AI Optimization 🧠
Note: This is the initial release (V1) of the AI Clustering feature. Ongoing development will introduce further refinements and capabilities.
The AI Optimization feature is the most advanced component of Infinity Algo. It uses clustering algorithms to continuously test thousands of setting combinations in the background, automatically identifying and applying the most profitable strategy for the current market environment.
How the AI Works The AI follows a simple yet powerful three-step process:
Simulate: On every AI Update Frequency cycle, the AI simulates trades for every possible combination of settings within your chosen Sensitivity Range.
Evaluate: It scores each combination's performance based on the AI Performance Metric you select (e.g., Profit Factor, Win Rate).
Apply: The AI identifies the single best-performing combination and automatically applies those settings to the live signals on your chart.
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 🧠:
The master switch to activate or deactivate the entire AI engine. It is highly recommended to keep this enabled to leverage the full power of the indicator.
AI Update Frequency (Bars) 🔄:
Defines how often (in bars) the AI reruns its optimization process.
Lower Value (e.g., 10): More responsive to market changes, but has a higher risk of causing a script timeout error on TradingView due to heavy calculations.
Higher Value (e.g., 100): More stable and less resource-intensive, but adapts more slowly to changes in the market.
Recommendation: The default of 50 is a safe balance for most users.
Sensitivity Range 🧪:
Specifies the range of sensitivity periods the AI will test. Limiting this range can speed up calculations.
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 📋:
Displays a dashboard on your chart showing the AI's currently selected optimal settings and its simulated performance.
Dashboard Location 📍:
Adjusts dashboard placement on the chart (Top Right, Bottom Right, Bottom Left, Top Left).
AI Target Long TP % 📈 & AI Target Short TP % 📉:
These define the take-profit percentages used internally by the AI only to evaluate which strategies are most profitable during its simulations.
Crucially, these settings are for internal testing only and do not affect your actual trade exits or place live take-profit orders.
AI Performance Metric 📊:
This tells the AI what "best" means. Select the metric that aligns with your trading goals.
Profit Factor \* Sqrt(Trades): A balanced approach favoring both profitability and a healthy number of trades.
Average Profit: Select this to find strategies that yield the highest profit per trade.
Win Rate: Select this to prioritize strategies that win most often, even if individual profits are smaller.
Total Profit: A straightforward metric to maximize the cumulative profit over the test period.
Profit Factor: Focuses purely on the highest gain-to-loss ratio, seeking maximum efficiency.
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.
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