signal-platform/.kiro/specs/intelligent-trade-recommendations/design.md

Design Document: Intelligent Trade Recommendation System

Overview

The Intelligent Trade Recommendation System enhances the Signal Dashboard's trade setup generation by providing bidirectional analysis (LONG and SHORT), confidence scoring, multiple price targets with probability estimates, and signal conflict detection. This system transforms raw multi-dimensional signals into actionable trading recommendations suitable for non-professional traders.

Goals

• Generate both LONG and SHORT trade setups for every ticker with independent confidence scores
• Identify 3-5 price targets at S/R levels with probability estimates for staged profit-taking
• Detect and flag contradictions between sentiment, technical, momentum, and fundamental signals
• Provide clear recommendation summaries with action, reasoning, and risk level
• Maintain performance targets: 500ms per ticker, 10 tickers/second batch processing

Non-Goals

• Real-time trade execution or order management
• Backtesting or historical performance tracking (deferred to future phase)
• Machine learning-based prediction models
• Integration with external trading platforms

Key Design Decisions

1. Extend TradeSetup model rather than create new tables to maintain backward compatibility
2. Synchronous recommendation generation during R:R scanner job (no separate scheduled job)
3. Quality-score based target selection combining R:R ratio, S/R strength, and proximity
4. Rule-based confidence scoring using dimension score thresholds and alignment checks
5. JSON fields for flexible data (targets array, conflict flags) to avoid complex schema changes

Architecture

System Context

graph TB
    subgraph "Existing System"
        RR[R:R Scanner Service]
        SCORE[Scoring Service]
        SR[S/R Service]
        IND[Indicator Service]
        SENT[Sentiment Service]
        FUND[Fundamental Service]
    end
    
    subgraph "New Components"
        REC[Recommendation Engine]
        DIR[Direction Analyzer]
        TGT[Target Generator]
        PROB[Probability Estimator]
        CONF[Signal Conflict Detector]
    end
    
    subgraph "Data Layer"
        TS[(TradeSetup Model)]
        DS[(DimensionScore)]
        SRL[(SRLevel)]
        SENT_M[(SentimentScore)]
    end
    
    RR --> REC
    SCORE --> REC
    SR --> REC
    
    REC --> DIR
    REC --> TGT
    REC --> PROB
    REC --> CONF
    
    DIR --> TS
    TGT --> TS
    PROB --> TS
    CONF --> TS
    
    DS --> DIR
    DS --> CONF
    SRL --> TGT
    SRL --> PROB
    SENT_M --> CONF

Integration Strategy

The recommendation system integrates into the existing R:R scanner workflow:

1. Trigger Point: rr_scanner_service.scan_ticker() generates base LONG/SHORT setups
2. Enhancement Phase: New recommendation_service.enhance_trade_setup() enriches each setup
3. Persistence: Extended TradeSetup model stores all recommendation data
4. API Layer: Existing /api/v1/trades endpoints return enhanced data

This approach ensures:

• Zero breaking changes to existing scanner logic
• Backward compatibility with current TradeSetup consumers
• Single transaction for setup generation and enhancement
• No additional scheduled jobs required

Components and Interfaces

Recommendation Engine (recommendation_service.py)

Responsibility: Orchestrate the recommendation generation process for a trade setup.

Interface:

async def enhance_trade_setup(
    db: AsyncSession,
    ticker: Ticker,
    setup: TradeSetup,
    dimension_scores: dict[str, float],
    sr_levels: list[SRLevel],
    sentiment_classification: str | None,
    atr_value: float,
) -> TradeSetup:
    """Enhance a base trade setup with recommendation data.
    
    Args:
        db: Database session
        ticker: Ticker model instance
        setup: Base TradeSetup with direction, entry, stop, target, rr_ratio
        dimension_scores: Dict of dimension -> score (technical, sentiment, momentum, etc.)
        sr_levels: All S/R levels for the ticker
        sentiment_classification: Latest sentiment (bearish/neutral/bullish)
        atr_value: Current ATR for volatility adjustment
        
    Returns:
        Enhanced TradeSetup with confidence_score, targets, conflict_flags, etc.
    """

Algorithm:

1. Call direction_analyzer.calculate_confidence() to get confidence score
2. Call target_generator.generate_targets() to get 3-5 targets
3. Call probability_estimator.estimate_probabilities() for each target
4. Call signal_conflict_detector.detect_conflicts() to identify contradictions
5. Generate recommendation summary based on confidence and conflicts
6. Update setup model with all recommendation data
7. Return enhanced setup

Dependencies: All four sub-components, SystemSetting for thresholds

Direction Analyzer

Responsibility: Calculate confidence scores for LONG and SHORT directions based on signal alignment.

Interface:

def calculate_confidence(
    direction: str,
    dimension_scores: dict[str, float],
    sentiment_classification: str | None,
) -> float:
    """Calculate confidence score (0-100%) for a trade direction.
    
    Args:
        direction: "long" or "short"
        dimension_scores: Dict with keys: technical, sentiment, momentum, fundamental
        sentiment_classification: "bearish", "neutral", "bullish", or None
        
    Returns:
        Confidence score 0-100%
    """

Algorithm:

Base confidence = 50.0

For LONG direction:
  - If technical > 60: add 15 points
  - If technical > 70: add additional 10 points
  - If momentum > 60: add 15 points
  - If sentiment is "bullish": add 15 points
  - If fundamental > 60: add 10 points
  
For SHORT direction:
  - If technical < 40: add 15 points
  - If technical < 30: add additional 10 points
  - If momentum < 40: add 15 points
  - If sentiment is "bearish": add 15 points
  - If fundamental < 40: add 10 points

Clamp result to [0, 100]

Rationale: Rule-based scoring provides transparency and predictability. Weights favor technical and momentum (15 points each) as they reflect price action, with sentiment and fundamentals as supporting factors.

Target Generator

Responsibility: Identify 3-5 price targets at S/R levels with classification and R:R calculation.

Interface:

def generate_targets(
    direction: str,
    entry_price: float,
    stop_loss: float,
    sr_levels: list[SRLevel],
    atr_value: float,
) -> list[dict]:
    """Generate multiple price targets for a trade setup.
    
    Args:
        direction: "long" or "short"
        entry_price: Entry price for the trade
        stop_loss: Stop loss price
        sr_levels: All S/R levels for the ticker
        atr_value: Current ATR value
        
    Returns:
        List of target dicts with keys:
        - price: Target price level
        - distance_from_entry: Absolute distance
        - distance_atr_multiple: Distance as multiple of ATR
        - rr_ratio: Risk-reward ratio for this target
        - classification: "Conservative", "Moderate", or "Aggressive"
        - sr_level_id: ID of the S/R level used
        - sr_strength: Strength score of the S/R level
    """

Algorithm:

1. Filter S/R levels by direction:
   - LONG: resistance levels above entry (type="resistance", price > entry)
   - SHORT: support levels below entry (type="support", price < entry)

2. Apply volatility filter:
   - Exclude levels within 1x ATR of entry (too close)
   - If ATR > 5% of price: include levels up to 10x ATR
   - If ATR < 2% of price: limit to levels within 3x ATR

3. Calculate quality score for each candidate:
   quality = 0.35 * norm_rr + 0.35 * norm_strength + 0.30 * norm_proximity
   where:
   - norm_rr = min(rr_ratio / 10.0, 1.0)
   - norm_strength = strength / 100.0
   - norm_proximity = 1.0 - min(distance / entry, 1.0)

4. Sort candidates by quality score descending

5. Select top 3-5 targets:
   - Take top 5 if available
   - Minimum 3 required (flag setup if fewer)

6. Classify targets by distance:
   - Conservative: nearest 1-2 targets
   - Aggressive: furthest 1-2 targets
   - Moderate: middle targets

7. Calculate R:R ratio for each target:
   risk = abs(entry_price - stop_loss)
   reward = abs(target_price - entry_price)
   rr_ratio = reward / risk

Rationale: Quality-based selection ensures targets balance multiple factors. ATR-based filtering adapts to volatility. Classification helps traders plan staged exits.

Probability Estimator

Responsibility: Calculate probability (0-100%) of reaching each price target.

Interface:

def estimate_probability(
    target: dict,
    dimension_scores: dict[str, float],
    sentiment_classification: str | None,
    direction: str,
    config: dict,
) -> float:
    """Estimate probability of reaching a price target.
    
    Args:
        target: Target dict from generate_targets()
        dimension_scores: Current dimension scores
        sentiment_classification: Latest sentiment
        direction: "long" or "short"
        config: Configuration dict with weights from SystemSetting
        
    Returns:
        Probability percentage 0-100%
    """

Algorithm:

Base probability calculation:

1. Distance factor (40% weight):
   - Conservative targets (nearest): base = 70%
   - Moderate targets (middle): base = 55%
   - Aggressive targets (furthest): base = 40%

2. S/R strength factor (30% weight):
   - strength >= 80: add 15%
   - strength 60-79: add 10%
   - strength 40-59: add 5%
   - strength < 40: subtract 10%

3. Signal alignment factor (20% weight):
   - Check if signals support direction:
     * LONG: technical > 60 AND (sentiment bullish OR momentum > 60)
     * SHORT: technical < 40 AND (sentiment bearish OR momentum < 40)
   - If aligned: add 15%
   - If conflicted: subtract 15%

4. Volatility factor (10% weight):
   - If distance_atr_multiple > 5: add 5% (high volatility favors distant targets)
   - If distance_atr_multiple < 2: add 5% (low volatility favors near targets)

Final probability = base + strength_adj + alignment_adj + volatility_adj
Clamp to [10, 90] (never 0% or 100% to reflect uncertainty)

Configuration Parameters (stored in SystemSetting):

• signal_alignment_weight: Default 0.15 (15%)
• sr_strength_weight: Default 0.20 (20%)
• distance_penalty_factor: Default 0.10 (10%)

Rationale: Multi-factor approach balances distance (primary), S/R quality (secondary), and signal confirmation (tertiary). Clamping to [10, 90] acknowledges market uncertainty.

Signal Conflict Detector

Responsibility: Identify contradictions between sentiment, technical, momentum, and fundamental signals.

Interface:

def detect_conflicts(
    dimension_scores: dict[str, float],
    sentiment_classification: str | None,
) -> list[str]:
    """Detect signal conflicts across dimensions.
    
    Args:
        dimension_scores: Dict with technical, sentiment, momentum, fundamental scores
        sentiment_classification: "bearish", "neutral", "bullish", or None
        
    Returns:
        List of conflict descriptions, e.g.:
        - "sentiment-technical: Bearish sentiment conflicts with bullish technical (72)"
        - "momentum-technical: Momentum (35) diverges from technical (68) by 33 points"
    """

Algorithm:

Conflicts detected:

1. Sentiment-Technical conflict:
   - If sentiment="bearish" AND technical > 60: flag conflict
   - If sentiment="bullish" AND technical < 40: flag conflict
   - Message: "sentiment-technical: {sentiment} sentiment conflicts with {direction} technical ({score})"

2. Momentum-Technical divergence:
   - If abs(momentum - technical) > 30: flag conflict
   - Message: "momentum-technical: Momentum ({momentum}) diverges from technical ({technical}) by {diff} points"

3. Sentiment-Momentum conflict:
   - If sentiment="bearish" AND momentum > 60: flag conflict
   - If sentiment="bullish" AND momentum < 40: flag conflict
   - Message: "sentiment-momentum: {sentiment} sentiment conflicts with momentum ({score})"

4. Fundamental-Technical divergence (informational only):
   - If abs(fundamental - technical) > 40: flag as "weak conflict"
   - Message: "fundamental-technical: Fundamental ({fund}) diverges significantly from technical ({tech})"

Return list of all detected conflicts

Impact on Confidence:

• Each conflict reduces confidence by 15-25%:
  • Sentiment-Technical: -20%
  • Momentum-Technical: -15%
  • Sentiment-Momentum: -20%
  • Fundamental-Technical: -10% (weaker impact)
• Applied in direction_analyzer.calculate_confidence() after base calculation

Rationale: Conflicts indicate uncertainty and increase risk. Sentiment-technical conflicts are most serious as they represent narrative vs. price action divergence.

Data Models

Extended TradeSetup Model

New Fields:

class TradeSetup(Base):
    __tablename__ = "trade_setups"
    
    # Existing fields (unchanged)
    id: Mapped[int] = mapped_column(primary_key=True)
    ticker_id: Mapped[int] = mapped_column(ForeignKey("tickers.id", ondelete="CASCADE"))
    direction: Mapped[str] = mapped_column(String(10), nullable=False)
    entry_price: Mapped[float] = mapped_column(Float, nullable=False)
    stop_loss: Mapped[float] = mapped_column(Float, nullable=False)
    target: Mapped[float] = mapped_column(Float, nullable=False)  # Primary target
    rr_ratio: Mapped[float] = mapped_column(Float, nullable=False)  # Primary R:R
    composite_score: Mapped[float] = mapped_column(Float, nullable=False)
    detected_at: Mapped[datetime] = mapped_column(DateTime(timezone=True))
    
    # NEW: Recommendation fields
    confidence_score: Mapped[float | None] = mapped_column(Float, nullable=True)
    targets_json: Mapped[str | None] = mapped_column(Text, nullable=True)
    conflict_flags_json: Mapped[str | None] = mapped_column(Text, nullable=True)
    recommended_action: Mapped[str | None] = mapped_column(String(20), nullable=True)
    reasoning: Mapped[str | None] = mapped_column(Text, nullable=True)
    risk_level: Mapped[str | None] = mapped_column(String(10), nullable=True)

Field Descriptions:

• confidence_score: Float 0-100, confidence in this direction
• targets_json: JSON array of target objects (see schema below)
• conflict_flags_json: JSON array of conflict strings
• recommended_action: Enum-like string: "LONG_HIGH", "LONG_MODERATE", "SHORT_HIGH", "SHORT_MODERATE", "NEUTRAL"
• reasoning: Human-readable explanation of recommendation
• risk_level: "Low", "Medium", or "High" based on conflicts

Targets JSON Schema:

[
  {
    "price": 150.25,
    "distance_from_entry": 5.25,
    "distance_atr_multiple": 2.5,
    "rr_ratio": 3.5,
    "probability": 65.0,
    "classification": "Conservative",
    "sr_level_id": 42,
    "sr_strength": 75
  },
  ...
]

Conflict Flags JSON Schema:

[
  "sentiment-technical: Bearish sentiment conflicts with bullish technical (72)",
  "momentum-technical: Momentum (35) diverges from technical (68) by 33 points"
]

Backward Compatibility:

• All new fields are nullable
• Existing target and rr_ratio fields remain as primary target data
• Old consumers can ignore new fields
• New consumers use targets_json for full target list

SystemSetting Extensions

New Configuration Keys:

# Recommendation thresholds
"recommendation_high_confidence_threshold": 70.0  # % for "High Confidence"
"recommendation_moderate_confidence_threshold": 50.0  # % for "Moderate Confidence"
"recommendation_confidence_diff_threshold": 20.0  # % difference for directional recommendation

# Probability calculation weights
"recommendation_signal_alignment_weight": 0.15  # 15%
"recommendation_sr_strength_weight": 0.20  # 20%
"recommendation_distance_penalty_factor": 0.10  # 10%

# Conflict detection thresholds
"recommendation_momentum_technical_divergence_threshold": 30.0  # points
"recommendation_fundamental_technical_divergence_threshold": 40.0  # points

Access Pattern:

async def get_recommendation_config(db: AsyncSession) -> dict:
    """Load all recommendation configuration from SystemSetting."""
    # Query all keys starting with "recommendation_"
    # Return dict with defaults for missing keys

No New Tables Required

The design intentionally avoids new tables to minimize schema complexity:

• TradeSetup extensions handle all recommendation data
• JSON fields provide flexibility for evolving data structures
• SystemSetting stores configuration
• Existing relationships (Ticker → TradeSetup) remain unchanged

Algorithm Design

Confidence Scoring Formula

Detailed Implementation:

def calculate_confidence(
    direction: str,
    dimension_scores: dict[str, float],
    sentiment_classification: str | None,
    conflicts: list[str],
) -> float:
    """Calculate confidence score with conflict penalties."""
    
    base = 50.0
    technical = dimension_scores.get("technical", 50.0)
    momentum = dimension_scores.get("momentum", 50.0)
    fundamental = dimension_scores.get("fundamental", 50.0)
    
    if direction == "long":
        # Technical contribution
        if technical > 70:
            base += 25.0
        elif technical > 60:
            base += 15.0
        
        # Momentum contribution
        if momentum > 70:
            base += 20.0
        elif momentum > 60:
            base += 15.0
        
        # Sentiment contribution
        if sentiment_classification == "bullish":
            base += 15.0
        elif sentiment_classification == "neutral":
            base += 5.0
        
        # Fundamental contribution
        if fundamental > 60:
            base += 10.0
    
    elif direction == "short":
        # Technical contribution
        if technical < 30:
            base += 25.0
        elif technical < 40:
            base += 15.0
        
        # Momentum contribution
        if momentum < 30:
            base += 20.0
        elif momentum < 40:
            base += 15.0
        
        # Sentiment contribution
        if sentiment_classification == "bearish":
            base += 15.0
        elif sentiment_classification == "neutral":
            base += 5.0
        
        # Fundamental contribution
        if fundamental < 40:
            base += 10.0
    
    # Apply conflict penalties
    for conflict in conflicts:
        if "sentiment-technical" in conflict:
            base -= 20.0
        elif "momentum-technical" in conflict:
            base -= 15.0
        elif "sentiment-momentum" in conflict:
            base -= 20.0
        elif "fundamental-technical" in conflict:
            base -= 10.0
    
    return max(0.0, min(100.0, base))

Scoring Breakdown:

• Base: 50 points (neutral starting point)
• Technical: up to 25 points (most important - reflects price action)
• Momentum: up to 20 points (confirms trend strength)
• Sentiment: up to 15 points (narrative support)
• Fundamental: up to 10 points (value support)
• Maximum possible: 120 points before conflicts
• Conflicts: -10 to -20 points each

Probability Calculation Formula

Detailed Implementation:

def estimate_probability(
    target: dict,
    dimension_scores: dict[str, float],
    sentiment_classification: str | None,
    direction: str,
    config: dict,
) -> float:
    """Estimate probability of reaching a price target."""
    
    # 1. Base probability from classification (40% weight)
    classification = target["classification"]
    if classification == "Conservative":
        base_prob = 70.0
    elif classification == "Moderate":
        base_prob = 55.0
    else:  # Aggressive
        base_prob = 40.0
    
    # 2. S/R strength adjustment (30% weight)
    strength = target["sr_strength"]
    if strength >= 80:
        strength_adj = 15.0
    elif strength >= 60:
        strength_adj = 10.0
    elif strength >= 40:
        strength_adj = 5.0
    else:
        strength_adj = -10.0
    
    # 3. Signal alignment adjustment (20% weight)
    technical = dimension_scores.get("technical", 50.0)
    momentum = dimension_scores.get("momentum", 50.0)
    
    alignment_adj = 0.0
    if direction == "long":
        if technical > 60 and (sentiment_classification == "bullish" or momentum > 60):
            alignment_adj = 15.0
        elif technical < 40 or (sentiment_classification == "bearish" and momentum < 40):
            alignment_adj = -15.0
    elif direction == "short":
        if technical < 40 and (sentiment_classification == "bearish" or momentum < 40):
            alignment_adj = 15.0
        elif technical > 60 or (sentiment_classification == "bullish" and momentum > 60):
            alignment_adj = -15.0
    
    # 4. Volatility adjustment (10% weight)
    atr_multiple = target["distance_atr_multiple"]
    volatility_adj = 0.0
    if atr_multiple > 5:
        volatility_adj = 5.0  # High volatility favors distant targets
    elif atr_multiple < 2:
        volatility_adj = 5.0  # Low volatility favors near targets
    
    # Combine all factors
    probability = base_prob + strength_adj + alignment_adj + volatility_adj
    
    # Clamp to [10, 90] to reflect uncertainty
    return max(10.0, min(90.0, probability))

Probability Ranges by Classification:

• Conservative: 60-90% (typically 70-85%)
• Moderate: 40-70% (typically 50-65%)
• Aggressive: 10-50% (typically 30-45%)

Signal Alignment Logic

Implementation:

def check_signal_alignment(
    direction: str,
    dimension_scores: dict[str, float],
    sentiment_classification: str | None,
) -> tuple[bool, str]:
    """Check if signals align with the trade direction.
    
    Returns:
        (is_aligned, description)
    """
    technical = dimension_scores.get("technical", 50.0)
    momentum = dimension_scores.get("momentum", 50.0)
    
    if direction == "long":
        tech_bullish = technical > 60
        momentum_bullish = momentum > 60
        sentiment_bullish = sentiment_classification == "bullish"
        
        # Need at least 2 of 3 signals aligned
        aligned_count = sum([tech_bullish, momentum_bullish, sentiment_bullish])
        
        if aligned_count >= 2:
            return True, f"Signals aligned for LONG: technical={technical:.0f}, momentum={momentum:.0f}, sentiment={sentiment_classification}"
        else:
            return False, f"Mixed signals for LONG: technical={technical:.0f}, momentum={momentum:.0f}, sentiment={sentiment_classification}"
    
    elif direction == "short":
        tech_bearish = technical < 40
        momentum_bearish = momentum < 40
        sentiment_bearish = sentiment_classification == "bearish"
        
        # Need at least 2 of 3 signals aligned
        aligned_count = sum([tech_bearish, momentum_bearish, sentiment_bearish])
        
        if aligned_count >= 2:
            return True, f"Signals aligned for SHORT: technical={technical:.0f}, momentum={momentum:.0f}, sentiment={sentiment_classification}"
        else:
            return False, f"Mixed signals for SHORT: technical={technical:.0f}, momentum={momentum:.0f}, sentiment={sentiment_classification}"
    
    return False, "Unknown direction"

Alignment Criteria:

• LONG: At least 2 of [technical > 60, momentum > 60, sentiment bullish]
• SHORT: At least 2 of [technical < 40, momentum < 40, sentiment bearish]
• Fundamental score is informational but not required for alignment

API Design

Enhanced Trade Setup Endpoints

GET /api/v1/trades

Returns all trade setups with recommendation data.

Query Parameters:

• direction: Optional filter ("long" or "short")
• min_confidence: Optional minimum confidence score (0-100)
• recommended_action: Optional filter ("LONG_HIGH", "LONG_MODERATE", "SHORT_HIGH", "SHORT_MODERATE", "NEUTRAL")

Response Schema:

{
  "status": "success",
  "data": [
    {
      "id": 1,
      "symbol": "AAPL",
      "direction": "long",
      "entry_price": 145.00,
      "stop_loss": 142.50,
      "target": 150.00,
      "rr_ratio": 2.0,
      "composite_score": 75.5,
      "detected_at": "2024-01-15T10:30:00Z",
      
      "confidence_score": 72.5,
      "recommended_action": "LONG_HIGH",
      "reasoning": "Strong technical (75) and bullish sentiment align with upward momentum (68). No major conflicts detected.",
      "risk_level": "Low",
      
      "targets": [
        {
          "price": 147.50,
          "distance_from_entry": 2.50,
          "distance_atr_multiple": 1.5,
          "rr_ratio": 1.0,
          "probability": 75.0,
          "classification": "Conservative",
          "sr_level_id": 42,
          "sr_strength": 80
        },
        {
          "price": 150.00,
          "distance_from_entry": 5.00,
          "distance_atr_multiple": 3.0,
          "rr_ratio": 2.0,
          "probability": 60.0,
          "classification": "Moderate",
          "sr_level_id": 43,
          "sr_strength": 70
        },
        {
          "price": 155.00,
          "distance_from_entry": 10.00,
          "distance_atr_multiple": 6.0,
          "rr_ratio": 4.0,
          "probability": 35.0,
          "classification": "Aggressive",
          "sr_level_id": 44,
          "sr_strength": 60
        }
      ],
      
      "conflict_flags": []
    }
  ]
}

GET /api/v1/trades/{symbol}

Returns trade setups for a specific ticker (both LONG and SHORT if available).

Response: Same schema as above, filtered by symbol.

Admin Configuration Endpoints

GET /api/v1/admin/settings/recommendations

Get current recommendation configuration.

Response:

{
  "status": "success",
  "data": {
    "high_confidence_threshold": 70.0,
    "moderate_confidence_threshold": 50.0,
    "confidence_diff_threshold": 20.0,
    "signal_alignment_weight": 0.15,
    "sr_strength_weight": 0.20,
    "distance_penalty_factor": 0.10,
    "momentum_technical_divergence_threshold": 30.0,
    "fundamental_technical_divergence_threshold": 40.0
  }
}

PUT /api/v1/admin/settings/recommendations

Update recommendation configuration.

Request Body:

{
  "high_confidence_threshold": 75.0,
  "signal_alignment_weight": 0.20
}

Response: Updated configuration object.

Validation:

• All thresholds must be 0-100
• All weights must be 0-1
• Returns 400 error for invalid values

Frontend Components

Ticker Detail Page Enhancement

Location: frontend/src/components/ticker/RecommendationPanel.tsx

Component Structure:

interface RecommendationPanelProps {
  symbol: string;
  longSetup?: TradeSetup;
  shortSetup?: TradeSetup;
}

export function RecommendationPanel({ symbol, longSetup, shortSetup }: RecommendationPanelProps) {
  // Display recommendation summary at top
  // Show LONG and SHORT setups side-by-side
  // Highlight recommended direction
  // Display targets table for each direction
  // Show conflict warnings if present
}

Visual Design:

• Recommendation summary card at top with large action text and confidence badge
• Two-column layout: LONG setup on left, SHORT setup on right
• Recommended direction has green border and subtle glow
• Non-recommended direction has muted opacity
• Risk level badge: green (Low), yellow (Medium), red (High)
• Targets table with sortable columns
• Conflict warnings in amber alert box

Data Flow:

// In TickerDetailPage.tsx
const { data: tradeSetups } = useTradeSetups(symbol);

const longSetup = tradeSetups?.find(s => s.direction === 'long');
const shortSetup = tradeSetups?.find(s => s.direction === 'short');

<RecommendationPanel 
  symbol={symbol}
  longSetup={longSetup}
  shortSetup={shortSetup}
/>

Scanner Page Enhancement

Location: frontend/src/components/scanner/TradeTable.tsx

New Columns:

• Recommended Action (with badge)
• Confidence Score (with progress bar)
• Best Target (highest probability target)
• Risk Level (with color-coded badge)

Filtering Controls:

interface ScannerFilters {
  direction?: 'long' | 'short';
  minConfidence?: number;
  recommendedAction?: 'LONG_HIGH' | 'LONG_MODERATE' | 'SHORT_HIGH' | 'SHORT_MODERATE' | 'NEUTRAL';
  riskLevel?: 'Low' | 'Medium' | 'High';
}

Table Enhancement:

<Table>
  <TableHeader>
    <TableRow>
      <TableHead>Symbol</TableHead>
      <TableHead>Recommended Action</TableHead>
      <TableHead>Confidence</TableHead>
      <TableHead>Entry</TableHead>
      <TableHead>Stop</TableHead>
      <TableHead>Best Target</TableHead>
      <TableHead>R:R</TableHead>
      <TableHead>Risk Level</TableHead>
      <TableHead>Composite</TableHead>
    </TableRow>
  </TableHeader>
  <TableBody>
    {setups.map(setup => (
      <TableRow 
        key={setup.id}
        onClick={() => navigate(`/ticker/${setup.symbol}`)}
        className="cursor-pointer hover:bg-white/5"
      >
        <TableCell>{setup.symbol}</TableCell>
        <TableCell>
          <RecommendationBadge action={setup.recommended_action} />
        </TableCell>
        <TableCell>
          <ConfidenceBar value={setup.confidence_score} />
        </TableCell>
        {/* ... other cells ... */}
      </TableRow>
    ))}
  </TableBody>
</Table>

Sorting:

• Default: Confidence score descending
• Secondary: R:R ratio descending
• Allow sorting by any column

Admin Settings Page Enhancement

Location: frontend/src/components/admin/RecommendationSettings.tsx

Form Fields:

<form onSubmit={handleSubmit}>
  <section>
    <h3>Confidence Thresholds</h3>
    <Input
      label="High Confidence Threshold (%)"
      type="number"
      min={0}
      max={100}
      value={config.high_confidence_threshold}
      onChange={...}
    />
    <Input
      label="Moderate Confidence Threshold (%)"
      type="number"
      min={0}
      max={100}
      value={config.moderate_confidence_threshold}
      onChange={...}
    />
    <Input
      label="Confidence Difference Threshold (%)"
      type="number"
      min={0}
      max={100}
      value={config.confidence_diff_threshold}
      onChange={...}
      helpText="Minimum difference between LONG and SHORT confidence for directional recommendation"
    />
  </section>

  <section>
    <h3>Probability Calculation Weights</h3>
    <Input
      label="Signal Alignment Weight"
      type="number"
      min={0}
      max={1}
      step={0.01}
      value={config.signal_alignment_weight}
      onChange={...}
    />
    <Input
      label="S/R Strength Weight"
      type="number"
      min={0}
      max={1}
      step={0.01}
      value={config.sr_strength_weight}
      onChange={...}
    />
    <Input
      label="Distance Penalty Factor"
      type="number"
      min={0}
      max={1}
      step={0.01}
      value={config.distance_penalty_factor}
      onChange={...}
    />
  </section>

  <section>
    <h3>Conflict Detection Thresholds</h3>
    <Input
      label="Momentum-Technical Divergence Threshold"
      type="number"
      min={0}
      max={100}
      value={config.momentum_technical_divergence_threshold}
      onChange={...}
      helpText="Points difference to flag momentum-technical conflict"
    />
    <Input
      label="Fundamental-Technical Divergence Threshold"
      type="number"
      min={0}
      max={100}
      value={config.fundamental_technical_divergence_threshold}
      onChange={...}
    />
  </section>

  <Button type="submit">Save Configuration</Button>
  <Button type="button" onClick={handleReset}>Reset to Defaults</Button>
</form>

Validation:

• Client-side validation before submission
• Toast notification on success/error
• Confirmation dialog for reset to defaults

Database Schema Changes

Migration Strategy

Alembic Migration: alembic revision -m "add_recommendation_fields_to_trade_setup"

Migration Script:

"""add_recommendation_fields_to_trade_setup

Revision ID: abc123def456
Revises: previous_revision
Create Date: 2024-01-15 10:00:00.000000
"""
from alembic import op
import sqlalchemy as sa

def upgrade():
    # Add new columns to trade_setups table
    op.add_column('trade_setups', 
        sa.Column('confidence_score', sa.Float(), nullable=True))
    op.add_column('trade_setups', 
        sa.Column('targets_json', sa.Text(), nullable=True))
    op.add_column('trade_setups', 
        sa.Column('conflict_flags_json', sa.Text(), nullable=True))
    op.add_column('trade_setups', 
        sa.Column('recommended_action', sa.String(20), nullable=True))
    op.add_column('trade_setups', 
        sa.Column('reasoning', sa.Text(), nullable=True))
    op.add_column('trade_setups', 
        sa.Column('risk_level', sa.String(10), nullable=True))

def downgrade():
    # Remove columns if rolling back
    op.drop_column('trade_setups', 'risk_level')
    op.drop_column('trade_setups', 'reasoning')
    op.drop_column('trade_setups', 'recommended_action')
    op.drop_column('trade_setups', 'conflict_flags_json')
    op.drop_column('trade_setups', 'targets_json')
    op.drop_column('trade_setups', 'confidence_score')

Deployment Steps:

1. Run migration: alembic upgrade head
2. Deploy new backend code with recommendation_service
3. Trigger R:R scanner job to populate recommendation data
4. Deploy frontend with new components
5. Verify data in admin panel

Rollback Plan:

• New fields are nullable, so old code continues to work
• If issues arise, run alembic downgrade -1
• Frontend gracefully handles missing recommendation fields

Integration Points

Integration with R:R Scanner Service

Modified rr_scanner_service.scan_ticker():

async def scan_ticker(
    db: AsyncSession,
    symbol: str,
    rr_threshold: float = 1.5,
    atr_multiplier: float = 1.5,
) -> list[TradeSetup]:
    """Scan a single ticker for trade setups with recommendations."""
    
    # ... existing code to generate base setups ...
    
    # NEW: Fetch data needed for recommendations
    dimension_scores = await _get_dimension_scores(db, ticker.id)
    sentiment_classification = await _get_latest_sentiment(db, ticker.id)
    
    # NEW: Enhance each setup with recommendations
    from app.services.recommendation_service import enhance_trade_setup
    
    enhanced_setups = []
    for setup in setups:
        enhanced = await enhance_trade_setup(
            db=db,
            ticker=ticker,
            setup=setup,
            dimension_scores=dimension_scores,
            sr_levels=sr_levels,
            sentiment_classification=sentiment_classification,
            atr_value=atr_value,
        )
        enhanced_setups.append(enhanced)
    
    # Delete old setups and persist enhanced ones
    await db.execute(
        delete(TradeSetup).where(TradeSetup.ticker_id == ticker.id)
    )
    for setup in enhanced_setups:
        db.add(setup)
    
    await db.commit()
    
    for s in enhanced_setups:
        await db.refresh(s)
    
    return enhanced_setups

Helper Functions:

async def _get_dimension_scores(db: AsyncSession, ticker_id: int) -> dict[str, float]:
    """Fetch all dimension scores for a ticker."""
    result = await db.execute(
        select(DimensionScore).where(DimensionScore.ticker_id == ticker_id)
    )
    scores = {ds.dimension: ds.score for ds in result.scalars().all()}
    return scores

async def _get_latest_sentiment(db: AsyncSession, ticker_id: int) -> str | None:
    """Fetch the most recent sentiment classification."""
    result = await db.execute(
        select(SentimentScore)
        .where(SentimentScore.ticker_id == ticker_id)
        .order_by(SentimentScore.timestamp.desc())
        .limit(1)
    )
    sentiment = result.scalar_one_or_none()
    return sentiment.classification if sentiment else None

Integration with Scoring Service

Data Dependencies:

• DimensionScore table: technical, sentiment, momentum, fundamental scores
• Accessed via _get_dimension_scores() helper
• No modifications to scoring_service required

Staleness Handling:

• Recommendation generation uses current dimension scores
• If dimension scores are stale, recommendations reflect that uncertainty
• Consider triggering score recomputation before R:R scan in scheduler

Integration with S/R Service

Data Dependencies:

• SRLevel table: price_level, type, strength, detection_method
• Already fetched in rr_scanner_service.scan_ticker()
• No modifications to sr_service required

Usage:

• Target generation filters S/R levels by type and direction
• Probability estimation uses strength scores
• Quality scoring combines strength with R:R and proximity

Integration with Sentiment Service

Data Dependencies:

• SentimentScore table: classification, confidence, timestamp
• Accessed via _get_latest_sentiment() helper
• No modifications to sentiment_service required

Usage:

• Conflict detection compares sentiment with technical/momentum
• Confidence scoring adds/subtracts points based on sentiment alignment
• Probability estimation adjusts for sentiment support

Integration with Indicator Service

Data Dependencies:

• ATR calculation already performed in rr_scanner_service
• No additional calls needed

Usage:

• Target generation uses ATR for volatility filtering
• Probability estimation uses distance_atr_multiple

Correctness Properties

A property is a characteristic or behavior that should hold true across all valid executions of a system—essentially, a formal statement about what the system should do. Properties serve as the bridge between human-readable specifications and machine-verifiable correctness guarantees.

Property Reflection

After analyzing all acceptance criteria, I identified several areas of redundancy:

Redundancy Group 1: Confidence Score Range Validation

• Properties 2.1 and 2.2 both test that confidence scores are 0-100%
• Resolution: Combine into single property testing both LONG and SHORT

Redundancy Group 2: Target Count and Direction

• Properties 3.1 and 3.2 both test target count (3-5) and direction relationship
• Resolution: Combine into single property covering both directions

Redundancy Group 3: Probability Range by Classification

• Properties 4.6, 4.7, 4.8 all test probability ranges for different classifications
• Resolution: Combine into single property testing all classifications

Redundancy Group 4: Schema Validation

• Properties 15.1-15.6 all test individual field existence
• Resolution: Combine into single property testing complete schema

Redundancy Group 5: API Response Schema

• Properties 7.2-7.6 all test individual response fields
• Resolution: Combine into single property testing complete response schema

Redundancy Group 6: S/R Strength Impact

• Properties 8.2 and 8.3 both test strength score impact on probability
• Resolution: Combine into single monotonicity property

After reflection, 60+ criteria reduce to 35 unique properties.

Property 1: Bidirectional Setup Generation

For any ticker with sufficient OHLCV data and S/R levels, the recommendation engine shall generate exactly two trade setups: one LONG and one SHORT, each with distinct direction fields.

Validates: Requirements 1.1, 1.5

Property 2: Direction-Appropriate S/R Level Usage

For any LONG setup, all targets shall be resistance levels above entry price. For any SHORT setup, all targets shall be support levels below entry price.

Validates: Requirements 1.3, 1.4

Property 3: Confidence Score Bounds

For any trade setup (LONG or SHORT), the confidence score shall be within the range [0, 100].

Validates: Requirements 2.1, 2.2

Property 4: Conflict Impact on Confidence

For any trade setup, when signal conflicts are detected, the confidence score shall be reduced compared to the same setup without conflicts.

Validates: Requirements 2.5, 5.7

Property 5: Confidence Persistence

For any generated trade setup, the confidence_score field shall be populated in the TradeSetup model.

Validates: Requirements 2.6

Property 6: Target Count and Direction

For any trade setup, the targets array shall contain 3 to 5 targets, all positioned in the correct direction relative to entry (above for LONG, below for SHORT).

Validates: Requirements 3.1, 3.2, 7.3

Property 7: Target Classification Correctness

For any targets array, targets shall be classified such that Conservative targets are nearest to entry, Aggressive targets are furthest, and Moderate targets are in between, based on distance ordering.

Validates: Requirements 3.3

Property 8: R:R Ratio Calculation

For any target in a trade setup, the R:R ratio shall equal (abs(target_price - entry_price)) / (abs(entry_price - stop_loss)).

Validates: Requirements 3.4

Property 9: Target Distance Ordering

For any targets array, targets shall be ordered by increasing distance from entry price.

Validates: Requirements 3.6

Property 10: Probability Bounds

For any target, the probability percentage shall be within the range [10, 90] to reflect market uncertainty.

Validates: Requirements 4.1

Property 11: S/R Strength Monotonicity

For any two targets at the same distance with different S/R strength scores, the target with higher strength shall have equal or higher probability.

Validates: Requirements 4.2, 8.2, 8.3

Property 12: Distance Probability Relationship

For any two targets with the same S/R strength, the target closer to entry shall have higher probability than the target further from entry.

Validates: Requirements 4.3

Property 13: Signal Alignment Impact

For any target, when signals are aligned with the trade direction, the probability shall be higher than when signals are not aligned, all other factors being equal.

Validates: Requirements 4.4

Property 14: Probability Classification Ranges

For any Conservative target, probability shall be above 60%. For any Moderate target, probability shall be between 40% and 70%. For any Aggressive target, probability shall be below 50%.

Validates: Requirements 4.6, 4.7, 4.8

Property 15: Sentiment-Technical Conflict Detection

For any ticker, when sentiment is bearish and technical score is above 60, OR when sentiment is bullish and technical score is below 40, a sentiment-technical conflict shall be flagged.

Validates: Requirements 5.1, 5.2, 5.3

Property 16: Momentum-Technical Divergence Detection

For any ticker, when the absolute difference between momentum score and technical score exceeds 30 points, a momentum-technical conflict shall be flagged.

Validates: Requirements 5.4, 5.5

Property 17: Conflict Persistence

For any trade setup with detected conflicts, the conflict_flags array shall contain descriptions of all detected conflicts.

Validates: Requirements 5.6

Property 18: Recommended Action Validity

For any trade setup, the recommended_action field shall be one of: "LONG_HIGH", "LONG_MODERATE", "SHORT_HIGH", "SHORT_MODERATE", or "NEUTRAL".

Validates: Requirements 6.1

Property 19: Reasoning Presence

For any trade setup, the reasoning field shall be populated with non-empty text explaining the recommendation.

Validates: Requirements 6.5

Property 20: Risk Level Validity

For any trade setup, the risk_level field shall be one of: "Low", "Medium", or "High".

Validates: Requirements 6.6

Property 21: Composite Score Inclusion

For any trade setup, the composite_score field shall be populated with the ticker's composite score.

Validates: Requirements 6.7

Property 22: API Bidirectional Response

For any ticker with trade setups, the API endpoint shall return both LONG and SHORT setups.

Validates: Requirements 7.1

Property 23: API Response Schema Completeness

For any trade setup returned by the API, the response shall include: confidence_score, targets array, conflict_flags array, recommended_action, reasoning, risk_level, and composite_score fields.

Validates: Requirements 7.2, 7.3, 7.5, 7.6

Property 24: API Target Object Schema

For any target object in the API response, it shall include: price, distance_from_entry, rr_ratio, probability, and classification fields.

Validates: Requirements 7.4

Property 25: API Response Ordering

For any API response containing multiple setups, setups shall be ordered by confidence score in descending order.

Validates: Requirements 7.7

Property 26: S/R Strength Retrieval

For any target generated, the S/R strength score shall be correctly retrieved from the SRLevel model and included in the target object.

Validates: Requirements 8.1

Property 27: Strength Score Normalization

For any S/R strength score used in probability calculation, it shall be normalized to the range [0, 1] before application.

Validates: Requirements 8.4

Property 28: ATR Retrieval

For any ticker being analyzed, the current ATR value shall be retrieved and used in target generation.

Validates: Requirements 9.1

Property 29: High Volatility Target Inclusion

For any ticker where ATR exceeds 5% of current price, the target generator shall include S/R levels up to 10x ATR distance as valid targets.

Validates: Requirements 9.2

Property 30: Low Volatility Target Restriction

For any ticker where ATR is below 2% of current price, the target generator shall limit targets to S/R levels within 3x ATR distance.

Validates: Requirements 9.3

Property 31: ATR Multiple Calculation

For any target, the distance_atr_multiple field shall equal (abs(target_price - entry_price)) / ATR.

Validates: Requirements 9.4

Property 32: Minimum Distance Filter

For any generated targets, no target shall be closer than 1x ATR from the entry price.

Validates: Requirements 9.5

Property 33: Timestamp Presence

For any generated trade setup, the detected_at field shall be populated with a timestamp.

Validates: Requirements 10.1

Property 34: Single Ticker Performance

For any single ticker recommendation generation, the operation shall complete within 500 milliseconds.

Validates: Requirements 14.1

Property 35: Batch Processing Resilience

For any batch of tickers, if recommendation generation fails for one ticker, the engine shall continue processing remaining tickers without stopping.

Validates: Requirements 14.5

Property 36: Dimension Score Query Efficiency

For any ticker recommendation generation, all required dimension scores shall be retrieved in a single database query.

Validates: Requirements 14.3

Property 37: TradeSetup Model Schema

The TradeSetup model shall include all required fields: confidence_score (Float), targets_json (Text), conflict_flags_json (Text), recommended_action (String), reasoning (Text), and risk_level (String).

Validates: Requirements 15.1, 15.2, 15.3, 15.4, 15.5, 15.6

Property 38: Backward Compatibility

For any trade setup, the existing fields (entry_price, stop_loss, target, rr_ratio) shall remain populated with the primary target data for backward compatibility.

Validates: Requirements 15.7

Error Handling

Service-Level Error Handling

recommendation_service.py:

async def enhance_trade_setup(...) -> TradeSetup:
    """Enhance trade setup with error handling."""
    try:
        # Calculate confidence
        confidence = direction_analyzer.calculate_confidence(...)
        
        # Generate targets
        targets = target_generator.generate_targets(...)
        
        # Estimate probabilities
        for target in targets:
            target["probability"] = probability_estimator.estimate_probability(...)
        
        # Detect conflicts
        conflicts = signal_conflict_detector.detect_conflicts(...)
        
        # Generate recommendation summary
        recommendation = _generate_recommendation_summary(...)
        
        # Update setup model
        setup.confidence_score = confidence
        setup.targets_json = json.dumps(targets)
        setup.conflict_flags_json = json.dumps(conflicts)
        setup.recommended_action = recommendation["action"]
        setup.reasoning = recommendation["reasoning"]
        setup.risk_level = recommendation["risk_level"]
        
        return setup
        
    except Exception as e:
        logger.exception(f"Error enhancing trade setup for {ticker.symbol}: {e}")
        # Return setup with minimal recommendation data
        setup.confidence_score = None
        setup.reasoning = f"Recommendation generation failed: {str(e)}"
        setup.risk_level = "High"
        return setup

Graceful Degradation:

• If recommendation enhancement fails, return base setup without recommendation data
• Log error for debugging but don't fail the entire scan
• Set risk_level to "High" to warn users of incomplete analysis

API-Level Error Handling

Trade Setup Endpoints:

@router.get("/trades")
async def get_trade_setups(
    direction: str | None = None,
    min_confidence: float | None = None,
    recommended_action: str | None = None,
    db: AsyncSession = Depends(get_db),
):
    """Get trade setups with validation."""
    try:
        # Validate parameters
        if min_confidence is not None and not (0 <= min_confidence <= 100):
            raise ValidationError("min_confidence must be between 0 and 100")
        
        if recommended_action is not None:
            valid_actions = ["LONG_HIGH", "LONG_MODERATE", "SHORT_HIGH", "SHORT_MODERATE", "NEUTRAL"]
            if recommended_action not in valid_actions:
                raise ValidationError(f"recommended_action must be one of: {', '.join(valid_actions)}")
        
        # Fetch and filter setups
        setups = await rr_scanner_service.get_trade_setups(db, direction)
        
        # Apply filters
        if min_confidence is not None:
            setups = [s for s in setups if s.get("confidence_score", 0) >= min_confidence]
        
        if recommended_action is not None:
            setups = [s for s in setups if s.get("recommended_action") == recommended_action]
        
        return {"status": "success", "data": setups}
        
    except ValidationError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        logger.exception(f"Error fetching trade setups: {e}")
        raise HTTPException(status_code=500, detail="Internal server error")

Admin Configuration Endpoints:

@router.put("/admin/settings/recommendations")
async def update_recommendation_config(
    config: RecommendationConfigUpdate,
    db: AsyncSession = Depends(get_db),
    _: User = Depends(require_admin),
):
    """Update recommendation configuration with validation."""
    try:
        # Validate thresholds (0-100)
        for key in ["high_confidence_threshold", "moderate_confidence_threshold", "confidence_diff_threshold"]:
            if hasattr(config, key):
                value = getattr(config, key)
                if value is not None and not (0 <= value <= 100):
                    raise ValidationError(f"{key} must be between 0 and 100")
        
        # Validate weights (0-1)
        for key in ["signal_alignment_weight", "sr_strength_weight", "distance_penalty_factor"]:
            if hasattr(config, key):
                value = getattr(config, key)
                if value is not None and not (0 <= value <= 1):
                    raise ValidationError(f"{key} must be between 0 and 1")
        
        # Update settings
        updated = await settings_service.update_recommendation_config(db, config.dict(exclude_unset=True))
        
        return {"status": "success", "data": updated}
        
    except ValidationError as e:
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        logger.exception(f"Error updating recommendation config: {e}")
        raise HTTPException(status_code=500, detail="Internal server error")

Data Validation

JSON Field Validation:

• Validate targets_json structure before parsing
• Handle malformed JSON gracefully
• Provide default empty arrays for missing data

Null Handling:

• All new TradeSetup fields are nullable
• Frontend checks for null before rendering
• API returns null fields explicitly (not omitted)

Testing Strategy

Dual Testing Approach

This feature requires both unit tests and property-based tests for comprehensive coverage:

Unit Tests: Verify specific examples, edge cases, and integration points Property Tests: Verify universal properties across all inputs using randomization

Together, these approaches ensure both concrete correctness (unit tests) and general correctness (property tests).

Property-Based Testing

Framework: Hypothesis (Python property-based testing library)

Configuration: Each property test shall run minimum 100 iterations to ensure comprehensive input coverage.

Test Organization: tests/property/test_recommendation_properties.py

Example Property Test:

from hypothesis import given, strategies as st
import pytest

@given(
    technical=st.floats(min_value=0, max_value=100),
    momentum=st.floats(min_value=0, max_value=100),
    sentiment=st.sampled_from(["bearish", "neutral", "bullish", None]),
)
@pytest.mark.property
def test_confidence_score_bounds(technical, momentum, sentiment):
    """Feature: intelligent-trade-recommendations, Property 3: Confidence Score Bounds
    
    For any trade setup (LONG or SHORT), the confidence score shall be 
    within the range [0, 100].
    """
    from app.services.recommendation_service import direction_analyzer
    
    dimension_scores = {
        "technical": technical,
        "momentum": momentum,
        "fundamental": 50.0,
    }
    
    # Test LONG direction
    long_confidence = direction_analyzer.calculate_confidence(
        direction="long",
        dimension_scores=dimension_scores,
        sentiment_classification=sentiment,
        conflicts=[],
    )
    assert 0 <= long_confidence <= 100, f"LONG confidence {long_confidence} out of bounds"
    
    # Test SHORT direction
    short_confidence = direction_analyzer.calculate_confidence(
        direction="short",
        dimension_scores=dimension_scores,
        sentiment_classification=sentiment,
        conflicts=[],
    )
    assert 0 <= short_confidence <= 100, f"SHORT confidence {short_confidence} out of bounds"

Property Test Tags: Each test includes a comment with format:

"""Feature: intelligent-trade-recommendations, Property {N}: {Property Title}

{Property description from design document}
"""

Unit Testing

Test Organization: tests/unit/test_recommendation_service.py

Unit Test Focus:

• Specific examples from requirements (e.g., bullish sentiment + high technical = high LONG confidence)
• Edge cases (e.g., fewer than 3 S/R levels available)
• Integration points (e.g., R:R scanner calling recommendation service)
• Error conditions (e.g., missing dimension scores, malformed data)

Example Unit Tests:

import pytest
from app.services.recommendation_service import direction_analyzer

def test_high_confidence_long_example():
    """Feature: intelligent-trade-recommendations, Requirement 2.3
    
    WHEN sentiment is bullish AND technical score is above 60 AND momentum 
    score is above 60, THE Direction_Analyzer SHALL assign LONG confidence 
    above 70%.
    """
    dimension_scores = {
        "technical": 75.0,
        "momentum": 68.0,
        "fundamental": 50.0,
    }
    
    confidence = direction_analyzer.calculate_confidence(
        direction="long",
        dimension_scores=dimension_scores,
        sentiment_classification="bullish",
        conflicts=[],
    )
    
    assert confidence > 70.0, f"Expected LONG confidence > 70%, got {confidence}"


def test_high_confidence_short_example():
    """Feature: intelligent-trade-recommendations, Requirement 2.4
    
    WHEN sentiment is bearish AND technical score is below 40 AND momentum 
    score is below 40, THE Direction_Analyzer SHALL assign SHORT confidence 
    above 70%.
    """
    dimension_scores = {
        "technical": 32.0,
        "momentum": 35.0,
        "fundamental": 50.0,
    }
    
    confidence = direction_analyzer.calculate_confidence(
        direction="short",
        dimension_scores=dimension_scores,
        sentiment_classification="bearish",
        conflicts=[],
    )
    
    assert confidence > 70.0, f"Expected SHORT confidence > 70%, got {confidence}"


def test_limited_targets_edge_case():
    """Feature: intelligent-trade-recommendations, Requirement 3.5
    
    WHEN fewer than 3 S/R levels exist in the target direction, THE 
    Target_Generator SHALL use the available levels and flag the setup 
    as having limited targets.
    """
    from app.services.recommendation_service import target_generator
    
    # Only 2 resistance levels available
    sr_levels = [
        SRLevel(price_level=150.0, type="resistance", strength=70),
        SRLevel(price_level=155.0, type="resistance", strength=65),
    ]
    
    targets = target_generator.generate_targets(
        direction="long",
        entry_price=145.0,
        stop_loss=142.0,
        sr_levels=sr_levels,
        atr_value=2.0,
    )
    
    assert len(targets) == 2, f"Expected 2 targets, got {len(targets)}"
    # Check for limited targets flag in reasoning or metadata

Test Fixtures (tests/conftest.py):

@pytest.fixture
def sample_dimension_scores():
    """Sample dimension scores for testing."""
    return {
        "technical": 65.0,
        "sr_quality": 70.0,
        "sentiment": 60.0,
        "fundamental": 55.0,
        "momentum": 62.0,
    }

@pytest.fixture
def sample_sr_levels():
    """Sample S/R levels for testing."""
    return [
        SRLevel(id=1, price_level=140.0, type="support", strength=75),
        SRLevel(id=2, price_level=145.0, type="support", strength=80),
        SRLevel(id=3, price_level=155.0, type="resistance", strength=70),
        SRLevel(id=4, price_level=160.0, type="resistance", strength=65),
        SRLevel(id=5, price_level=165.0, type="resistance", strength=60),
    ]

Frontend Testing

Framework: Vitest with React Testing Library

Test Organization: frontend/src/components/**/*.test.tsx

Component Tests:

import { describe, it, expect } from 'vitest';
import { render, screen } from '@testing-library/react';
import { RecommendationPanel } from './RecommendationPanel';

describe('RecommendationPanel', () => {
  it('displays LONG and SHORT setups side-by-side', () => {
    const longSetup = {
      direction: 'long',
      confidence_score: 75.0,
      recommended_action: 'LONG_HIGH',
      // ... other fields
    };
    
    const shortSetup = {
      direction: 'short',
      confidence_score: 45.0,
      recommended_action: 'SHORT_MODERATE',
      // ... other fields
    };
    
    render(
      <RecommendationPanel 
        symbol="AAPL"
        longSetup={longSetup}
        shortSetup={shortSetup}
      />
    );
    
    expect(screen.getByText(/LONG/i)).toBeInTheDocument();
    expect(screen.getByText(/SHORT/i)).toBeInTheDocument();
    expect(screen.getByText('75.0')).toBeInTheDocument();
    expect(screen.getByText('45.0')).toBeInTheDocument();
  });
  
  it('highlights recommended direction with visual emphasis', () => {
    const longSetup = {
      direction: 'long',
      confidence_score: 75.0,
      recommended_action: 'LONG_HIGH',
      // ... other fields
    };
    
    const shortSetup = {
      direction: 'short',
      confidence_score: 45.0,
      recommended_action: 'SHORT_MODERATE',
      // ... other fields
    };
    
    const { container } = render(
      <RecommendationPanel 
        symbol="AAPL"
        longSetup={longSetup}
        shortSetup={shortSetup}
      />
    );
    
    // LONG should have recommended styling
    const longCard = container.querySelector('[data-direction="long"]');
    expect(longCard).toHaveClass('border-green-500');
    
    // SHORT should have muted styling
    const shortCard = container.querySelector('[data-direction="short"]');
    expect(shortCard).toHaveClass('opacity-60');
  });
  
  it('displays conflict warnings when present', () => {
    const setupWithConflicts = {
      direction: 'long',
      confidence_score: 55.0,
      conflict_flags: [
        'sentiment-technical: Bearish sentiment conflicts with bullish technical (72)',
      ],
      // ... other fields
    };
    
    render(
      <RecommendationPanel 
        symbol="AAPL"
        longSetup={setupWithConflicts}
      />
    );
    
    expect(screen.getByText(/Bearish sentiment conflicts/i)).toBeInTheDocument();
  });
});

Integration Testing

End-to-End Flow Test:

@pytest.mark.asyncio
async def test_recommendation_generation_e2e(db_session, sample_ticker):
    """Test complete recommendation generation flow."""
    from app.services.rr_scanner_service import scan_ticker
    
    # Setup: Ensure ticker has all required data
    # - OHLCV records
    # - Dimension scores
    # - S/R levels
    # - Sentiment scores
    
    # Execute: Run scanner with recommendation enhancement
    setups = await scan_ticker(
        db=db_session,
        symbol=sample_ticker.symbol,
        rr_threshold=1.5,
        atr_multiplier=1.5,
    )
    
    # Verify: Both LONG and SHORT setups generated
    assert len(setups) == 2
    long_setup = next(s for s in setups if s.direction == "long")
    short_setup = next(s for s in setups if s.direction == "short")
    
    # Verify: Recommendation fields populated
    assert long_setup.confidence_score is not None
    assert long_setup.targets_json is not None
    assert long_setup.recommended_action is not None
    assert long_setup.reasoning is not None
    assert long_setup.risk_level is not None
    
    # Verify: Targets structure
    targets = json.loads(long_setup.targets_json)
    assert 3 <= len(targets) <= 5
    for target in targets:
        assert "price" in target
        assert "probability" in target
        assert "classification" in target

Performance Testing

Benchmark Tests:

import time
import pytest

@pytest.mark.benchmark
@pytest.mark.asyncio
async def test_single_ticker_performance(db_session, sample_ticker):
    """Feature: intelligent-trade-recommendations, Property 34
    
    For any single ticker recommendation generation, the operation shall 
    complete within 500 milliseconds.
    """
    from app.services.rr_scanner_service import scan_ticker
    
    start = time.time()
    await scan_ticker(db=db_session, symbol=sample_ticker.symbol)
    elapsed = (time.time() - start) * 1000  # Convert to ms
    
    assert elapsed < 500, f"Recommendation generation took {elapsed}ms, expected < 500ms"


@pytest.mark.benchmark
@pytest.mark.asyncio
async def test_batch_processing_throughput(db_session, sample_tickers):
    """Feature: intelligent-trade-recommendations, Property 14.2
    
    WHEN the scheduled job generates recommendations for all tickers, THE 
    Trade_Recommendation_Engine SHALL process at least 10 tickers per second.
    """
    from app.services.rr_scanner_service import scan_all_tickers
    
    start = time.time()
    await scan_all_tickers(db=db_session)
    elapsed = time.time() - start
    
    throughput = len(sample_tickers) / elapsed
    assert throughput >= 10, f"Throughput {throughput:.2f} tickers/sec, expected >= 10"

Test Coverage Goals

• Unit Tests: 80%+ code coverage for recommendation_service
• Property Tests: 100% coverage of all 38 correctness properties
• Integration Tests: Complete E2E flow from scanner to API response
• Frontend Tests: 70%+ coverage for recommendation components
• Performance Tests: Verify both single-ticker and batch performance targets

Implementation Roadmap

Phase 1: Backend Core (Week 1)

1. Create Alembic migration for TradeSetup model extensions
2. Implement direction_analyzer module with confidence calculation
3. Implement signal_conflict_detector module
4. Write unit tests for confidence scoring and conflict detection

Phase 2: Target Generation (Week 1-2)

1. Implement target_generator module with quality scoring
2. Implement probability_estimator module
3. Add volatility-based filtering logic
4. Write unit tests and property tests for target generation

Phase 3: Integration (Week 2)

1. Create recommendation_service orchestrator
2. Integrate with rr_scanner_service
3. Add SystemSetting configuration support
4. Write integration tests for complete flow

Phase 4: API Layer (Week 2-3)

1. Extend trade setup endpoints with filtering
2. Create admin configuration endpoints
3. Update Pydantic schemas for responses
4. Write API tests

Phase 5: Frontend (Week 3)

1. Create RecommendationPanel component
2. Enhance Scanner table with new columns
3. Add RecommendationSettings admin page
4. Write component tests

Phase 6: Testing & Optimization (Week 3-4)

1. Implement all 38 property-based tests
2. Run performance benchmarks
3. Optimize database queries if needed
4. Complete integration testing

Phase 7: Deployment (Week 4)

1. Run migration on staging database
2. Deploy backend to staging
3. Deploy frontend to staging
4. User acceptance testing
5. Production deployment

Success Metrics

• Correctness: All 38 properties pass with 100 iterations
• Performance: Single ticker < 500ms, batch >= 10 tickers/sec
• Coverage: 80%+ backend unit test coverage, 70%+ frontend coverage
• User Adoption: 80%+ of users interact with recommendation features within first month
• Accuracy: Track recommendation outcomes for future validation (Phase 2 feature)