Data Collection and Preprocessing — Feature Engineering (Creating Custom Features Relevant to Trading)
Introduction
Feature engineering is the process of creating new features or transforming existing ones to better capture relevant patterns in the data. In trading, effective feature engineering can help improve the performance and accuracy of neural network models. In this article, we will discuss various feature engineering techniques that can be applied to trading data to create custom features for use in neural network models.
Feature Engineering Techniques for Trading
Technical Indicators
Technical indicators are mathematical calculations based on historical price and volume data, used to analyze trends and make trading decisions. Creating features based on technical indicators can help neural networks identify patterns in the data that may be indicative of future price movements. Common technical indicators include:
- Moving averages (simple, exponential, weighted)
- Relative strength index (RSI)
- Moving average convergence/divergence (MACD)
- Bollinger Bands
- Stochastic oscillator
Lagged Variables
Lagged variables are created by shifting the data points of a time series by a specified number of periods. These variables can help capture the temporal dependencies in time series data and allow neural networks to learn from past observations. For example, you can create lagged variables for price, volume, or technical indicators to capture their historical values.
Price-Based Features
Price-based features are derived from the historical price data and can help capture various aspects of price movements. Examples of price-based features include:
- Price change (absolute or percentage)
- Price volatility (standard deviation or average true range)
- Price momentum (rate of change or price differences)
- Price patterns (candlestick patterns, chart patterns)
Sentiment Analysis
Sentiment analysis involves processing textual data, such as news articles, social media posts, or analyst reports, to gauge market sentiment. By extracting sentiment scores or other relevant information from text data, you can create features that help neural networks identify the impact of market sentiment on asset prices. Common sentiment analysis techniques include:
- Lexicon-based methods (using predefined dictionaries of positive and negative words)
- Machine learning-based methods (training classifiers on labeled text data)
- Deep learning-based methods (using pre-trained models like BERT or GPT for sentiment classification)
Macro-Economic Features
Macro-economic features capture the broader economic environment and can help neural networks account for external factors that influence asset prices. Examples of macro-economic features include:
- Interest rates
- Inflation rates
- Gross domestic product (GDP) growth
- Unemployment rates
- Economic policy announcements
Conclusion
Feature engineering plays a crucial role in improving the performance and accuracy of neural network models in trading applications. By creating custom features based on technical indicators, lagged variables, price-based factors, sentiment analysis, and macro-economic data, you can help your neural network models better capture the underlying patterns in the data. As you progress through the lessons, you will learn how to apply these feature engineering techniques to various trading applications, such as price prediction, trading signal generation, and portfolio optimization.
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