance. This process may include re-training the machine learning model or re-collecting data.
Conclusion
Algorithmic trading and macro fundamental forecasting based on machine learning and deep learning have become essential skills for modern traders. By analyzing past data and recognizing patterns, one can increase forecasting accuracy in the market. This course aims to provide an opportunity to understand and utilize the basics of algorithmic trading and various techniques. We hope to improve data-driven decision making in the complex financial market through AI and pave the way for successful trading. Thank you.