Systematic Event Analysis

Systematic Event Analysis Framework

Showcase

• Iran War Asset Impact Dashboard: https://iran-war-dashboard.vercel.app — Built by an OpenClaw agent (Mushroom) using this framework. Tracks 27 assets across commodities, defense, energy, airlines, and shipping sectors. Auto-updated every hour via cron.

Core Philosophy

"Don't predict prices. Detect when market reaction diverges from logical event impact."

This framework helps you systematically answer three questions:

1. What happened? → Event classification and impact assessment
2. What should be affected? → Asset-event mapping via scoring
3. What is the market missing? → Divergence detection and signal generation

The Three-Layer Funnel

┌─────────────────────────────────────────────────────────────┐
│  LAYER 1: EVENTS (What happened?)                          │
│  ─────────────────────────────                              │
│  • Classify: Industry / Policy / Sentiment                 │
│  • Verify: Tier 1 facts vs Tier 2 reports                  │
│  • Timestamp: When did it happen vs when was it known?     │
└─────────────────────────┬───────────────────────────────────┘
                          │ maps to
                          ▼
┌─────────────────────────────────────────────────────────────┐
│  LAYER 2: ASSETS (What should be affected?)                │
│  ─────────────────────────────────                          │
│  • Score: 0-100 (victim to beneficiary)                    │
│  • Document: Rationale for each score                      │
│  • Validate: Does theory match similar historical events?  │
└─────────────────────────┬───────────────────────────────────┘
                          │ reveals
                          ▼
┌─────────────────────────────────────────────────────────────┐
│  LAYER 3: SIGNALS (What is the market missing?)            │
│  ────────────────────────────────────────                   │
│  • Compare: Expected vs actual returns                     │
│  • Detect: Score divergence, time lag, cross-market gaps   │
│  • Act: Position sizing based on signal strength           │
└─────────────────────────────────────────────────────────────┘

How to Use This Framework

Progressive Disclosure

This skill uses progressive disclosure—load only what you need:

1. SKILL.md (this file): Core concepts and workflow
2. references/event-typing.md: Detailed event classification guidelines
3. references/asset-categories.md: Asset selection checklists by event type
4. references/dashboard-patterns.md: UI/UX design patterns
5. references/quick-reference.md: Printable reference tables

When to Load References

Layer 1: Event Classification

The Three Event Types

Every event falls into exactly one of these categories:

🔴 Type A: Industry Shock

Definition: Direct impact on industry operations—supply, demand, or production capacity

Sub-types:

• Supply Shock: Facility destruction, logistics disruption, production stoppage
• Demand Shock: Demand surge/collapse, customer loss, substitution
• Operational Shock: Infrastructure damage, labor issues, regulatory halt

Key Characteristics:

• Immediate price impact (minutes to hours)
• Sector-specific effects
• Often irreversible in short term

Investment Question:

"What operational capacity was affected, and how quickly can it recover?"

See references/event-typing.md for:

• Detailed classification criteria
• Decision tree for event typing
• Examples for each sub-type
• Quality checklist

🟡 Type B: Policy / Political

Definition: Government action or political development changing operating environment

Key Characteristics:

• Medium-term impact (days to weeks)
• Affects sectors through regulatory changes
• Can be reversed or modified

Examples:

• Sanctions and trade restrictions
• Tax policy changes
• Regulatory approvals/rejections
• Elections and regime changes

Investment Question:

"What is the scope, certainty, and enforcement likelihood?"

🔵 Type C: Sentiment / Cognitive

Definition: Changes in expectations, predictions, or market psychology without physical/policy change

Key Characteristics:

• Short-term volatility (hours to days)
• Can reverse quickly
• Often precedes physical/policy events

Examples:

• Analyst predictions and forecasts
• Media narrative shifts
• Prediction market movements

Investment Question:

"Is this new information or confirmation? How credible is the source?"

Layer 2: Asset-Event Mapping

The Scoring System

Scale: 0-100

• 0-20: Severe victim (existential risk)
• 21-40: Clear victim (negative impact)
• 41-60: Neutral/Mixed (conflicting factors)
• 61-80: Clear beneficiary (positive impact)
• 81-100: Major beneficiary (transformative impact)

Reference point: 50 = market-neutral

Asset Selection

Tier 1 (9-12 core): Direct exposure, liquid, differentiated responses Tier 2 (10-15 secondary): Indirect exposure, correlation analysis Tier 3 (5-10 monitoring): Sentiment indicators, expansion options

Maximum total: 30 assets

See references/asset-categories.md for:

• Asset checklists by event type (geopolitical, pandemic, election, climate)
• Scoring methodology
• Quality checklist

Layer 3: Signal Detection

Three Types of Divergence

Type 1: Score Divergence

Logic: High-score assets should outperform low-score assets

expected_return = (score - 50) / 50 * 10  # -10% to +10%
divergence = expected_return - actual_return

Strength Thresholds:

• STRONG: |divergence| ≥ 5%
• MODERATE: |divergence| ≥ 3%
• WEAK: |divergence| ≥ 1.5%

Type 2: Time Divergence

Logic: Markets should price in events within reasonable time

Interpretation:

• Lag > 4 hours (intraday): Market may have missed it
• Lag > 1 day (major events): Significant information gap

Type 3: Cross-Market Divergence

Logic: Related markets should move together

Interpretation:

• Spread > 5%: Arbitrage or information gap exists
• Spot > Futures: Market expects reversal

Signal Quality Factors

Confidence Multipliers:

• Tier 1 source: +20%
• High liquidity: +10%
• Multiple signals agree: +15%
• Event < 24 hrs: +10%

Action Thresholds:

• Act immediately: Signal score ≥ 80
• Monitor closely: Signal score 50-79
• Track for pattern: Signal score 25-49
• Ignore: Signal score < 25

From Framework to Production

When You Need External Tools

This framework is methodology-only. When you need to implement, look for skills or tools that provide:

Note: These are recommendations based on proven effectiveness. The framework works with any equivalent tools available in your environment.

For Dashboard Deployment: Vercel is recommended because:

• Free tier generous for personal/small projects
• Automatic HTTPS and CDN
• Git integration for auto-deploy
• Easy Next.js/React hosting

For Asset Research: deep-research skill is recommended because:

• Structured, comprehensive analysis output
• Source citation and quality tiering
• JSON output for programmatic consumption
• Cost estimation before execution

Common Pitfalls

1. Over-Engineering

Wrong: Complex ML models to predict prices Right: Simple score divergence catches most opportunities

2. Event Saturation

Wrong: Track every headline Right: 90% of "events" are noise; focus on industry shocks

3. Static Scoring

Wrong: Set scores once and forget Right: Rescore weekly as event evolves

4. Ignoring Liquidity

Wrong: Trade illiquid assets on small divergences Right: Require larger divergences for illiquid positions

5. Confirmation Bias

Wrong: Only look for confirming evidence Right: Actively seek disconfirming evidence

Decision Trees

Should I Act on This Signal?

Signal strength STRONG?
├── NO → Don't act
│
└── YES
    ├── Source is Tier 1?
    │   ├── NO → Reduce position size
    │
    ├── Asset is liquid?
    │   ├── NO → Reduce position size
    │
    ├── Multiple signals agree?
    │   ├── NO → Wait for confirmation
    │
    └── Event recent?
        ├── NO → Check if opportunity still exists
        
    → Calculate position size
    → Set stop-loss
    → Document rationale

What Type of Event Is This?

Physical damage or operational impact?
├── YES → 🔴 Industry Shock
│
└── NO
    ├── Government action?
    │   ├── YES → 🟡 Policy/Political
    │
    └── NO
        ├── Prediction or sentiment shift?
        │   ├── YES → 🔵 Sentiment/Cognitive
        │
        └── NO → Not trackable (discard)

Quick Reference

See references/quick-reference.md for:

• Event classification guide
• Scoring scale (0-100)
• Signal strength thresholds
• Confidence multipliers

Summary: The Core Loop

1. EVENT → Classify (Industry/Policy/Sentiment)
         → Verify (Tier 1/2 source?)
         → Timestamp (occurrence vs knowledge)

2. ASSETS → Select (20-30 relevant assets)
          → Score (0-100, beneficiary vs victim)
          → Document (rationale for each)

3. SIGNALS → Calculate (expected vs actual)
           → Detect (divergences)
           → Filter (confidence multipliers)
           → Act (position sizing)

4. LEARN → Track (signal performance)
         → Iterate (update scores, refine)
         → Document (build pattern library)

Remember:

• The goal is better decision-making, not perfect prediction
• Start simple, iterate based on usage
• Framework first, tools second