gogo2/SCALPING_DASHBOARD_DYNAMIC_THROTTLING_SUMMARY.md

Scalping Dashboard Dynamic Throttling Implementation

Issues Fixed

1. Critical Dash Callback Error

Problem: TypeError: unhashable type: 'list' in Dash callback definition Solution: Fixed callback structure by removing list brackets around outputs and inputs

Before:

@self.app.callback(
    [Output(...), Output(...)],  # ❌ Lists cause unhashable type error
    [Input(...)]
)

After:

@self.app.callback(
    Output(...),  # ✅ Individual outputs
    Output(...),
    Input(...)    # ✅ Individual input
)

2. Unicode Encoding Issues

Problem: Windows console (cp1252) couldn't encode Unicode characters like ✓, ✅, ❌ Solution: Replaced all Unicode characters with ASCII-safe alternatives

Changes:

• ✓ → "OK"
• ✅ → "ACTIVE" / "OK"
• ❌ → "INACTIVE"
• Removed all emoji characters from logging

3. Missing Argument Parsing

Problem: run_scalping_dashboard.py didn't support command line arguments from launch.json Solution: Added comprehensive argument parsing

Added Arguments:

• --episodes (default: 1000)
• --max-position (default: 0.1)
• --leverage (default: 500)
• --port (default: 8051)
• --host (default: '127.0.0.1')
• --debug (flag)

Dynamic Throttling Implementation

Core Features

1. Adaptive Update Frequency

• Range: 500ms (fast) to 2000ms (slow)
• Default: 1000ms (1 second)
• Automatic adjustment based on performance

2. Performance-Based Throttling Levels

• Level 0: No throttling (optimal performance)
• Level 1-5: Increasing throttle levels
• Skip Factor: Higher levels skip more updates

3. Performance Monitoring

• Tracks: Callback execution duration
• History: Last 20 measurements for averaging
• Thresholds:
  • Fast: < 0.5 seconds
  • Slow: > 2.0 seconds
  • Critical: > 5.0 seconds

Dynamic Adjustment Logic

Performance Degradation Response

if duration > 5.0 or error:
    # Critical performance issue
    throttle_level = min(5, throttle_level + 2)
    update_frequency = min(2000, frequency * 1.5)
    
elif duration > 2.0:
    # Slow performance
    throttle_level = min(5, throttle_level + 1)
    update_frequency = min(2000, frequency * 1.2)

Performance Improvement Response

if duration < 0.5 and avg_duration < 0.5:
    consecutive_fast_updates += 1
    
    if consecutive_fast_updates >= 5:
        throttle_level = max(0, throttle_level - 1)
        if throttle_level <= 1:
            update_frequency = max(500, frequency * 0.9)

Throttling Mechanisms

1. Time-Based Throttling

• Prevents updates if called too frequently
• Minimum 80% of expected interval between updates

2. Skip-Based Throttling

• Skips updates based on throttle level
• Skip factor = throttle_level + 1
• Example: Level 3 = skip every 4th update

3. State Caching

• Stores last known good state
• Returns cached state when throttled
• Prevents empty/error responses

Client-Side Optimization

1. Fallback State Management

def _get_last_known_state(self):
    if self.last_known_state is not None:
        return self.last_known_state
    return safe_default_state

2. Performance Tracking

def _track_callback_performance(self, duration, success=True):
    # Track performance history
    # Adjust throttling dynamically
    # Log performance summaries

3. Smart Update Logic

def _should_update_now(self, n_intervals):
    # Check time constraints
    # Apply throttle level logic
    # Return decision with reason

Benefits

1. Automatic Load Balancing

• Adapts to system performance in real-time
• Prevents dashboard freezing under load
• Optimizes for best possible responsiveness

2. Graceful Degradation

• Maintains functionality during high load
• Provides cached data when fresh data unavailable
• Recovers automatically when performance improves

3. Performance Monitoring

• Logs detailed performance metrics
• Tracks trends over time
• Alerts on performance issues

4. User Experience

• Consistent dashboard responsiveness
• No blank screens or timeouts
• Smooth operation under varying loads

Configuration

Throttling Parameters

update_frequency = 1000      # Start frequency (ms)
min_frequency = 2000         # Maximum throttling (ms)
max_frequency = 500          # Minimum throttling (ms)
throttle_level = 0           # Current throttle level (0-5)

Performance Thresholds

fast_threshold = 0.5         # Fast performance (seconds)
slow_threshold = 2.0         # Slow performance (seconds)
critical_threshold = 5.0     # Critical performance (seconds)

Testing Results

✅ Fixed Issues

1. Dashboard starts successfully on port 8051
2. No Unicode encoding errors in Windows console
3. Proper argument parsing from launch.json
4. Dash callback structure works correctly
5. Dynamic throttling responds to load

✅ Performance Features

1. Adaptive frequency adjusts automatically
2. Throttling levels prevent overload
3. State caching provides fallback data
4. Performance monitoring tracks metrics
5. Graceful recovery when load decreases

Usage

Launch from VS Code

Use the launch configuration: "💹 Live Scalping Dashboard (500x Leverage)"

Command Line

python run_scalping_dashboard.py --port 8051 --leverage 500

Monitor Performance

Check logs for performance summaries:

PERFORMANCE SUMMARY: Avg: 1.2s, Throttle: 2, Frequency: 1200ms

Conclusion

The scalping dashboard now has robust dynamic throttling that:

• Automatically balances performance vs responsiveness
• Prevents system overload through intelligent throttling
• Maintains user experience even under high load
• Recovers gracefully when conditions improve
• Provides detailed monitoring of system performance

The dashboard is now production-ready with enterprise-grade performance management.