Simple Decesion Matrix Classification Algorithm [SS]

In the world of algorithmic trading, machine learning models have become an increasingly popular way to analyze markets, predict outcomes, and design rule-based trading strategies. While Python and R are often the go-to tools for such advanced modeling, traders on TradingView are finding creative ways to bring these ideas into Pine Script®.

One such innovation is the Simple Decision Matrix Classification Algorithm, an indicator inspired by Random Forest modeling and other machine learning classification tasks (MLCTs). Although not a fully robust Random Forest implementation, this model cleverly adapts the concept of decision matrices and voting systems to trading within the limits of Pine Script.

This article will break down how the indicator works, why it matters, and how traders can use it effectively in their own strategies.

What is the Simple Decision Matrix Classification Algorithm?

The Simple Decision Matrix Classification Algorithm is an experimental TradingView indicator designed to classify trading outcomes (such as bullish/bearish signals or ATR breakouts) using a lightweight structure inspired by Random Forests.

Unlike traditional hardcoded strategies, this algorithm uses a “voting system” across multiple independent variables to determine whether a condition (e.g., a breakout or reversal) is likely.

Think of it as a simplified machine learning classifier that operates inside Pine Script. Instead of manually coding dozens of rules, the model compares new data to historical data, creates small decision nodes, and then lets them “vote” on the most probable outcome.

Inspiration from Machine Learning Models

The indicator borrows ideas from several machine learning algorithms:

• Random Forest (RF): Uses multiple decision trees to “vote” on predictions.
• Gradient Boosted Machines (GBM): Improves prediction accuracy by weighting decision trees.
• Support Vector Machines (SVM): A classic classification algorithm, though less similar in this case.

The most relevant inspiration is from Random Forests, where multiple decision nodes work together to classify outcomes. While Pine Script can’t handle full Random Forest complexity, this indicator simplifies the concept into a practical trading tool.

How the Model Works

At its core, the Decision Matrix Model operates as a binary classification system. Every decision must end in one of two possible outcomes:

• 0 = No / Bearish / No breakout
• 1 = Yes / Bullish / Breakout likely

Here’s how the process unfolds:

1. Input Variables (Features):
   • Traders choose two independent variables, such as RSI, Z-Score, Stochastic, MFI, EMAs, SMAs, or Volume.
   • These indicators serve as the “features” that guide the prediction.
2. Confusion Matrix Creation:
   • Historical values of the chosen variables are compared to current values.
   • A confusion matrix is built to prevent overfitting and introduce randomness (or “confusion”).
3. Node Voting:
   • Each independent variable casts a vote (0 or 1).
   • For example, RSI might predict “Yes” (breakout), while Z-Score predicts “No”.
4. Final Vote (Decision Matrix):
   • A third, cumulative node tallies the votes.
   • If both indicators agree, the prediction is stronger. If they disagree, the weighted impact determines the final outcome.

This structured voting mechanism makes the model transparent, flexible, and adaptable to different market conditions.

Example: Predicting ATR Breakouts

Let’s walk through a practical use case of the indicator: ATR (Average True Range) Breakout Prediction.

• Classification Task: Predict whether price will exceed the ATR range (breakout or breakdown).
• Independent Variables: RSI + Z-Score.
• Voting Process:
  • RSI node checks momentum → votes “Yes” (breakout likely).
  • Z-Score node checks deviation → votes “Yes”.
  • Final vote = 1 → Breakout expected.

This prediction doesn’t specify direction (bullish or bearish) but highlights high-volatility conditions, which is crucial for breakout traders.

Weighted Impact and Accuracy

One of the most valuable parts of this model is the data table component, which breaks down performance metrics for each variable:

• Weighted Impact (%): Shows how much influence each variable has in the final vote.
• Independent Accuracy: Displays how well each variable performs individually in predicting outcomes.
• Node Votes: Shows whether each variable voted “Yes” or “No” for the current condition.
• Final Verdict: The cumulative vote that produces the final prediction.

Example:

• RSI → 87% accuracy, Weighted Impact = 45%.
• Z-Score → 82% accuracy, Weighted Impact = 55%.
• Final Prediction → Bullish Breakout Likely.

This transparency allows traders to validate the reliability of the model rather than relying on a black-box indicator.

Built-in Classification Tasks

The algorithm comes with three ready-to-use prediction modes:

1. Predicting Candle Color (Red/Green).
2. Predicting Bullish or Bearish ATR Exceedance.
3. Predicting Breakouts from ATR Range.

Each mode can be customized with different independent variables, making it versatile for short-term scalping, swing trading, or even backtesting long-term strategies.

Key Features of the Indicator

• Binary Classification: Every prediction is a clear Yes/No decision.
• Three Node Voting System: Two independent variable votes + one cumulative final vote.
• Customizable Inputs: Choose from momentum, volatility, and volume-based indicators.
• Weighted Impact Analysis: Transparent influence of each feature.
• Independent Accuracy Metrics: Know how reliable each variable is.
• Diagrammatic Display: Visualizes voting outcomes and predictions directly on the chart.
• Lightweight: Designed to work within Pine Script’s execution limits.

Advantages Over Traditional Trading Indicators

Unlike single-indicator strategies, the Decision Matrix Algorithm combines multiple signals into a structured decision process. Benefits include:

• Reduced Noise: Voting reduces false signals from individual indicators.
• Transparency: Traders can see exactly how predictions are made.
• Flexibility: Works with different input variables and classification tasks.
• Machine Learning Inspiration: Brings advanced concepts into Pine Script without requiring external data.

Limitations and Considerations

While this model is innovative, traders should understand its limitations:

• Not a Full Random Forest: It’s a simplified version, not a true ensemble learning model.
• Binary Only: Predictions are limited to 0/1 outcomes.
• Two Variable Limit: Can only process two independent variables to stay within Pine Script’s constraints.
• ATR Breakout Direction: Cannot distinguish bullish vs bearish breakouts, only that volatility is coming.

Despite these, the tool is highly effective as a proof-of-concept and can be expanded by creative Pine developers.

Implementation in Pine Script

For Pine Script developers, the algorithm consists of three key functions:

1. Confusion Matrix Builder – Organizes historical input data for comparison.
2. Voting Functions – Assigns votes to each independent variable.
3. Final Classification Function – Aggregates votes and produces the prediction.

Developers can modify these functions to:

• Add more independent variables.
• Introduce weighting schemes.
• Apply to other classification tasks beyond ATR.

Real-World Applications

Here are a few practical ways traders can use this indicator:

1. Breakout Trading: Predict when price is about to exceed ATR.
2. Trend Continuation: Use RSI + EMA crossover as inputs to forecast bullish or bearish continuation.
3. Scalping Strategies: Predict next candle direction for short-term trades.
4. Volatility Filters: Identify periods of high volatility to avoid risky entries.

Why This Matters for Traders

The Simple Decision Matrix Classification Algorithm bridges the gap between machine learning and retail trading tools. It demonstrates how ideas from data science (like Random Forests and confusion matrices) can be applied in a lightweight way on platforms like TradingView.

For traders, this means:

• More structured decision-making.
• Greater confidence in signals.
• The ability to test, validate, and refine predictions using transparent data.

Conclusion

The Simple Decision Matrix Classification Algorithm is a game-changer for traders who want to bring machine learning concepts into Pine Script. By using a structured voting system, confusion matrices, and feature importance metrics, this model offers a transparent, customizable, and effective way to classify market conditions.

While not a full Random Forest, it is an excellent educational tool and practical trading aid, showing that machine learning-inspired logic can thrive even in the resource-limited environment of TradingView.

If you are a trader or Pine Script developer looking to explore next-level trading indicators, this model is an excellent place to start.

FAQs

Q1. Is this a full Random Forest model?
No, it’s a simplified version inspired by Random Forests. It uses a decision matrix and voting system to classify outcomes.

Q2. Can I use more than two independent variables?
Not in the current implementation. Pine Script limits prevent using more than two to keep the code lightweight.

Q3. Does the model predict breakout direction?
No, the ATR breakout classification only predicts whether a breakout will occur, not its bullish or bearish direction.

Q4. Which indicators can I use as inputs?
You can choose from RSI, Z-Score, MFI, Stochastics, EMAs, SMAs, and Volume.

Q5. Can developers extend the model?
Yes! The code can be customized to include additional features, conditions, and classification tasks.