AI Clustering 🧠

Note: This is the initial release (V1) of the AI Clustering feature. Ongoing development will introduce further refinements and capabilities.

The AI Clustering feature in Infinity Algo utilizes advanced artificial intelligence techniques, specifically clustering algorithms, to dynamically optimize indicator settings. This enhances trading signal accuracy by continuously adapting to real-time market conditions.

How AI Clustering Works:

Infinity Algo’s AI employs sophisticated clustering methods to analyze historical and current market data, categorizing it into distinct groups or "clusters" based on volatility, trends, and price movements. By recognizing these clusters, the AI identifies which combination of sensitivity settings and parameters are most effective under specific market scenarios, automatically adjusting your trading strategy to match optimal conditions.

Advanced Features and Limitations:

• Dynamic Adaptation: The AI continuously recalculates and adjusts indicator settings as market conditions evolve, ensuring optimal performance.

• Pattern Recognition: Advanced algorithms detect subtle market patterns that human analysis might overlook, improving decision-making.

Limitations:

• Historical Data Constraint: AI clustering analysis is limited to recent historical data available on TradingView, typically restricted to certain bars back, potentially affecting its ability to capture longer-term market cycles.

• Performance Impact: If the AI Update Frequency is set too low (frequent updates), TradingView may experience computational overload, causing the indicator to timeout or slow down significantly.

AI Clustering Settings Explained:

• Enable AI Optimization 🧠:

  • Activates or deactivates the AI-driven optimization process.

  • Recommended to keep enabled for automatic performance improvements.

• AI Update Frequency (Bars) 🔄:

  • Defines how frequently the AI recalculates optimal settings.

  • Lower values adapt faster but risk computational timeout on TradingView.

  • Higher values are more stable but less responsive to rapid market changes.

  • Default recommendation: Start at "50" to balance responsiveness and performance.

• Sensitivity Range 🧪:

  • Specifies the range of sensitivity periods evaluated by the AI:

    • Auto: Evaluates the entire range (5-28 periods).

    • Very Fast (5-9): Quick responses, ideal for short-term traders.

    • Fast (10-14): Moderately fast, good for balancing signal frequency and accuracy.

    • Balanced (10-20): Default, suitable for general trading scenarios.

    • Medium (15-21): Moderate pace, good for swing trading.

    • Slow (22-28): Best for long-term trend-following.

• Show Optimization Dashboard 📋:

  • Activates a dashboard displaying AI-chosen optimal settings directly on your chart.

• Dashboard Location 📍:

  • Adjusts dashboard placement on the chart (Top Right, Bottom Right, Bottom Left, Top Left).

• AI Target Long TP % 📈 & AI Target Short TP % 📉:

  • Defines take-profit percentages used internally by the AI to evaluate strategy performance during simulations.

  • Important: Does not affect actual trade exits; only used for internal optimization.

• AI Performance Metric 📊:

  • Selects the performance metric AI uses to determine optimal settings:

    • Profit Factor * Sqrt(Trades): Balances profitability and trade frequency.

    • Average Profit: Maximizes the average profit per trade.

    • Win Rate: Maximizes winning trade percentage.

    • Total Profit: Maximizes cumulative profit.

    • Profit Factor: Focuses on the highest gain-to-loss ratio.

Recommended Usage:

• Always enable AI optimization for adaptive, dynamic trading.

• Choose AI Update Frequency and Sensitivity Range carefully to balance performance and system responsiveness.

• Select the AI Performance Metric that aligns best with your trading objectives.

Leveraging AI Clustering allows you to maintain a highly adaptive strategy, ensuring consistent alignment with real-time market dynamics while accounting for computational limitations.