The Unsupervised Clustering Strategy is a powerful data-driven trading approach that uses machine learning to identify hidden patterns and groupings in financial market data. Unlike supervised learning models, which rely on labelled data to make predictions, unsupervised clustering identifies structures or groupings within data without predefined labels. This allows the strategy to identify market segments, trends, or anomalies that may not be immediately visible through traditional technical indicators.

In the context of trading, the Unsupervised Clustering Strategy helps traders uncover underlying relationships in market data, such as asset correlations, price patterns, and market sentiment, which can be used to make informed trading decisions.

What is Unsupervised Clustering?

Unsupervised clustering is a type of machine learning technique that divides data into clusters or groups based on similarities in the data. It works by identifying patterns and structures in the data without needing any labelled examples or target outcomes. This is in contrast to supervised learning, where the model is trained on labelled data to make predictions.

There are several popular unsupervised clustering techniques, with the most common being:

• K-means Clustering: A method that divides data into k clusters based on the mean distance between data points. Each data point is assigned to the nearest cluster, and the cluster’s centroid is updated iteratively until convergence.
• Hierarchical Clustering: A technique that builds a tree-like structure of data points and progressively merges or splits clusters. It is useful for understanding the relationships between clusters at different levels of granularity.
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise): A clustering algorithm that groups together closely packed data points and labels others as outliers, making it effective for identifying clusters of varying shapes.
• Gaussian Mixture Models (GMM): A probabilistic model that assumes the data is generated from a mixture of several Gaussian distributions. It can capture more complex patterns than K-means.

How the Unsupervised Clustering Strategy Works

The Unsupervised Clustering Strategy for trading involves using these clustering algorithms to identify patterns and clusters in financial data. The strategy can be applied to various forms of market data, including price data, technical indicators, order flow, or even sentiment data.

1. Data Preprocessing

Before applying clustering algorithms, data preprocessing is a crucial step. The data used for clustering may include:

• Price Data: Historical prices of assets such as stocks, forex, or commodities.
• Technical Indicators: Data derived from market indicators such as moving averages, RSI, or MACD.
• Sentiment Data: Market sentiment data from sources like news, social media, or analyst reports.
• Order Flow: Data on the flow of market orders, such as bid/ask spreads and order book depth.

The data needs to be cleaned and normalized (scaled to a common range) to ensure meaningful clustering results.

2. Clustering Assets or Market Behavior

The core of the Unsupervised Clustering Strategy is to identify groups of assets or behaviors that exhibit similar characteristics. This might involve:

• Clustering Assets Based on Correlation: Grouping assets that exhibit similar price movements or correlations. For example, commodities like gold and silver or forex pairs like EUR/USD and GBP/USD may show similar trends.
• Identifying Market Regimes: Clustering periods of market behavior into distinct market regimes. For example, market data might be clustered into bullish, bearish, or sideways periods based on price action and indicators.
• Cluster Identification for Trading Opportunities: Once clusters are identified, the strategy can detect outliers (assets or periods that deviate from the identified patterns), which might indicate opportunities for mean reversion, trend continuation, or breakout strategies.

3. Clustering-Based Entry and Exit Signals

Once the clusters are formed, the strategy can generate trading signals based on the following:

• Entry Signals: When an asset falls into a specific cluster (e.g., a “bullish cluster”), the strategy can trigger a buy signal. Conversely, when the asset is part of a bearish cluster, it can trigger a sell signal.
• Exit Signals: When an asset moves out of a cluster (e.g., a move from a bullish to a neutral cluster), the strategy can generate an exit signal. Similarly, when assets break away from a clustered behavior, it could signal that the current trend is ending.
• Cluster Transitions: The strategy can track cluster transitions and adjust positions accordingly. For example, if an asset moves from a low volatility cluster to a high volatility cluster, it may signal an opportunity to enter a trade.

4. Risk Management

The strategy can incorporate dynamic risk management based on the behavior of the identified clusters. For example:

• Volatility-Based Stop-Loss: Clusters associated with high volatility can trigger wider stop-loss levels, while low volatility clusters might use tighter stop-loss levels.
• Position Sizing: The strategy can adjust position size based on the volatility or trend strength within a given cluster. For example, larger positions may be taken during strong momentum periods and smaller positions in uncertain or low-volatility environments.

5. Backtesting and Model Evaluation

Once the unsupervised clustering strategy has been developed, it needs to be backtested across historical market data to evaluate its effectiveness. This step involves testing the clustering model’s ability to generate profitable buy and sell signals across different market conditions.

Example of the Unsupervised Clustering Strategy in Action

Let’s consider a forex trader using unsupervised clustering to analyze the EUR/USD and GBP/USD currency pairs.

1. Data Preprocessing: The trader gathers price data, RSI, MACD, and ADX for both pairs, and normalizes them for clustering.
2. Clustering: The trader applies K-means clustering to group the EUR/USD and GBP/USD data into clusters based on similarities in price movement and indicator behavior. The algorithm identifies two clusters:
   • Cluster 1: High correlation between EUR/USD and GBP/USD, indicating a strong trend.
   • Cluster 2: Low correlation between the two pairs, indicating market consolidation or divergence.
3. Trading Signals:
   • When the EUR/USD price enters Cluster 1, showing strong trend correlation with GBP/USD, the trader might enter a buy position.
   • When the EUR/USD moves to Cluster 2, indicating weak correlation with GBP/USD, the trader might exit the position or move to a sideways market strategy.
4. Risk Management: The trader uses volatility-adjusted stop-losses, based on the ATR of each cluster, to adjust risk according to the market’s behavior.

Advantages of the Unsupervised Clustering Strategy

• Identifies Hidden Patterns: The strategy can uncover hidden relationships or market regimes that may not be evident using traditional technical indicators.
• Adaptive to Market Changes: As market conditions change, the strategy automatically adjusts the parameters of clustering to reflect new patterns.
• Improved Signal Quality: By using clustering to group similar market behaviors, the strategy can reduce the number of false signals, improving the overall quality of trade decisions.
• Versatile: It can be applied to various types of market data, including price data, technical indicators, sentiment analysis, and more.

Limitations of the Unsupervised Clustering Strategy

• Data Dependency: The strategy relies heavily on the quality and quantity of the data used for clustering. Poor or insufficient data may lead to incorrect patterns and signals.
• Overfitting: If the clustering model is over-optimized or not generalized properly, it might perform well on historical data but fail in real-time conditions.
• Complexity: Unsupervised clustering techniques require expertise in machine learning and data analysis. Proper implementation, evaluation, and interpretation are key to success.
• Market Changes: Sudden changes in market behavior (such as black swan events) might not be captured by the clustering model if the model is not adapted frequently.

Tools and Technologies

• Machine Learning Libraries: Libraries like scikit-learn, TensorFlow, and Keras in Python are commonly used to implement clustering algorithms.
• Backtesting Platforms: Platforms like Backtrader, QuantConnect, and TradingView can be used to backtest unsupervised clustering strategies.
• Data Sources: Historical market data from sources like Yahoo Finance, Alpha Vantage, or Bloomberg is needed to build the clustering models.

Conclusion

The Unsupervised Clustering Strategy offers a sophisticated and adaptive approach to trading by identifying hidden patterns and relationships in market data without predefined labels. By grouping similar market behaviors and adjusting trading decisions accordingly, the strategy can improve the accuracy of trading signals and risk management. However, the strategy requires a deep understanding of machine learning and data analysis techniques and needs careful backtesting to ensure its effectiveness.

To learn more about how to implement Unsupervised Clustering Strategies, backtest them in real-world scenarios, and improve your trading decisions, enrol in the expert-led Trading Courses at Traders MBA.