VIX Spike/Drop Tracker

١٧ نوفمبر

What It Does
Core Functionality:

Monitors daily VIX percentage changes and identifies significant spikes (fear increases) and drops (fear decreases)
Backtests historical SPY performance following VIX events across multiple timeframes (1-day, 2-day, 3-day, and 1-week)
Calculates win rates and average returns for each scenario
Provides real-time VIX statistics including percentile rankings, distance from moving average, and momentum

Key Features:

Historical Analysis: Analyzes up to 2,500 bars of historical data to build robust statistical profiles
Dual Event Tracking: Separate statistics for VIX spikes (fear events) and VIX drops (fear subsiding)
Performance Metrics: Shows average SPY returns and win rates at 1, 2, 3, and 5-day intervals
VIX Context: Real-time VIX level, daily change, moving average distance, percentile rank, and 3-day momentum
Smart Predictions: Context-aware signals based on VIX patterns and consecutive spike/drop days
Visual Alerts: Chart annotations and background highlighting for significant events

How to Use It
Setup:

Add the indicator to your chart (works on any timeframe, but analyzes daily data)
Adjust the VIX threshold (default 10%) to define what constitutes a "significant" move
Set the historical lookback period (500 bars = ~2 years of data)

Reading the Statistics Table:

Left side: Statistics for VIX spikes (when fear increases)
Right side: Statistics for VIX drops (when fear decreases)
D1/D2/D3/Wk columns: Average SPY returns 1, 2, 3, and 5 days after the VIX event
Win %: Percentage of time SPY was positive at each interval
n: Sample size (number of historical events analyzed)
Bottom row: Current VIX statistics and market positioning

Interpreting Predictions:

🔴 "FEAR SPIKE - SPY LIKELY DOWN": VIX spiking, historical data shows negative SPY tendency
🟢 "FEAR SUBSIDING - SPY LIKELY UP": VIX dropping, historical data shows positive SPY tendency
🟠 "VIX ELEVATED - MEAN REVERSION DUE": VIX extended above its moving average
🟡 "VIX SUPPRESSED - COMPLACENCY RISK": VIX unusually low, potential volatility expansion ahead

Best Practices

Use this as a statistical context tool, not a standalone trading signal
Combine with your own technical analysis and risk management
Pay attention to win rates alongside average returns for edge assessment
Monitor consecutive spike/drop days for potential mean reversion setups
Higher sample sizes (n) provide more reliable statistics

Customization
Fully customizable colors, threshold levels, table size, and VIX moving average period. Enable/disable chart labels based on your preference for clean charts.

This indicator provides historical statistical context and does not guarantee future performance. Past results do not predict future outcomes.

قبل ٧ أيام

ملاحظات الأخبار

Overview
VIX Spike/Drop Tracker analyzes SPY performance following VIX volatility events. Backtests historical data to quantify equity responses after fear spikes versus fear subsiding, providing statistical context for volatility-based positioning.
What Makes This Unique
Dual-Direction Statistics: Maintains separate performance profiles for VIX spikes (fear increases) and VIX drops (fear decreases). Tracks up to 2,500 bars and calculates SPY returns and win rates at 1, 2, 3, and 5-day intervals for each scenario.
Context-Aware Signals: Analyzes VIX positioning relative to moving average, percentile rankings, and 3-day momentum. Distinguishes between spikes from suppressed levels versus already-extended VIX, adjusting predictions accordingly.
Multi-Timeframe Tracking: Measures SPY performance across four timeframes (D+1, D+2, D+3, D+5) to identify optimal holding periods after volatility events.
How It Works
Event Detection:

Daily VIX % change = (VIX_close - VIX_previous) / VIX_previous × 100
Spike flagged when change > threshold (default 10%)
Drop flagged when change < -threshold

Performance Measurement:

For each event, measures SPY returns at D+1, D+2, D+3, D+5
Return = (SPY_future - SPY_event) / SPY_event × 100
Positive return = "win" for win rate calculation
Statistics aggregated separately for spikes vs drops

Statistical Engine:

Average return = sum(returns) / count(events)
Win rate = count(positive) / count(total) × 100
Rolling calculations over lookback period (default 500 bars)

VIX Context:

MA Distance = (VIX - VIX_MA) / VIX_MA × 100
Percentile = % of period where VIX below current
Signals combine event type, historical stats, and VIX positioning

How to Use
Statistical Context: When VIX moves beyond threshold, reference the statistics table to understand historical SPY behavior at various forward timeframes. Sample size (n) indicates statistical confidence.
Multi-Day Analysis: Compare performance across D+1, D+2, D+3, and D+5 intervals to identify patterns. Higher returns at D+3 vs D+1 may indicate delayed market responses.
VIX Positioning: Elevated percentile rankings (>75) combined with spike events often correlate with subsequent mean reversion behavior.
Pattern Recognition: Consecutive spike or drop days show different forward performance than isolated events. The statistics track these patterns separately.
Key Settings
VIX Threshold (10%): Minimum change to qualify as event. Lower = more events, less significance. Higher = extreme events only.
Lookback (500): Bars analyzed for statistics. 500 ≈ 2 years. Increase for long-term stats, decrease for recent regime focus.
VIX MA Period (20): For distance and mean reversion signals. Standard monthly cycle.
Why Protected Source
The statistical aggregation algorithms, multi-timeframe correlation logic, and context-aware signal generation combine multiple analytical components in proprietary ways that represent the author's unique approach to VIX-SPY relationship analysis.
Important Disclaimers
Not Financial Advice. Educational tool only. Does not provide trading signals or recommendations.
Statistical Limitations. Past performance doesn't guarantee future results. Market regime changes can invalidate historical patterns.
Sample Size Matters. Lower n values (<20) provide less reliable statistics. Check sample size before acting on data.
Timeframe Dependency. Measured at fixed intervals may miss optimal timing between measurement points.
Always use proper risk management and consider this as one input among many in your analysis.