Documentation Features Test

This page demonstrates various rich content features available in our documentation.

Mermaid Diagrams

Flow Charts

Sequence Diagrams

State Diagrams

Mathematical Equations

Inline Math

The correlation coefficient is calculated as $r = \frac{\sum{(x_i - \bar{x})(y_i - \bar{y})}}{\sqrt{\sum{(x_i - \bar{x})^2}\sum{(y_i - \bar{y})^2}}}$

Block Equations

Opportunity Score Formula:

Where:

• $S_{opportunity}$ = Overall opportunity score (0-100)
• $w_i$ = Weight of driver $i$
• $r_i$ = Correlation coefficient (r-value)
• $c_i$ = Confidence multiplier based on sample size
• $n$ = Number of active drivers

Standard Deviation:

Expected Return:

Code Blocks

Python Example

    
    def calculate_opportunity_score(drivers):
    """Calculate weighted opportunity score from multiple drivers."""
    score = 0
    total_weight = 0

    for driver in drivers:
        # Weight by correlation strength and sample size
        weight = abs(driver.r_value) * min(driver.sample_size / 100, 1.0)
        contribution = driver.r_value * driver.confidence * weight

        score += contribution
        total_weight += weight

    # Normalize to 0-100 scale
    normalized_score = (score / total_weight) * 100 if total_weight > 0 else 0
    return max(0, min(100, normalized_score))

  

SQL Query Example

    
    -- Find top opportunities with high confidence
SELECT
    symbol,
    opportunity_score,
    confidence_score,
    direction,
    COUNT(*) as num_drivers
FROM opportunities o
JOIN opportunity_drivers od ON o.id = od.opportunity_id
WHERE
    opportunity_score >= 70
    AND confidence_score >= 60
GROUP BY symbol, opportunity_score, confidence_score, direction
ORDER BY opportunity_score DESC, confidence_score DESC
LIMIT 10;

  

JavaScript Example

    
    // Real-time score updates
const useOpportunityScore = (symbol) => {
  const score = ref(0)
  const drivers = ref([])

  watchEffect(() => {
    const activeDrivers = drivers.value.filter(d => d.isActive)
    score.value = calculateScore(activeDrivers)
  })

  return { score, drivers }
}

  

Tables

Comparison Table

Statistical Significance Table

Callouts & Alerts

Lists

Ordered Process

1. Data Collection
   • Gather market events (8-K filings, Form 4s)
   • Collect price data
   • Store in database
2. Signal Processing
   • Calculate technical indicators
   • Analyze insider activity
   • Compute correlations
3. Opportunity Scoring
   • Weight drivers by strength
   • Adjust for confidence
   • Normalize to 0-100 scale
4. User Notification
   • Filter by user preferences
   • Rank by score
   • Send alerts

Checklist

• Review opportunity score (is it 70+?)
• Check confidence level (is it 60+?)
• Examine top 3 drivers (do they make sense?)
• Verify direction matches your strategy
• Check current price vs historical range
• Review recent news/events
• Set stop loss and take profit levels
• Execute trade

Visual Elements

Badges & Labels

Status Indicators:

ACTIVEFAILEDPENDING

Progress Indicators

Confidence Level:

Tabs (if supported)

Example Strategies

Momentum Trading:

• Look for scores 70+ with strong technical drivers
• Direction: Bullish preferred
• Timeframe: 5-20 days

Mean Reversion:

• Look for scores 70+ with negative correlation drivers
• Direction: Counter-trend
• Timeframe: 3-10 days

Insider Following:

• Look for high insider buying correlation
• Confidence: 60+ required
• Timeframe: 20-60 days

Embedded Content

YouTube Video (example)

Interactive Elements

Expandable Section

    
    def calculate_weighted_score(drivers):
    """
    Detailed implementation with all edge cases handled.
    """
    if not drivers:
        return 0

    weighted_sum = 0
    total_weight = 0

    for driver in drivers:
        # Calculate confidence multiplier
        if driver.sample_size >= 100:
            conf_mult = 1.0
        elif driver.sample_size >= 50:
            conf_mult = 0.8
        else:
            conf_mult = 0.6

        # Calculate weight
        weight = abs(driver.r_value) * conf_mult

        # Add to weighted sum
        weighted_sum += driver.r_value * 100 * weight
        total_weight += weight

    return weighted_sum / total_weight if total_weight > 0 else 0

  

Key Takeaways

Remember: The best opportunities combine high scores with high confidence and have drivers that make intuitive sense in the current market environment.

Next Steps

Want to see these features in action? Check out:

• Reading Opportunity Scores
• Understanding Drivers