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”.

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)

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