Machine Learning Moving Average [BackQuant]

Machine Learning Moving Average [BackQuant]

A powerful tool combining clustering, pseudo-machine learning, and adaptive prediction, enabling traders to understand and react to price behavior across multiple market regimes (Bullish, Neutral, Bearish). This script uses a dynamic clustering approach based on percentile thresholds and calculates an adaptive moving average, ideal for forecasting price movements with enhanced confidence levels.

What is Percentile Clustering?
Percentile clustering is a method that sorts and categorizes data into distinct groups based on its statistical distribution. In this script, the clustering process relies on the percentile values of a composite feature (based on technical indicators like RSI, CCI, ATR, etc.). By identifying key thresholds (lower and upper percentiles), the script assigns each data point (price movement) to a cluster (Bullish, Neutral, or Bearish), based on its proximity to these thresholds.

This approach mimics aspects of machine learning, where we “train” the model on past price behavior to predict future movements. The key difference is that this is not true machine learning; rather, it uses data-driven statistical techniques to "cluster" the market into patterns.

Why Percentile Clustering is Useful

• Clustering price data into meaningful patterns (Bullish, Neutral, Bearish) helps traders visualize how price behavior can be grouped over time.
  
• By leveraging past price behavior and technical indicators, percentile clustering adapts dynamically to evolving market conditions.
  
• It helps you understand whether price behavior today aligns with past bullish or bearish trends, improving market context.
  
• Clusters can be used to predict upcoming market conditions by identifying regimes with high confidence, improving entry/exit timing.
  

What This Script Does

• Clustering Based on Percentiles: The script uses historical price data and various technical features to compute a "composite feature" for each bar. This feature is then sorted and clustered based on predefined percentile thresholds (e.g., 10th percentile for lower, 90th percentile for upper).
  
• Cluster-Based Prediction: Once clustered, the script uses a weighted average, cluster momentum, or regime transition model to predict future price behavior over a specified number of bars.
  
• Dynamic Moving Average: The script calculates a machine-learning-inspired moving average (MLMA) based on the current cluster, adjusting its behavior according to the cluster regime (Bullish, Neutral, Bearish).
  
• Adaptive Confidence Levels: Confidence in the predicted return is calculated based on the distance between the current value and the other clusters. The further it is from the next closest cluster, the higher the confidence.
  
• Visual Cluster Mapping: The script visually highlights different clusters on the chart with distinct colors for Bullish, Neutral, and Bearish regimes, and plots the MLMA line.
  
• Prediction Output: It projects the predicted price based on the selected method and shows both predicted price and confidence percentage for each prediction horizon.
  
• Trend Identification: Using the clustering output, the script colors the bars based on the current cluster to reflect whether the market is trending Bullish (green), Bearish (red), or is Neutral (gray).
  

How Traders Use It

• Predicting Price Movements: The script provides traders with an idea of where prices might go based on past market behavior. Traders can use this forecast for short-term and long-term predictions, guiding their trades.
  
• Clustering for Regime Analysis: Traders can identify whether the market is in a Bullish, Neutral, or Bearish regime, using that information to adjust trading strategies.
  
• Adaptive Moving Average for Trend Following: The adaptive moving average can be used as a trend-following indicator, helping traders stay in the market when it’s aligned with the current trend (Bullish or Bearish).
  
• Entry/Exit Strategy: By understanding the current cluster and its associated trend, traders can time entries and exits with higher precision, taking advantage of favorable conditions when the confidence in the predicted price is high.
  
• Confidence for Risk Management: The confidence level associated with the predicted returns allows traders to manage risk better. Higher confidence levels indicate stronger market conditions, which can lead to higher position sizes.
  

Pseudo Machine Learning Aspect
While the script does not use conventional machine learning models (e.g., neural networks or decision trees), it mimics certain aspects of machine learning in its approach. By using clustering and the dynamic adjustment of a moving average, the model learns from historical data to adjust predictions for future price behavior. The "learning" comes from how the script uses past price data (and technical indicators) to create patterns (clusters) and predict future market movements based on those patterns.

Why This Is Important for Traders

• Understanding market regimes helps to adjust trading strategies in a way that adapts to current market conditions.
  
• Forecasting price behavior provides an additional edge, enabling traders to time entries and exits based on predicted price movements.
  
• By leveraging the clustering technique, traders can separate noise from signal, improving the reliability of trading signals.
  
• The combination of clustering and predictive modeling in one tool reduces the complexity for traders, allowing them to focus on actionable insights rather than manual analysis.
  

How to Interpret the Output

• Bullish (Green) Zone: When the price behavior clusters into the Bullish zone, expect upward price movement. The MLMA line will help confirm if the trend remains upward.
  
• Bearish (Red) Zone: When the price behavior clusters into the Bearish zone, expect downward price movement. The MLMA line will assist in tracking any downward trends.
  
• Neutral (Gray) Zone: A neutral market condition signals indecision or range-bound behavior. The MLMA line can help track any potential breakouts or trend reversals.
  
• Predicted Price: The projected price is shown on the chart, based on the cluster's predicted behavior. This provides a useful reference for where the price might move in the near future.
  
• Prediction Confidence: The confidence percentage helps you gauge the reliability of the predicted price. A higher percentage indicates stronger market confidence in the forecasted move.
  

Tips for Use

• Combining with Other Indicators: Use the output of this indicator in combination with your existing strategy (e.g., RSI, MACD, or moving averages) to enhance signal accuracy.
  
• Position Sizing with Confidence: Increase position size when the prediction confidence is high, and decrease size when it’s low, based on the confidence interval.
  
• Regime-Based Strategy: Consider developing a multi-strategy approach where you use this tool for Bullish or Bearish regimes and a separate strategy for Neutral markets.
  
• Optimization: Adjust the lookback period and percentile settings to optimize the clustering algorithm based on your asset’s characteristics.
  

Conclusion
The Machine Learning Moving Average [BackQuant] offers a novel approach to price prediction by leveraging percentile clustering and a dynamically adapting moving average. While not a traditional machine learning model, this tool mimics the adaptive behavior of machine learning by adjusting to evolving market conditions, helping traders predict price movements and identify trends with improved confidence and accuracy.