Monte Carlo simulation engine for Finance Guru's 4-layer dividend income + margin living strategy. Runs 10,000 market scenarios to project income probabilities, margin safety, and portfolio outcomes over 28 months.
Workflow Routing
| Workflow | Trigger | File |
|---|---|---|
| RunSimulation | "run monte carlo", "simulate portfolio", "stress test" | workflows/RunSimulation.md |
| IncorporateBuyTicket | "include buy ticket", "add ticket to simulation" | workflows/IncorporateBuyTicket.md |
Examples
Example 1: Run standard Monte Carlo simulation
User: "Run the monte carlo simulation with current portfolio"
-> Invokes RunSimulation workflow
-> Derives current values, then updates the hard-coded inputs in run_single_scenario()
-> Runs 10,000 scenarios with v3.0 4-layer model
-> Outputs JSON summary + full CSV + Excel to analysis/
Example 2: Incorporate a buy ticket into simulation
User: "Run monte carlo with my new buy ticket from 12-31"
-> Invokes IncorporateBuyTicket workflow
-> Reads buy ticket from tickets/buy-ticket-2025-12-31-*.md
-> Parses YAML frontmatter + Execution Summary table from the canonical ticket format
-> Adjusts starting portfolio values based on ticket allocations
-> Runs simulation with updated positions
Example 3: Stress test margin safety
User: "What's my margin call probability?"
-> Invokes RunSimulation workflow
-> Focuses on margin_call_rate and margin_ratio metrics
-> Reports 5th percentile (worst case) margin ratio
Key Metrics Produced
Success Metrics
- P($100k income) - Probability of reaching $100k annual dividend income
- P($75k income) - Probability of reaching $75k annual dividend income
- P($50k income) - Probability of reaching $50k annual dividend income
- Margin call rate - % of scenarios triggering margin call (<3:1 ratio)
- Backstop usage rate - % of scenarios requiring business income injection
Portfolio Metrics
- Total portfolio value - Median, P5, P95 at month 28
- Layer 1 (Growth) - PLTR, TSLA, VOO, etc. (no new deployment)
- Layer 2 (Income) - Dividend funds (recurring W2-funded monthly deployment)
- Layer 3 (Hedge) - SQQQ ($800/month deployment)
- GOOGL position - Scale-in ($1,000/month deployment)
Risk Metrics
- Margin ratio - Portfolio / Margin debt (must stay >3:1)
- Max drawdown - Worst peak-to-trough decline
- Break-even timing - When dividends cover margin draws
Output Files
All outputs saved to analysis/:
monte-carlo-v3-{date}.json- Summary statisticsmonte-carlo-v3-full-results-{date}.csv- All 10,000 scenariosmonte-carlo-v3-analysis-{date}.xlsx- Excel workbook with charts
Configuration
The instance-local script at strategies/dividend_margin_monte_carlo.py reads starting portfolio values that are hard-coded in run_single_scenario(). It does not auto-detect values from CSV. Before each run, follow the RunSimulation workflow to derive current values and edit those assignments.
Simulation parameters include:
- Starting portfolio values (manually set in
run_single_scenario()) - Monthly deployment amounts
- Bucket allocations and yields
- Margin schedule
- Market regime probabilities
Model Version
v3.0 (Jan 2026) - Full 4-layer portfolio:
- Layer 1: Growth portfolio (market returns only, no new deployment)
- Layer 2: Income portfolio (5-bucket dividend allocation)
- Layer 3: Hedge (SQQQ for crisis protection)
- GOOGL: Scale-in position (diverted from Layer 2)
Fixes applied:
- Floor at $0 for all positions (stocks can't go negative)
- Full portfolio margin ratio (all layers count toward Fidelity margin)
- Operator-supplied starting values derived from current portfolio data