TR_Agent/DOCUMENTAZIONE_AGENTI.md

# Multi-Agent Trading System - Interaction and Market Logic Documentation

## Agent Interaction Flow

### Overview

The multi-agent system operates through a structured interaction flow where each agent contributes to the final trading decision. This flow is divided into three main phases:

1. **Analysis Phase**: Each agent analyzes market data and produces individual signals
2. **Interaction Phase**: Agents communicate and combine their signals
3. **Orchestration Phase**: The orchestrator makes the final trading decision

### Detailed Interaction Flow

#### Phase 1: Analysis

Each agent independently analyzes the market data during the `Analyze()` method call:

1. **Hurst Agent (RegimeDetector)**:
   - Calculates Hurst exponent using DFA/R/S analysis
   - Updates `SHARED_regimeH` and `SHARED_regimeZ`
   - Period dynamically adjusted based on data length

2. **ADX Agent (RegimeADX)**:
   - Calculates ADX using native MT5 function
   - Normalizes ADX to z-score
   - Updates `SHARED_adxZ` and `SHARED_adxRaw`

3. **MA Agent (MAAgent)**:
   - Calculates price-to-MA ratio
   - Computes slope of MA
   - Combines signals using variance weighting
   - Updates `lastZScore` (local, not shared)

4. **Momentum Agent (MomentumAgent)**:
   - Calculates momentum and acceleration
   - Computes z-scores for both
   - Combines signals using variance weighting
   - Updates `lastZScore` (local, not shared)

5. **Consensus Agent (RegimeConsensus)**:
   - Called by orchestrator during `Interact()` phase
   - Combines Hurst and ADX signals
   - Calculates agreement between agents
   - Updates `SHARED_regimeConsensus`, `SHARED_regimeAgreement`
   - Detects trading patterns
   - Updates `SHARED_patternCode`, `SHARED_patternName`

6. **Pattern Hunter Agent (PatternHunter)**:
   - Called by orchestrator during `Interact()` phase
   - Analyzes historical patterns
   - Updates `lastZScore` (local, not shared)

#### Phase 2: Orchestration

The orchestrator combines agent signals using a sophisticated softmax gating mechanism:

1. **Signal Collection**:
   - Collects z-scores from all agents
   - Filters out disabled agents

2. **Softmax Gating**:
   - Calculates mean and variance of agent z-scores
   - Temperature parameter: `temp = √(zVar) / √(agentCount)`
   - Higher variance = higher temperature = more mixing
   - Lower variance = lower temperature = more selective

3. **Combined Signal**:
   - Applies softmax to get weighted agent contributions
   - Applies tanh to normalize to [-1, 1]
   - Updates `combinedZ` and `combinedZStats`

#### Phase 3: Market Decision

1. **Entry Signal**:
   - Combined z-score compared to `minActionableZ`
   - `minActionableZ = combinedZStats.Std()` (empirical)
   - Fallback: 0.5 (when stats not ready)

2. **Position Sizing**:
   - Base lot: `AdaptiveBaseLot()`
   - Adjusted by entry z-score magnitude
   - Capped by broker limits

3. **Risk Management**:
   - SL: `AdaptiveSL()` = returnStats.Std()
   - TP: `AdaptiveTP()` = `AdaptiveSL() * RR ratio`
   - RR ratio: `1.0 / (1.0 - winRate)`

## Market Entry/Exit Logic

### Entry Conditions

The system enters a trade when:

1. **Signal Strength**:
   - `|combinedZ| > minActionableZ`
   - `minActionableZ = combinedZStats.Ready() ? combinedZStats.Std() : 0.5`
   - This ensures we only trade when the signal is statistically significant

2. **Position Direction**:
   - `dir = 1` if `combinedZ > thr`
   - `dir = -1` if `combinedZ < -thr`
   - `thr = (overrideMinZ > 0) ? overrideMinZ : AdaptiveMinZ()`

3. **Lot Size Calculation**:
   ```
   riskFrac = wr * (1.0 - MathMin(maxDrawdown, 0.5)) * (0.5 + sharpeBonus * 0.5)
   riskFrac = MathMax(0.002, MathMin(0.02, riskFrac))
   lot = balance * riskFrac / 100000.0
   lot = lot * MathAbs(fs.zScore)  // Scale by signal strength
   ```

### Exit Conditions

#### Take Profit (TP)
- **Trigger**: Price reaches TP level
- **TP Level**: `entryPrice + (atr * tpMult)` (long) or `entryPrice - (atr * tpMult)` (short)
- **tpMult**: `AdaptiveTP() / atr`

#### Stop Loss (SL)
- **Trigger**: Price reaches SL level
- **SL Level**: `entryPrice - (atr * slMult)` (long) or `entryPrice + (atr * slMult)` (short)
- **slMult**: `AdaptiveSL() / atr`

#### Opposite Signal
- **Trigger**: New trade signal in opposite direction
- **Logic**: Close existing position before opening new one
- **Implementation**: Check `CountPositions()` before opening new trade

#### Time-based Exit
- **Maximum Trades**: 20 trades per session
- **Rollback Convergence**: If weights stabilize for `minTrades`, system marks as converged

### Risk Management Details

#### Drawdown Tracking
```
double eq = AccountInfoDouble(ACCOUNT_EQUITY);
if(eq > peakEquity) peakEquity = eq;
double dd = (peakEquity > 0) ? (peakEquity - eq) / peakEquity : 0;
if(dd > maxDrawdown) maxDrawdown = dd;
```

#### Weight Adaptation
```
if(combinedZStats.Ready() && returnStats.Count() > 0) {
   double rv = returnStats.Std();
   weightAlpha = rv / (1.0 + rv);
   weightAlpha = MathMax(minAlpha, weightAlpha);
   agents[i].weight = MathMax(weightMin, rho);
}
```

#### Sharpe Bonus Calculation
```
double sharpeBonus = MathMax(0, MathMin(1, rollingSharpe * 0.5));
```

### Market Hours and Session Management

#### Trading Hours
- **Symbol-specific**: Respects each symbol's trading hours
- **No weekend trading**: No trades on weekends
- **Session limits**: Maximum 20 trades per trading session

#### State Management
- **Model persistence**: Saves state at intervals
- **Warm-up**: Uses historical data for initial statistics
- **Recovery**: Loads saved state on startup

## Detailed Agent Interaction Examples

### Example 1: Hurst and ADX Interaction

1. **Hurst Agent Output**:
   - `SHARED_regimeH = 0.6` (trending market)
   - `SHARED_regimeZ = 0.8` (strong bullish signal)

2. **ADX Agent Output**:
   - `SHARED_adxZ = 0.7` (strong trend)
   - `SHARED_adxRaw = 35.0` (strong trend strength)

3. **Consensus Agent Processing**:
   - `hurstZ = 0.8`, `adxZ = 0.7`
   - `hStd = 0.5`, `aStd = 0.5`
   - `combinedScale = √(0.5² + 0.5²) = 0.707`
   - `agreement = 1.0 - (0.1 / 0.707) = 0.86`
   - `SHARED_regimeConsensus = tanh((0.8 + 0.7) / 2 * 0.86) = 0.94`
   - Pattern: `strong_trend` (pCode = 1)

### Example 2: Pattern Hunter Interaction

1. **Pattern Hunter Inputs**:
   - `hurst = 0.8`, `adx = 0.7`, `ma = 0.3`, `mom = 0.9`
   - `tH = 0.5`, `tA = 0.5`, `tM = 0.4`, `tM2 = 0.6`

2. **Pattern Detection**:
   - `maMomAgree = (0.3 * 0.9 > 0.4 * 0.6) = true`
   - `regimeTrend = (0.8 + 0.7) > 0.707 = true`
   - `momAccel = (0.9 > 0.3 + 0.4 * 0.15) && (0.3 > 0.3 + 0.4 * 0.15) = true`
   - Pattern: `trend_accel` (pCode = 8)
   - `pZ = (0.3 + 0.9) / comboNorm = 1.2 / 0.707 = 1.70`
   - `patternStrength = 1.0 - exp(-1.70) = 0.82`

### Example : Market Entry

1. **Combined Signal**:
   - `combinedZ = 0.94` (from consensus)
   - `minActionableZ = 0.5` (from combinedZStats)
   - Entry: `|0.94| > 0.5` ✓

2. **Position Sizing**:
   - `balance = 10000`
   - `winRate = 0.6`, `maxDrawdown = 0.2`, `sharpeBonus = 0.8`
   - `riskFrac = 0.6 * (1 - 0.2) * (0.5 + 0.8 * 0.5) = 0.6 * 0.8 * 0.9 = 0.432`
   - `riskFrac = MathMax(0.002, MathMin(0.02, 0.432)) = 0.02`
   - `lot = 10000 * 0.02 / 100000 = 0.2`
   - `lot = 0.2 * MathAbs(0.94) = 0.188`

3. **SL/TP**:
   - `ATR = 0.02`
   - `SL = entryPrice - 0.02 * 1.5 = entryPrice - 0.03`
   - `TP = entryPrice + 0.02 * 2.5 = entryPrice + 0.05`

## Error Handling and Recovery

### Data Issues
1. **Insufficient Data**:
   - Wait for minimum samples before using statistics
   - Use fallback values when data insufficient

2. **Division by Zero**:
   - Use `DATA_EPS()` for safe division
   - Check for zero variance before calculations

3. **Invalid Values**:
   - Validate all input data
     - Use reasonable defaults for invalid values

### Recovery Mechanisms
1. **Model Reload**:
   - Automatic reload on startup if save file exists
   - Graceful degradation if save file corrupted

2. **State Reset**:
   - Reset all statistics on error
   - Log errors for debugging

## Performance Monitoring

### Key Metrics
1. **Trading Performance**:
   - Win rate
   - Average win/loss ratio
   - Maximum drawdown
   - Sharpe ratio

2. **System Performance**:
   - Agent weight convergence
   - Signal strength statistics
   - Processing time
   - Memory usage

### Health Monitoring
1. **Orchestrator Health**:
   - `PrintAgentStatus()`: Shows all agent signals and weights
   - `LogHealth()`: Shows rolling performance metrics
   - `LogBarHistory()`: Stores historical signals

2. **Agent Health**:
   - Each agent has `SignalInfo()` method
   - Shows agent name, z-score, and statistics
   - Helps identify underperforming agents

## Future Enhancements

1. **Advanced Features**:
   - Multi-timeframe analysis
   - Machine learning for pattern recognition
   - Natural language processing for news sentiment

2. **Risk Management**:
   - Dynamic position sizing based on volatility
   - Correlation-based risk limits
   - Market regime-specific risk adjustments

3. **Monitoring**:
   - Real-time performance dashboards
   - Automated alert system
   - Performance benchmarking

## Conclusion

This documentation provides a comprehensive understanding of the multi-agent trading system's architecture, interaction patterns, and market logic. The system combines multiple analytical approaches to create a robust, data-driven trading strategy that adapts to changing market conditions.

Key strengths:
- **Diversification**: Multiple analytical approaches reduce reliance on single indicators
- **Adaptability**: All parameters are data-driven and adaptive
- **Robustness**: Consensus-based decision making with lateral communication
- **Transparency**: Clear documentation of all agent interactions and logic

The system is designed to be both powerful and maintainable, with clear separation of concerns between different analytical components.