Understanding Blindspots
Individual models generate predictions for specific assets. When tracking multiple models for an asset, their collective sentiment should ideally align with actual price movement. Blindspots occur when your tracked models fail to recognize significant market moves - periods where sentiment was wrong relative to what actually happened.
These gaps in coverage reveal systematic problems with your model tracking portfolio. Understanding and fixing blindspots improves the reliability of your aggregate sentiment.
What Are Blindspots
A blindspot is a time period where your tracked models' collective sentiment misclassified market conditions relative to actual price movement. The models were collectively "blind" to what was happening in the market.
Examples:
Sentiment was bullish or neutral during a strong downward price move
Sentiment was bearish or neutral during a strong upward price move
Sentiment indicated strong conviction in the wrong direction
Not every misalignment creates a blindspot. Small discrepancies between sentiment and price are normal - models can't perfectly predict every move. Blindspots specifically identify periods where sentiment was significantly wrong during notable market moves.
Why Blindspots Occur
Blindspots arise from systematic bias in the models you track:
Directional bias - If you track primarily bullish models, your aggregate sentiment will skew bullish even during bearish market periods. The models you chose create inherent bias in your coverage.
Pattern gaps - Models learn specific patterns during training. If current market conditions fall outside those learned patterns, the models won't recognize them correctly. Tracking models trained on similar data creates shared blind spots.
Lagging recognition - Some models take several days to recognize trend changes. If most of your tracked models lag, your sentiment will consistently miss early-stage moves.
Quality issues - Models that learned poor patterns during training generate systematically inaccurate predictions. Tracking multiple low-quality models compounds their errors rather than canceling them out.
The key insight: blindspots are not random. They reveal systematic problems with your model selection that can be identified and corrected.
Visualization
Blindspots appear on the sentiment timeline as marked periods below the chart. Each period shows whether your sentiment correctly or incorrectly classified market conditions:
Correct periods (✓) - Sentiment aligned with price movement. Bullish sentiment during upward moves, bearish sentiment during downward moves, or neutral sentiment during range-bound periods.
Incorrect periods (✗) - Sentiment misaligned with price movement. These are your blindspots.
The timeline divides into distinct periods based on price behavior. Each period is evaluated independently to determine if your tracked models correctly classified conditions during that specific timeframe.
Types of Blindspots
Blindspots are categorized by the type of move missed and the direction:
Bullish Blindspots
These occur when models fail to recognize bullish conditions:
Bullish Entry Miss - Price began moving upward, but sentiment remained bearish or neutral. Your models missed the start of a bullish period.
Example: Bitcoin starts rallying from $40k to $45k over 5 days. Your sentiment stays at -0.8 (bearish) for the first 3 days before slowly turning bullish. Those first 3 days are a bullish entry miss - you tracked models that couldn't recognize the rally beginning.
Bullish Exit Miss - Price stopped moving upward and turned bearish, but sentiment remained bullish too long. Your models held bullish classification after conditions changed.
Example: Ethereum tops at $3,200 and starts declining. Your sentiment stays at +1.5 (bullish) for 4 days into the decline. Those 4 days are a bullish exit miss - your models didn't recognize the bullish period ending.
Bearish Blindspots
These occur when models fail to recognize bearish conditions:
Bearish Entry Miss - Price began moving downward, but sentiment remained bullish or neutral. Your models missed the start of a bearish period.
Example: Stock starts declining from $150 to $140 over 6 days. Your sentiment stays at +0.6 (bullish) for the first 4 days. Those 4 days are a bearish entry miss - your models couldn't recognize the selloff beginning.
Bearish Exit Miss - Price stopped moving downward and turned bullish, but sentiment remained bearish too long. Your models held bearish classification after conditions changed.
Example: Gold bottoms at $1,800 and starts rallying. Your sentiment stays at -1.2 (bearish) for 3 days into the rally. Those 3 days are a bearish exit miss - your models didn't recognize the bearish period ending.
Measuring Coverage Quality
Several metrics quantify how well your tracked models cover market conditions:
Accuracy
Coverage accuracy measures the percentage of time periods where sentiment correctly classified market conditions:
Example: Over 30 days analyzed:
18 periods correctly classified
12 periods incorrectly classified (blindspots)
Accuracy: 18/30 = 60%
Accuracy above 60% indicates reasonably reliable tracking coverage. Below 50% suggests serious systematic problems - your models are wrong more often than right.
Missed Moves Count
The raw count of blindspot periods by type:
Bullish misses - Number of periods where bullish moves were missed
Bearish misses - Number of periods where bearish moves were missed
Example:
30 days analyzed produced 25 distinct periods
3 bullish entry misses
2 bullish exit misses
4 bearish entry misses
1 bearish exit miss
Total: 10 blindspots out of 25 periods (40% blindspot rate)
Strength of Missed Moves
Not all blindspots are equally significant. Missing a 2% move matters less than missing a 15% move.
The system calculates the magnitude of price movement during each blindspot period. Blindspots during strong moves indicate more serious coverage problems than blindspots during weak moves.
Example:
Bearish entry miss #1: Missed 8% downward move
Bearish entry miss #2: Missed 3% downward move
Bearish entry miss #3: Missed 12% downward move
Miss #3 is the most problematic - you tracked models that completely missed a significant downward move.
Fixing Blindspots
When blindspot analysis identifies systematic bias, the system recommends specific models to address the gap.
Recommendations are targeted based on your specific bias:
Bullish bias detected - System suggests models with bearish tendencies for the same asset
Bearish bias detected - System suggests models with bullish tendencies for the same asset
Balanced tracking - No directional recommendations, focus on overall quality
Model Selection Criteria
Recommended models are filtered by:
Opposite bias - If you have bullish bias and missed bearish moves, recommendations show models with demonstrated bearish classification patterns.
Same asset - Only models tracking the same asset you're analyzing appear in recommendations. Adding bearish Bitcoin models won't fix your bullish bias on Ethereum.
Performance threshold - Only models meeting minimum quality standards appear. The system won't recommend adding poor-quality models just to balance bias.
How to Use Recommendations
When the system shows recommended models:
Review the models' track records - Look at their live performance, not just rarity tier
Check their classification patterns - Do they actually show opposite bias from your current tracking?
Add 1-3 models initially - Don't completely overhaul tracking all at once
Re-evaluate after 2-4 weeks - Check if new models improved coverage or created different problems
Practical Example
Your Bitcoin tracking portfolio:
8 models tracked
Analysis shows: 2 bullish misses, 9 bearish misses
Strong bullish bias identified
Missed 3 significant downward moves (8%, 12%, 6%)
System recommends 5 models with bearish tendencies for Bitcoin. You review their track records and add 2 that show consistent bearish classification during historical downturns.
After 3 weeks:
Bearish misses reduced from 9 to 4
Bullish misses increased from 2 to 3
More balanced: closer to 1:1 ratio
Coverage accuracy improved from 58% to 67%
The new models didn't eliminate all blindspots, but they significantly reduced systematic bearish bias.
Limitations
Blindspot analysis requires history - The system needs weeks or months of data to identify patterns. Newly tracked models don't immediately reveal blindspots.
Past bias doesn't guarantee future bias - Models can change behavior as market conditions evolve. A model with bearish tendency during one market regime might behave differently in another.
Adding models doesn't guarantee improvement - More models can introduce new problems. If recommended models have quality issues, they might improve directional balance while reducing overall accuracy.
Blindspots can't be eliminated - No model combination perfectly tracks all market moves. The goal is reducing systematic bias, not achieving perfect coverage.
Fixes take time to validate - After adding recommended models, you need several weeks to see if the change actually improved coverage. Quick evaluation can be misleading.
Key Takeaways
Blindspots are systematic, not random - They reveal directional bias in your model tracking portfolio that can be identified and addressed.
Bias matters more during mismatches - Bullish bias doesn't hurt when markets are bullish. It becomes critical during bearish periods when your sentiment will be systematically wrong.
Balance improves reliability - Tracking models with diverse directional tendencies produces more reliable aggregate sentiment across different market conditions.
Accuracy is the ultimate metric - The percentage of correctly classified periods matters more than the raw count of blindspots. 70% accuracy with some bias can be more useful than 50% accuracy with perfect balance.
Fixes require patience - Adding recommended models takes weeks to show results. Frequent portfolio changes prevent meaningful evaluation.
Quality trumps quantity - Adding more models to fix bias only helps if those models are reasonably accurate. Poor quality models just create different problems.
FAQ
How many blindspots is too many?
It depends on accuracy rather than raw count. If 70% of periods are correctly classified, having some blindspots is normal. If accuracy drops below 50%, the tracking portfolio has serious problems regardless of how many total blindspots exist.
Should I immediately add all recommended models?
No. Add 1-3 models initially and evaluate results over several weeks. Adding too many models at once makes it impossible to determine which changes helped or hurt.
Can a model have bias and still be high quality?
Yes. A model can be excellent at identifying bullish conditions while poor at recognizing bearish conditions. High-rarity models can absolutely have directional bias - they just consistently perform well in their preferred direction.
What if I don't have any blindspots?
Either: (1) your tracking portfolio is genuinely balanced and effective, or (2) you haven't tracked long enough for analysis to identify patterns. A few weeks isn't sufficient - several months of data provides more reliable bias detection.
Do I need equal numbers of bullish and bearish models?
No. Balance comes from aggregate behavior, not equal counts. You could track 8 slightly bullish models and 2 strongly bearish models and achieve reasonable balance if the aggregate sentiment reflects market conditions accurately.
What if adding recommended models makes things worse?
Remove them and try different models from the recommendation list. Not every recommended model will improve your specific portfolio. The system suggests candidates based on bias patterns, but you need to evaluate if they actually help.
Should I remove models that contribute to bias?
Not necessarily. If a model has strong bullish bias but excellent performance during bullish periods, removing it eliminates valuable signal. Better approach: add models with opposite bias rather than removing useful models.
How often should I review blindspot analysis?
Monthly is reasonable. More frequent reviews don't provide enough new data to identify meaningful patterns. Less frequent reviews allow problematic bias to persist too long.