Wealth Management

User-provided (portfolio input)

Portfolio data stored at ~/.wealth-management/portfolio.json.

Skill Modes

1. summary — Portfolio Health Dashboard (Descriptive)

Current-state analysis of all holdings aggregated from finance-substrate and user portfolio data.

Script: scripts/portfolio_summary.py --year 2025

Outputs:

• Net worth breakdown by asset class (cash, fixed income, equities, real estate, pension)
• Allocation pie: actual vs target %
• Concentration risk: any single position > 20% of portfolio
• Currency exposure: COP vs USD vs other
• Year-over-year growth: patrimonio líquido trajectory
• Liquidity analysis: liquid vs illiquid assets

2. project — Compound Growth Projection (Predictive)

Forward-looking wealth projection with configurable assumptions.

Script: scripts/project_wealth.py --years 20 --monthly-contribution-usd 2000

Inputs:

• Starting capital (from patrimonio or manual)
• Monthly/annual contribution amount
• Expected real return by asset class (default: equities 7%, bonds 3%, RE 5%)
• Inflation assumption (Colombia CPI: ~5-7%, US CPI: ~2-3%)
• Tax drag (from finance-substrate effective rate)
• TRM trend assumption (mean-reverting to historical average)

Outputs:

• Year-by-year table: contributions, growth, taxes, net value
• Milestones: when you hit $100M, $500M, $1B COP or $100K, $500K, $1M USD
• Contribution vs growth ratio over time (crossover point)
• Inflation-adjusted purchasing power
• Sensitivity table: ±2% return scenarios

Formulas:

FV = PV × (1 + r)^n + PMT × [((1+r)^n - 1) / r]
Real return = nominal - inflation - tax_drag
CAGR = (Ending / Beginning)^(1/Years) - 1

3. goal — Goal-Based Financial Planning (Predictive)

Reverse-engineer: given a target, what's needed?

Script: scripts/goal_planner.py --target-usd 500000 --target-date 2035

Inputs:

• Target amount (COP or USD)
• Target date
• Current savings (from patrimonio)
• Risk tolerance (conservative / moderate / aggressive)
• Income growth assumption

Outputs:

• Required monthly savings (COP + USD)
• Required return rate to meet goal with current savings only
• Probability of success (linked to Monte Carlo)
• Gap analysis: on track / behind / ahead
• Recommended asset allocation for the goal's time horizon

4. allocation — Asset Allocation Strategy (Prescriptive)

Recommend an optimal asset allocation based on risk profile and time horizon.

Script: scripts/allocate_assets.py --risk moderate --horizon 15

Framework: Modified Bogle Three-Fund + Colombian Extensions

Colombian-specific considerations:

• AFC cuenta as cash/fixed income (tax-deferred, housing-eligible)
• Pensión voluntaria (Skandia) = long-term equity proxy (10yr lock)
• Cesantías = forced savings (annual withdrawal allowed)
• Colombian equities (BVC) vs international via DolarApp/ARQ or US brokerage
• TRM hedging: maintain USD reserves for FX diversification

Outputs:

• Target allocation table
• Current vs target delta
• Rebalancing trades needed
• Tax impact of rebalancing (from finance-substrate tax projection)

5. rebalance — Tactical Rebalancing (Prescriptive)

Generate specific trades to bring portfolio back to target.

Script: scripts/rebalance.py --threshold 5

Inputs:

• Current holdings (from portfolio.json + certificates)
• Target allocation (from allocation mode or manual)
• Drift threshold (default: 5% absolute deviation triggers rebalance)
• Tax sensitivity (minimize realized gains)

Outputs:

• Trades to execute (buy/sell, amount, account)
• Tax impact estimate (short-term vs long-term gains)
• Priority order (tax-loss harvest first, then rebalance)
• "Do nothing" zones where drift is within tolerance

6. scenario — Monte Carlo & Stress Testing (Predictive)

Simulate portfolio outcomes under uncertainty.

Script: scripts/scenario_analysis.py --simulations 10000 --years 20

Scenarios:

• Monte Carlo: 10,000 simulations with log-normal returns, historical volatility
• Historical stress: 2008 GFC, 2020 COVID, 2022 rate hike, 1999 Colombian crisis
• COP devaluation: TRM shock (+30%, +50%)
• Stagflation: High inflation (10%) + low growth (0%) for 5 years
• Career disruption: 0 income for 6-12 months

Outputs:

• Success probability (% of simulations meeting goal)
• Percentile outcomes: P10, P25, P50, P75, P90
• Worst-case scenario: minimum portfolio value
• Sequence-of-returns risk: early vs late bear market impact
• Safe withdrawal rate for given success probability

7. optimize — Tax-Efficient Strategy (Prescriptive)

Maximize after-tax wealth growth using Colombian tax law.

Script: scripts/optimize_strategy.py --year 2025

Strategies analyzed:

1. Contribution ordering: AFC vs voluntaria vs libre inversión

   • AFC: tax-deferred, 10yr lock or housing withdrawal
   • Voluntaria: tax-deferred, 10yr lock or pension age
   • Libre: no tax benefit, full liquidity
   • Decision depends on marginal tax rate and cap utilization (1,340 UVT)

2. Account type placement: Which assets in which account?

   • High-growth (equities) → tax-deferred (voluntaria/AFC) for tax-free compounding
   • Income-producing (bonds, rendimientos) → taxable, claim INCR deduction
   • International (USD equities) → DolarApp/ARQ for FX diversification

3. Withdrawal sequencing (for wealth distribution phase):

   • Taxable accounts first (lower tax rate on capital gains)
   • AFC for housing needs (tax-free withdrawal)
   • Voluntaria after 10yr + pension age (tax-free)
   • Cesantías annually (forced, taxable)

4. Tax-loss harvesting: Identify positions with unrealized losses to offset gains

Outputs:

• Optimal contribution plan (monthly amounts by account)
• Account placement recommendations
• 5-year after-tax growth comparison: optimized vs naive
• Marginal benefit table (extra $1M COP in each account → after-tax impact)

Integration with finance-substrate

wealth-management imports directly from finance-substrate scripts:

# Import patrimonio for current net worth
from patrimonio_calc import compute_patrimonio

# Import tax projection for effective rates
from tax_projection import project_tax

# Import budget for contribution capacity
from budget_planner import estimate_annual_tax

# Read salary trajectory
salary = load_jsonl("~/.finance-substrate/tax/salary-history.jsonl")

Data Directory

~/.wealth-management/
├── portfolio.json          # Current holdings (user-maintained)
├── targets.json            # Target allocation profiles
├── goals.json              # Financial goals with timelines
├── projections/            # Saved projection results
│   └── projection-YYYY-MM-DD.json
├── scenarios/              # Monte Carlo results
│   └── scenario-YYYY-MM-DD.json
└── history/                # Net worth snapshots over time
    └── networth-history.jsonl

References

Key Formulas

Colombian-Specific Parameters

Asset Class Expected Returns (Real, After Inflation)

Compound: Autoany Integration (EGRI)

This skill is EGRI-aware. When a user's request implies iterative optimization of savings strategies, contribution plans, or investment horizons, the agent should scaffold a problem-spec and delegate to /autoany.

Optimization Triggers

Invoke /autoany when the user asks to:

• "Optimize contributions" — AFC vs pension vs brokerage split
• "Maximize probability" — probability of reaching a financial goal
• "Best allocation for my goals" — multi-horizon allocation
• "How should I split my savings" — tax-efficient contribution ordering
• "Run simulations" — with optimization intent (not just a single run)
• "Stress test my plan" — combined with finding a better plan

EGRI Problem-Spec Templates

Templates are at templates/egri/.

Delegation Flow

1. User request → agent detects optimization intent
2. Load personal context:
   - patrimonio from finance-substrate (starting capital)
   - salary trajectory (budget constraint)
   - TRM rates (COP/USD conversion)
   - existing goals from ~/.wealth-management/goals.json
3. Scaffold problem-spec from template
4. Invoke /autoany
5. EGRI loop: Proposer → Executor (scenario_analysis.py) → Evaluator → Selector
6. Return promoted plan + ledger summary
7. Show concrete action items:
   - "Increase AFC contributions to $X/month"
   - "Shift 10% from fixed income to equities in retirement bucket"

EGRI Evaluator Bridge

scenario_analysis.py --egri outputs structured Outcome for autoany:

• Score: probability_of_goal_pct (0-100)
• Constraints: median_max_drawdown_pct > -25, probability_of_ruin_pct <= 5
• Metrics: full Monte Carlo statistics for the proposer to learn from

Safety Constraints (enforced in EGRI loops)

• All simulations use historical/synthetic data only (no live data risk)
• Contribution plans are advisory — no automatic financial actions
• AFC + pensión voluntaria combined cap: 1,340 UVT (~$66.7M COP)
• Monthly contribution cannot exceed income
• Ruin probability must stay below 5%
• Budget: 20-40 trials max, 10-40 minutes total

Related Skills

• finance-substrate — Data layer: bank certificates, patrimonio, tax projection, salary history, TRM rates
• investment-management — Execution layer: security screening, scoring, market data, trade execution, factor analysis, backtesting
• autoany — EGRI framework for recursive improvement loops

Dependencies

• Python 3.10+
• finance-substrate skill (data layer — certificates, patrimonio, tax, salary)
• autoany (optional, for EGRI optimization loops)
• numpy (optional, for Monte Carlo simulations)
• No paid services. All data stays local.

File Structure

wealth-management/
├── SKILL.md                          # This file
├── skill.json                        # Schema definition (7 modes)
├── scripts/
│   ├── portfolio_summary.py          # Mode 1: descriptive dashboard
│   ├── project_wealth.py             # Mode 2: compound growth projection
│   ├── goal_planner.py               # Mode 3: goal-based planning
│   ├── allocate_assets.py            # Mode 4: asset allocation strategy
│   ├── rebalance.py                  # Mode 5: tactical rebalancing
│   ├── scenario_analysis.py          # Mode 6: Monte Carlo & stress tests
│   └── optimize_strategy.py          # Mode 7: tax-efficient strategy
├── references/
│   ├── compounding-formulas.md       # Mathematical foundations
│   ├── colombian-investment-landscape.md  # Local market reference
│   └── tax-efficiency-strategies.md  # Withdrawal ordering, harvesting
├── templates/
│   └── egri/                         # EGRI problem-spec templates (autoany)
│       ├── contribution-optimization.yaml  # Savings split optimization
│       └── horizon-evaluation.yaml         # Multi-goal horizon allocation
├── .control/
│   └── policy.yaml                   # Rebalancing thresholds, contribution caps
└── README.md