Druckenmiller Strategy Synthesizer

Input Requirements

Required Skills (5)

Optional Skills (3)

Run the required skills first. The synthesizer reads their JSON output from reports/.

Execution Workflow

Phase 1: Verify Prerequisites

Check that the 5 required skill JSON reports exist in reports/ and are recent (< 72 hours). If any are missing, run the corresponding skill first.

Phase 2: Execute Strategy Synthesizer

python3 skills/stanley-druckenmiller-investment/scripts/strategy_synthesizer.py \
  --reports-dir reports/ \
  --output-dir reports/ \
  --max-age 72

The script will:

1. Load and validate all upstream skill JSON reports
2. Extract normalized signals from each skill
3. Calculate 7 component scores (weighted 0-100)
4. Compute composite conviction score
5. Classify into one of 4 Druckenmiller patterns
6. Generate target allocation and position sizing
7. Output JSON and Markdown reports

Phase 3: Present Results

Present the generated Markdown report, highlighting:

• Conviction score and zone
• Detected pattern and match strength
• Strongest and weakest components
• Target allocation (equity/bonds/alternatives/cash)
• Position sizing parameters
• Relevant Druckenmiller principle

Phase 4: Provide Druckenmiller Context

Load appropriate reference documents to provide philosophical context:

• High conviction: Emphasize concentration and "fat pitch" principles
• Low conviction: Emphasize capital preservation and patience
• Pattern-specific: Apply relevant case study from references/case-studies.md

7-Component Scoring System

4 Pattern Classifications

Conviction Zone Mapping

Output Files

• druckenmiller_strategy_YYYY-MM-DD_HHMMSS.json — Structured analysis data
• druckenmiller_strategy_YYYY-MM-DD_HHMMSS.md — Human-readable report

API Requirements

None. This skill reads JSON outputs from other skills. No API keys required.

Reference Documents

references/investment-philosophy.md

• Core Druckenmiller principles: concentration, capital preservation, 18-month horizon
• Quantitative rules: daily vol targets, max position sizing
• Load when providing philosophical context for conviction assessment

references/market-analysis-guide.md

• Signal-to-action mapping framework
• Macro regime interpretation for allocation decisions
• Load when explaining component scores or allocation rationale

references/case-studies.md

• Historical examples: 1992 GBP, 2000 tech bubble, 2008 crisis
• Pattern classification examples with actual market conditions
• Load when user asks about historical parallels

references/conviction_matrix.md

• Quantitative signal-to-action mapping tables
• Market Top Zone x Macro Regime matrix
• Load when user needs precise exposure numbers for specific signal combinations

When to Load References

• First use: Load investment-philosophy.md for framework understanding
• Allocation questions: Load market-analysis-guide.md + conviction_matrix.md
• Historical context: Load case-studies.md
• Regular execution: References not needed — script handles scoring

Relationship to Other Skills