027af0d2f0
- Add stop-loss-decision-learner.js: Core learning engine
- Add enhanced-autonomous-risk-manager.js: Learning-enhanced decisions
- Add AI learning API and dashboard components
- Add database schema for decision tracking
- Integrate with existing automation system
- Demo scripts and documentation
Result: AI learns from every decision and improves over time! 🚀
109 lines
3.2 KiB
Markdown
109 lines
3.2 KiB
Markdown
# 🧠 Stop Loss Decision Learning System
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## 📋 **Missing Learning Components**
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### 1. **Decision Recording**
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The autonomous risk manager needs to record every decision made near stop loss:
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```javascript
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// When AI makes a decision near SL:
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await this.recordDecision({
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tradeId: trade.id,
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distanceFromSL: stopLoss.distancePercent,
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decision: 'TIGHTEN_STOP_LOSS', // or 'HOLD', 'EXIT', etc.
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reasoning: decision.reasoning,
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marketConditions: await this.analyzeMarketContext(),
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timestamp: new Date()
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});
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```
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### 2. **Outcome Assessment**
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Track what happened after each AI decision:
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```javascript
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// Later, when trade closes:
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await this.assessDecisionOutcome({
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decisionId: originalDecision.id,
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actualOutcome: 'HIT_ORIGINAL_SL', // or 'HIT_TIGHTENED_SL', 'PROFITABLE_EXIT'
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timeToOutcome: minutesFromDecision,
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pnlImpact: decision.pnlDifference,
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wasDecisionCorrect: calculateIfDecisionWasOptimal()
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});
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```
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### 3. **Learning Integration**
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Connect decision outcomes to AI improvement:
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```javascript
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// Analyze historical decision patterns:
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const learningInsights = await this.analyzeDecisionHistory({
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successfulPatterns: [], // What decisions work best at different SL distances
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failurePatterns: [], // What decisions often lead to worse outcomes
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optimalTiming: {}, // Best times to act vs hold
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contextFactors: [] // Market conditions that influence decision success
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});
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```
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## 🎯 **Implementation Requirements**
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### **Database Schema Extension**
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```sql
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-- New table for SL decision tracking
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CREATE TABLE sl_decisions (
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id STRING PRIMARY KEY,
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trade_id STRING,
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decision_type STRING, -- 'HOLD', 'EXIT', 'TIGHTEN_SL', 'PARTIAL_EXIT'
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distance_from_sl FLOAT,
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reasoning TEXT,
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market_conditions JSON,
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decision_timestamp DATETIME,
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outcome STRING, -- 'CORRECT', 'INCORRECT', 'NEUTRAL'
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outcome_timestamp DATETIME,
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pnl_impact FLOAT,
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learning_score FLOAT
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);
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```
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### **Enhanced Autonomous Risk Manager**
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```javascript
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class AutonomousRiskManager {
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async analyzePosition(monitor) {
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// Current decision logic...
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const decision = this.makeDecision(stopLoss);
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// NEW: Record this decision for learning
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await this.recordDecision(monitor, decision);
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return decision;
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}
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async recordDecision(monitor, decision) {
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// Store decision with context for later analysis
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}
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async learnFromPastDecisions() {
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// Analyze historical decisions and outcomes
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// Adjust decision thresholds based on what worked
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}
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}
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```
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## 📊 **Learning Outcomes**
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With this system, the AI would learn:
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1. **Optimal Decision Points**: At what SL distance should it act vs hold?
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2. **Context Sensitivity**: When do market conditions make early exit better?
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3. **Risk Assessment**: How accurate are its "emergency" vs "safe" classifications?
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4. **Strategy Refinement**: Which stop loss adjustments actually improve outcomes?
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## 🚀 **Integration with Existing System**
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This would extend the current drift-feedback-loop.js to include:
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- SL decision tracking
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- Decision outcome assessment
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- Learning pattern recognition
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- Strategy optimization based on decision history
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The result: An AI that not only learns from trade outcomes but also learns from its own decision-making process near stop losses! 🎯
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