gogo2/ANNOTATE/REALTIME_INFERENCE_GUIDE.md

Real-Time Inference Guide

🎯 Overview

Real-time inference mode runs your trained model on live streaming data from the DataProvider, updating charts every second and displaying model predictions in real-time.

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Starting Real-Time Inference

Step 1: Select Model

Choose the model you want to run from the dropdown in the training panel.

Step 2: Click "Start Live Inference"

• Button turns red: "Stop Inference"
• Live mode banner appears at top
• Charts begin updating every second
• Model predictions displayed

Visual Indicators

• 🔴 LIVE banner at top of page
• Green status box in training panel
• Update counter showing number of updates
• Signal markers on charts (🔵 BUY, 🔴 SELL)

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📊 What Updates in Real-Time

Charts (Every 1 Second)

• All 4 timeframes update with latest data
• Candlesticks show new price action
• Volume bars update with new volume
• Smooth updates without page refresh

Model Signals

• Latest prediction displayed (BUY/SELL/HOLD)
• Confidence level shown as percentage
• Signal markers added to charts
• Last 10 signals kept visible

Data Source

• Uses DataProvider's cached data
• Same data as main trading system
• Updates from exchange feeds
• 1-second resolution

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🎨 Visual Elements

Live Mode Banner

🔴 LIVE | Real-Time Inference Active
Charts updating with live data every second
[X updates]

Signal Markers on Charts

• 🔵 BUY - Green marker with arrow
• 🔴 SELL - Red marker with arrow
• Timestamp - When signal was generated
• Price - Price at signal time

Training Panel Status

🔴 LIVE
Signal: BUY
Confidence: 75.3%
Charts updating every 1s

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🛑 Stopping Real-Time Inference

Click "Stop Inference"

• Live mode banner disappears
• Charts stop updating
• Signal markers remain visible
• Can review final signals

What Happens

• Inference loop terminates
• Chart updates stop
• Last 100 signals saved
• Model remains loaded

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📈 Monitoring Performance

Watch For

• Signal frequency - How often model signals
• Confidence levels - Higher is better (>70%)
• Signal accuracy - Do signals make sense?
• False positives - Signals that shouldn't happen

Good Signs

• Signals at key levels (support/resistance)
• High confidence (>70%)
• Signals match your analysis
• Few false positives

Warning Signs

• Too many signals (every second)
• Low confidence (<50%)
• Random signals
• Signals don't match patterns

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🔧 Technical Details

Update Frequency

• Charts: 1 second
• Signals: 1 second
• Model inference: 1 second

Data Flow

DataProvider (Live Data)
    ↓
Latest Market State (4 timeframes)
    ↓
Model Inference
    ↓
Prediction (Action + Confidence)
    ↓
Update Charts + Display Signal

Performance

• Latency: ~100-200ms per update
• CPU Usage: Moderate (model inference)
• Memory: Stable (no leaks)
• Network: Minimal (uses cached data)

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💡 Tips & Tricks

Tip 1: Watch Multiple Timeframes

All 4 charts update simultaneously. Watch for:

• Alignment across timeframes
• Divergences between timeframes
• Pattern confirmation

Tip 2: Monitor Confidence

• >80%: Very strong signal
• 70-80%: Strong signal
• 50-70%: Moderate signal
• <50%: Weak signal (ignore)

Tip 3: Compare with Annotations

• Do live signals match your annotations?
• Are signals at similar price levels?
• Is timing similar to your trades?

Tip 4: Test Different Models

• Try CNN vs DQN vs Transformer
• Compare signal quality
• Note which performs best

Tip 5: Use for Validation

• After training, test with live inference
• Verify model learned correctly
• Check for overfitting

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🐛 Troubleshooting

Charts Not Updating

Issue: Live mode active but charts frozen

Solutions:

• Check browser console for errors
• Verify DataProvider has live data
• Refresh page and restart inference
• Check network tab for failed requests

No Signals Generated

Issue: Status shows "HOLD" constantly

Solutions:

• Model may need more training
• Check model is loaded correctly
• Verify market conditions (model may correctly hold)
• Try different model

Signals Too Frequent

Issue: Signal every second

Solutions:

• Model may be overtrained
• Need more negative examples in training
• Adjust confidence threshold
• Retrain with better annotations

Performance Issues

Issue: Browser slow/laggy

Solutions:

• Close other tabs
• Reduce number of visible timeframes
• Stop inference when not needed
• Clear browser cache

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📊 Example Session

Scenario: Testing CNN After Training

1. Preparation

• Trained CNN on 20 breakout annotations
• Model learned breakout patterns
• Ready to test on live data

2. Start Inference

• Select "StandardizedCNN"
• Click "Start Live Inference"
• 🔴 LIVE banner appears
• Charts begin updating

3. Observation (5 minutes)

• Charts update smoothly
• Model generates 2 BUY signals
• Both at resistance breakouts
• Confidence: 78% and 82%

4. Validation

• Signals match training patterns
• Timing is precise
• No false positives
• Model learned correctly

5. Stop Inference

• Click "Stop Inference"
• Review signal history
• Model performs well
• Ready for production

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🎯 Best Practices

Before Starting

• Train model first
• Verify model loaded
• Check DataProvider has data
• Close unnecessary tabs

During Inference

• Monitor all timeframes
• Note signal quality
• Check confidence levels
• Compare with your analysis

After Stopping

• Review signal history
• Note performance
• Identify improvements
• Adjust training if needed

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Summary

Real-time inference provides:

Live chart updates (1/second)
Model predictions in real-time
Signal markers on charts
Confidence levels displayed
Performance monitoring built-in

Use it to:

• Validate training - Check model learned correctly
• Test models - Compare different models
• Monitor performance - Track signal quality
• Debug issues - Identify problems

Result: Confidence that your model works correctly before deploying to production! 🎯