| name | stanley-druckenmiller-investment |
| description | Druckenmiller Strategy Synthesizer - Integrates 8 upstream skill outputs (Market Breadth, Uptrend Analysis, Market Top, Macro Regime, FTD Detector, VCP Screener, Theme Detector, CANSLIM Screener) into a unified conviction score (0-100), pattern classification, and allocation recommendation. Use when user asks about overall market conviction, portfolio positioning, asset allocation, strategy synthesis, or Druckenmiller-style analysis. Triggers on queries like "What is my conviction level?", "How should I position?", "Run the strategy synthesizer", "Druckenmiller analysis", "総合的な市場判断", "確信度スコア", "ポートフォリオ配分", "ドラッケンミラー分析". |
Druckenmiller Strategy Synthesizer
Purpose
Synthesize outputs from 8 upstream analysis skills (5 required + 3 optional) into a single composite conviction score (0-100), classify the market into one of 4 Druckenmiller patterns, and generate actionable allocation recommendations. This is a meta-skill that consumes structured JSON outputs from other skills — it requires no API keys of its own.
When to Use This Skill
English:
- User asks "What's my overall conviction?" or "How should I be positioned?"
- User wants a unified view synthesizing breadth, uptrend, top risk, macro, and FTD signals
- User asks about Druckenmiller-style portfolio positioning
- User requests strategy synthesis after running individual analysis skills
- User asks "Should I increase or decrease exposure?"
- User wants pattern classification (policy pivot, distortion, contrarian, wait)
Japanese:
- 「総合的な市場判断は？」「今のポジショニングは？」
- ブレッドス、アップトレンド、天井リスク、マクロの統合判断
- 「エクスポージャーを増やすべき？減らすべき？」
- 「ドラッケンミラー分析を実行して」
- 個別スキル実行後の戦略統合レポート
Input Requirements
Required Skills (5)
| # | Skill | JSON Prefix | Role |
|---|
| 1 | Market Breadth Analyzer | market_breadth_ | Market participation breadth |
| 2 | Uptrend Analyzer | uptrend_analysis_ | Sector uptrend ratios |
| 3 | Market Top Detector | market_top_ | Distribution / top risk (defense) |
| 4 | Macro Regime Detector | macro_regime_ | Macro regime transition (1-2Y structure) |
| 5 | FTD Detector | ftd_detector_ | Bottom confirmation / re-entry (offense) |
Optional Skills (3)
| # | Skill | JSON Prefix | Role |
|---|
| 6 | VCP Screener | vcp_screener_ | Momentum stock setups (VCP) |
| 7 | Theme Detector | theme_detector_ | Theme / sector momentum |
| 8 | CANSLIM Screener | canslim_screener_ | Growth stock setups + M(Market Direction) |
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:
- Load and validate all upstream skill JSON reports
- Extract normalized signals from each skill
- Calculate 7 component scores (weighted 0-100)
- Compute composite conviction score
- Classify into one of 4 Druckenmiller patterns
- Generate target allocation and position sizing
- 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
| # | Component | Weight | Source Skill(s) | Key Signal |
|---|
| 1 | Market Structure | 18% | Breadth + Uptrend | Market participation health |
| 2 | Distribution Risk | 18% | Market Top (inverted) | Institutional selling risk |
| 3 | Bottom Confirmation | 12% | FTD Detector | Re-entry signal after correction |
| 4 | Macro Alignment | 18% | Macro Regime | Regime favorability |
| 5 | Theme Quality | 12% | Theme Detector | Sector momentum health |
| 6 | Setup Availability | 10% | VCP + CANSLIM | Quality stock setups |
| 7 | Signal Convergence | 12% | All 5 required | Cross-skill agreement |
4 Pattern Classifications
| Pattern | Trigger Conditions | Druckenmiller Principle |
|---|
| Policy Pivot Anticipation | Transitional regime + high transition probability | "Focus on central banks and liquidity" |
| Unsustainable Distortion | Top risk >= 60 + contraction/inflationary regime | "How much you lose when wrong matters most" |
| Extreme Sentiment Contrarian | FTD confirmed + high top risk + bearish breadth | "Most money made in bear markets" |
| Wait & Observe | Low conviction + mixed signals (default) | "When you don't see it, don't swing" |
Conviction Zone Mapping
| Score | Zone | Exposure | Guidance |
|---|
| 80-100 | Maximum Conviction | 90-100% | Fat pitch - swing hard |
| 60-79 | High Conviction | 70-90% | Standard risk management |
| 40-59 | Moderate Conviction | 50-70% | Reduce position sizes |
| 20-39 | Low Conviction | 20-50% | Preserve capital, minimal risk |
| 0-19 | Capital Preservation | 0-20% | Maximum defense |
Output Files
druckenmiller_strategy_YYYY-MM-DD_HHMMSS.json — Structured analysis datadruckenmiller_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
| Skill | Relationship | Time Horizon |
|---|
| Market Breadth Analyzer | Input (required) | Current snapshot |
| Uptrend Analyzer | Input (required) | Current snapshot |
| Market Top Detector | Input (required) | 2-8 weeks tactical |
| Macro Regime Detector | Input (required) | 1-2 years structural |
| FTD Detector | Input (required) | Days-weeks event |
| VCP Screener | Input (optional) | Setup-specific |
| Theme Detector | Input (optional) | Weeks-months thematic |
| CANSLIM Screener | Input (optional) | Setup-specific |
| This Skill | Synthesizer | Unified conviction |
