AI Optimization 🧠
Note: This is the initial release (V1) of the AI Clustering feature. Ongoing development will introduce further refinements and capabilities.
AI Optimization
The AI Optimization feature is the most advanced component of Infinity Algo V3.0. It uses machine learning algorithms to continuously test thousands of setting combinations in the background, automatically identifying and applying the most profitable configuration for current market conditions.
How the AI Works
The AI follows a powerful three-step optimization cycle:
Simulate: Tests hundreds to thousands of parameter combinations within your chosen Sensitivity Range
Evaluate: Scores each combination using your selected Performance Metric
Apply: Automatically implements the best-performing configuration in real-time
AI Modes Available
AI Mode: Optimizes sensitivity and threshold parameters for adaptive signal generation
AI Sniper Mode: Combines AI optimization with precision entry algorithms for maximum accuracy
AI Settings Explained
🧠 Enable AI Optimization
Master switch for the AI engine. Must be enabled for AI and AI Sniper signal modes to function.
🧠 AI Optimization Mode
Determines how the AI performs optimization:
Walk-Forward: Continuously reselects the best configuration every N bars (defined by Update Frequency). Adapts to changing market conditions in real-time. Recommended for live trading.
Static (Full History): Optimizes once using the first 4900 bars of historical data, then locks that configuration permanently. Provides consistent backtesting results with proper train/test split. Recommended for strategy validation and backtesting.
Mode Selection Guide:
Live Trading
Walk-Forward
Adapts to market changes
Backtesting
Static
Consistent, reproducible results
Strategy Development
Static
Eliminates lookahead bias
Market Analysis
Walk-Forward
Shows adaptation patterns
🔄 AI Update Frequency (Bars)
Controls how often the AI recalculates optimal settings (Walk-Forward mode only).
Lower Values (1-100): More responsive but computationally intensive
Medium Values (200-1000): Balanced performance and adaptation
Higher Values (1000-5000): Stable, efficient, recommended for most users
Note: In Static mode, this setting is ignored as optimization occurs only once.
🧪 Sensitivity Range
Defines the parameter space the AI explores:
Auto: Full range (5-28) - Most comprehensive but slower
Very Fast (5-9): Ultra-responsive for scalping
Fast (10-14): Active day trading
Balanced (10-20): Recommended - Optimal for most strategies
Medium (15-21): Swing trading focus
Slow (22-28): Position trading and trend following
📈📉 AI Sim Long/Short TP %
Internal simulation parameters only - These define profit targets used by the AI to evaluate strategies during backtesting. They do NOT affect your actual trades or create real TP orders.
Default: 1.0% for both directions
Adjust based on your typical profit targets for more accurate optimization
📊 AI Performance Metric
Determines how the AI ranks and selects the best configuration:
Classic Metrics:
Total Profit: Raw cumulative profit
Average Profit: Profit per trade
Win Rate: Percentage of winning trades
GPR (Gross Profit Ratio): Profit efficiency ratio
GPR × √Trades: Balanced profit and trade count
Advanced Risk-Adjusted Metrics (New):
Sharpe Ratio: Return relative to volatility
Sortino Ratio: Return relative to downside risk
Calmar Ratio: Return relative to maximum drawdown
SQN (System Quality Number): Statistical system quality
Martin Ratio: Risk-adjusted return using Ulcer Index
Composite Metrics (New):
Sortino + Calmar Composite: Balanced risk-adjusted performance
Robust ML Score: Machine learning score resistant to outliers
Recommended Metric Selection:
Scalping
Win Rate or GPR × √Trades
Consistency matters most
Day Trading
Sharpe Ratio or Total Profit
Balance risk and return
Swing Trading
Sortino Ratio or Calmar Ratio
Downside protection important
Position Trading
Robust ML Score or Composite
Long-term stability
High Frequency
SQN or Average Profit
System quality crucial
Performance Considerations
Computational Limits
Maximum historical lookback: 4900-5000 bars
Processing time: Increases with lower Update Frequency (Walk-Forward mode)
Static mode: Single computation at bar 4900, then no further processing
Timeframe impact: Lower timeframes handle more complex calculations
Optimization Tips
Start Conservative: Use default settings (Static mode for testing, Walk-Forward for live)
Monitor Performance: Check the dashboard for current AI selections
Adjust Gradually: Make small changes to Update Frequency (Walk-Forward mode)
Match Your Style: Choose metrics aligned with your goals
Be Patient: AI needs 50+ bars minimum to establish patterns
Dashboard Features
When enabled, displays:
Current optimal sensitivity period
Selected threshold levels
Simulated performance metrics
Trade count and win rate
Configuration confidence score
Mode indicator: Shows "STATIC (LOCKED)", "OPTIMIZING", or "SIMULATED"
Troubleshooting
Indicator Timeout? → Use Static mode or increase Update Frequency
No AI Signals? → Ensure "Enable AI Optimization" is checked
Poor Performance? → Try different Performance Metrics or wider Sensitivity Range
Dashboard Not Showing? → Enable "Show Optimization Dashboard" in settings
Static Mode Not Working? → Ensure you have at least 5000 bars of historical data loaded
Summary
AI Optimization transforms Infinity Algo into a self-improving system that adapts to market conditions automatically. Choose Walk-Forward mode for live trading adaptation or Static mode for consistent backtesting. By selecting appropriate settings for your trading style and allowing the AI sufficient time to learn, you can achieve consistently optimized performance without manual parameter tuning.
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