# 🧠 AI Optimization Transform Infinity Algo into a self-improving system that adapts to markets automatically. {% hint style="success" %} **Quick Start:** Enable AI β†’ Select mode (Static/Walk-Forward) β†’ Choose metric β†’ Let AI optimize {% endhint %} *** #### πŸš€ Quick Setup {% stepper %} {% step %} **Enable AI** Turn ON `🧠 Enable AI Optimization` in settings {% endstep %} {% step %} **Choose Mode** * **Backtesting?** β†’ `Static (Full History)` * **Live Trading?** β†’ `Walk-Forward` {% endstep %} {% step %} **Select Signal Type** Choose `AI` or `AI Sniper` in Signal Mode {% endstep %} {% endstepper %} **That's it!** Default settings work for most users. *** #### 🎯 How AI Works | 1️⃣ **Simulate** | 2️⃣ **Evaluate** | 3️⃣ **Apply** | | ------------------------------------------ | ----------------------------- | ----------------------------- | | Tests 100s-1000s of parameter combinations | Scores each using your metric | Implements best configuration | **Walk-Forward:** Periodically re-optimizes on a rolling in-sample window and validates out-of-sample, reducing overfitting. *** #### βš™οΈ Core Settings {% tabs %} {% tab title="🧠 AI Modes" %} **Optimization Modes** | Mode | How It Works | Use For | | ---------------- | --------------------- | ------------ | | **Walk-Forward** | Updates every N bars | Live trading | | **Static** | Optimizes once, locks | Backtesting | {% hint style="info" %} **Tip:** Start with Static for testing, switch to Walk-Forward for live {% endhint %} {% endtab %} {% tab title="πŸ”„ Update Frequency" %} **Walk-Forward Only** {% code title="frequency-guide.txt" %} ``` 100 bars (default) β†’ Ultra-responsive (high CPU) 200-1000 bars β†’ Balanced βœ… 1000-5000 bars β†’ Very stable, slower to adapt Examples on 1h chart: - 100 bars = ~4 days - 1000 bars = ~42 days - 5000 bars = ~208 days ``` {% endcode %} Lower = More responsive but intensive | Higher = More stable and efficient {% endtab %} {% tab title="πŸ§ͺ Sensitivity Range" %} **Parameter Space** | Range | Values | Best For | | -------------- | ------ | ---------------- | | **Very Fast** | 5-9 | Scalping | | **Fast** | 10-14 | Day Trading | | **Balanced** βœ… | 10-20 | Most Strategies | | **Medium** | 15-21 | Swing Trading | | **Slow** | 22-28 | Position Trading | | **Auto** | 5-28 | Full exploration | | {% endtab %} | | | {% tab title="πŸ“Š Optimization Metric" %} **Choose Your Goal** **Quick Selection:** | Your Style | Use This Metric | Why | | --------------- | --------------- | ------------------- | | **Scalping** | Win Rate | Consistency matters | | **Day Trading** | Sharpe Ratio | Balance risk/return | | **Swing** | Sortino Ratio | Downside protection | | **Position** | Calmar Ratio | Avoid drawdowns | **All Available Metrics:** * **Classic:** Total Profit, Win Rate, Average P\&L, Gain-to-Pain * **Risk-Adjusted:** Sharpe, Sortino, Calmar, Martin * **Advanced:** SQN (System Quality Number), Robust ML Score {% hint style="warning" %} **Important:** High win rate β‰  profitability. A 90% win rate with large losses can be unprofitable. {% endhint %} {% hint style="info" %} Not sure? Use **Total Profit** for testing, **Sharpe Ratio** for live trading {% endhint %} {% endtab %} {% endtabs %} *** #### πŸ“ˆ Simulation Settings **AI Sim TP% (Testing Only)** {% hint style="warning" %} **Note:** These are internal simulation parameters - they do NOT create real orders {% endhint %} {% columns %} {% column width="60%" %} **What they do:** * Help AI evaluate strategies * Set internal profit targets * Default: 1.0% both directions {% endcolumn %} {% column %} {% code title="sim-settings:" %} ``` Long TP: 1.0% Short TP: 1.0% Purpose: AI testing only Real trades: Not affected ``` {% endcode %} {% endcolumn %} {% endcolumns %} *** #### πŸ“Š Dashboard Display **Live Monitoring** When enabled, see: * βœ… Current optimal sensitivity * βœ… Selected thresholds * βœ… Win rate & metrics * βœ… Confidence score * βœ… Mode status **Status Indicators:** * `STATIC (LOCKED)` - One-time optimization complete * `OPTIMIZING` - Currently calculating * `SIMULATED` - Results ready
*** #### πŸ’‘ Best Practices {% tabs %} {% tab title="🎯 Getting Started" %} 1. Use **Static** for initial testing 2. Select **Balanced** sensitivity 3. Default 100 bar frequency 4. Match metric to goals 5. **Walk-Forward needs \~535 bars** for first optimization 6. **Static needs \~5000 bars** total {% endtab %} {% tab title="⚑ Optimization" %} * **Lower timeframes** β†’ Complex calculations * **Monitor dashboard** β†’ Track selections * **Small adjustments** β†’ Better results * **Patience required** β†’ AI needs time {% endtab %} {% tab title="πŸ“ˆ Performance" %} **Limits:** * Max lookback: 5000 bars * Lower frequency = Higher CPU * Static = One calculation only at bar 4900 * Higher TF = Better performance {% endtab %} {% endtabs %} *** #### πŸ”§ Troubleshooting | Problem | Solution | | ---------------- | -------------------------------- | | **Timeout** | Use Static or increase frequency | | **No signals** | Check AI Optimization is ON | | **Poor results** | Try different metric/range | | **No dashboard** | Enable in settings | | **Static fails** | Need 5000+ bars data | *** #### ⚑ Quick Reference {% columns %} {% column width="50%" %} **For Testing** * Mode: `Static` * Range: `Balanced` * Metric: `Total Profit` * Frequency: N/A * Min bars: 5000 {% endcolumn %} {% column %} **For Live Trading** * Mode: `Walk-Forward` * Range: `Balanced` * Metric: Your preference * Frequency: `100` (default) * Min bars: 535 {% endcolumn %} {% endcolumns %} *** #### πŸ“š Understanding Performance Metrics
Detailed Metric Explanations {% hint style="info" %} **Note:** Infinity Algo computes metrics on **per-trade returns** with risk-free rate and MAR = 0. Industry definitions typically use time-series (daily/monthly) returns. {% endhint %} **Classic Metrics** | Metric | Formula | Best For | | ---------------- | ------------------------- | ------------------- | | **Total Profit** | Sum of all P\&L | Quick assessment | | **Win Rate** | Wins Γ· Total trades Γ— 100 | Consistency check | | **Average P\&L** | Total P\&L Γ· Trades | Trade quality | | **Gain-to-Pain** | Ξ£ gains / \|Ξ£ losses\| | Risk/reward balance | *** **Risk-Adjusted Metrics** **Sharpe Ratio - Industry Standard** * **Formula:** Excess return (over risk-free) Γ· Standard deviation * **Infinity Algo:** Uses risk-free = 0 * **Pros:** Most widely used, easy comparison, considers total volatility * **Cons:** Penalizes upside volatility, assumes normal distribution * **Benchmarks:** \~1 = Good | \~2 = Very good | 3+ = Outstanding **Sortino Ratio - Downside Focus** * **Formula:** Excess return (over target/MAR) Γ· Downside deviation * **Infinity Algo:** Uses MAR = 0 * **Pros:** Only penalizes bad volatility, better for trend following * **Cons:** Requires defining target return, less standardized * **Benchmarks:** >1 = Good | >2 = Very good | >3 = Excellent **Calmar Ratio - Drawdown Protection** * **Formula:** CAGR Γ· Maximum drawdown (commonly 36 months) * **Pros:** Focus on capital preservation, easy to understand * **Cons:** Based on single worst event, backward-looking * **Benchmarks:** >1 = Good | 3-5 = Strong **Martin Ratio - Ulcer Performance** * **Formula:** Excess return Γ· Ulcer Index (RMS of drawdowns) * **Pros:** Considers all drawdowns, smooth equity curve focus * **Cons:** Less known/comparable, complex calculation * **Use:** Compare across your strategies **SQN - System Quality Number** * **Formula:** (Expectancy Γ· Std Dev) Γ— √Number of trades * **Pros:** Accounts for sample size, good for system comparison * **Cons:** Requires sufficient trades for validity * **Benchmarks:** >2 = Good | >3 = Excellent | >5 = Superb *** **Choosing by Trading Style** | Style | Primary Metrics | Secondary Metrics | | -------------------- | ----------------- | ----------------- | | **Scalping** | Win Rate + Sharpe | Total Profit | | **Day Trading** | Sharpe + Win Rate | Average P\&L | | **Swing Trading** | Sortino + Calmar | Gain-to-Pain | | **Position Trading** | Calmar + Martin | Sortino | {% hint style="warning" %} **Remember:** Win rate alone is misleading. A strategy with 30% win rate but 3:1 reward/risk is more profitable than 70% win rate with 1:3 reward/risk. {% endhint %} {% hint style="info" %} **AI Mode Selection:** * **Intraday/Mean-reversion:** Optimizes for Sharpe + Win Rate * **Trend/Swing trading:** Prioritizes Sortino + Calmar * **Multi-metric:** Balances all metrics for robust performance {% endhint %}
*** {% hint style="success" %} **Bottom Line:** Let AI handle optimization while you focus on trading decisions and risk management. {% endhint %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.infinityalgo.com/features/infinity-algo-features/ai-optimization.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.