Stanley Druckenmiller Investment

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”.

No API

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Druckenmiller Strategy Synthesizer

2. When to Use

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

3. Prerequisites

  • API Key: None required
  • Python 3.9+ recommended

4. Quick Start 

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

5. 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

6. Resources

References:

  • skills/stanley-druckenmiller-investment/references/case-studies.md
  • skills/stanley-druckenmiller-investment/references/conviction_matrix.md
  • skills/stanley-druckenmiller-investment/references/investment-philosophy.md
  • skills/stanley-druckenmiller-investment/references/market-analysis-guide.md

Scripts:

  • skills/stanley-druckenmiller-investment/scripts/allocation_engine.py
  • skills/stanley-druckenmiller-investment/scripts/report_generator.py
  • skills/stanley-druckenmiller-investment/scripts/report_loader.py
  • skills/stanley-druckenmiller-investment/scripts/scorer.py
  • skills/stanley-druckenmiller-investment/scripts/strategy_synthesizer.py