finance-substrate (data layer)
  ├── certificates.jsonl     → current holdings, bank saldos
  ├── patrimonio_calc.py     → net worth snapshot (R29/R30/R31)
  ├── tax_projection.py      → annual tax liability
  ├── salary-history.jsonl   → income trajectory
  └── trm-history.jsonl      → FX rates
        ↓
wealth-management (analytics layer)
  ├── Descriptive:  portfolio health, allocation drift, performance
  ├── Predictive:   compound growth, Monte Carlo, goal feasibility
  └── Prescriptive: rebalancing trades, contribution strategy, withdrawal order

Data Sources

From finance-substrate (automatic)

Source	Data	Used by
`certificates.jsonl`	Bank saldos, pension funds, cesantías, investment funds	All modes
`exogena.jsonl`	Real estate (Marval), vehicle, stocks (Ecopetrol)	`summary`, `project`
`salary-history.jsonl`	Income trajectory (monthly USD + TRM)	`project`, `goal`
`patrimonio_calc.py`	Net worth aggregation (deduplication)	`summary`, `project`
`trm-history.jsonl`	USD/COP exchange rates	FX conversion

User-provided (portfolio input)

Source	Format	Data
Investment holdings	JSON/CSV	Ticker, units, cost basis, account type
Target allocation	JSON	Asset class → target %
Goals	JSON	Name, target amount, target date, priority

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

Risk Profile	Equities	Fixed Income	Real Estate	Cash/AFC
Conservative	30%	50%	10%	10%
Moderate	55%	25%	10%	10%
Aggressive	75%	10%	10%	5%

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:

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)
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
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)
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

Formula	Expression	Use
Future Value	`FV = PV(1+r)^n + PMT[((1+r)^n - 1)/r]`	Compound growth
CAGR	`(FV/PV)^(1/n) - 1`	Historical return
Real Return	`(1+nominal)/(1+inflation) - 1`	Purchasing power
Sharpe Ratio	`(R_p - R_f) / σ_p`	Risk-adjusted return
Safe Withdrawal	`Annual spend / Portfolio value`	Distribution phase
Tax Drag	`r_nominal × effective_tax_rate`	After-tax return
Rule of 72	`72 / r`	Years to double

Colombian-Specific Parameters

Parameter	Value	Source
UVT 2025	$49,799 COP	DIAN
AFC/VP cap	1,340 UVT ($66.7M)	Art. 336, Ley 2277/2022
Pensión oblig. rate	16% of IBC	Ley 797/2003
Colombian CPI (2024)	~6.1%	DANE
US CPI (2024)	~2.9%	BLS
Avg TRM 2025	~4,052 COP/USD	datos.gov.co
BVC COLCAP return (5yr avg)	~8-12% nominal	BVC
S&P 500 return (10yr avg)	~12% nominal	Historical
CDT rates (2024)	~10-13% nominal	Banks
FIC (fondos inversión colectiva)	~7-10% nominal	Skandia/Davivienda

Asset Class Expected Returns (Real, After Inflation)

Asset Class	Conservative	Moderate	Aggressive
Colombian equities (BVC)	3%	5%	7%
US equities (S&P 500)	5%	7%	9%
Colombian CDT/bonds	2%	3%	4%
Real estate (Colombia)	3%	5%	6%
Pension voluntaria (Skandia)	3%	5%	7%
AFC (Davivienda)	1%	2%	2%
Cash/savings	0%	0%	0%

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

Template	Artifact	Evaluator	Score	Use When
`contribution-optimization`	`contribution_plan.yaml`	`scenario_analysis.py --egri`	P(goal)	Optimizing savings splits
`horizon-evaluation`	`horizon_plan.yaml`	`eval_horizon.py --egri`	Risk-adjusted P(all goals)	Multi-goal allocation

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

Data Sources

From finance-substrate (automatic)

User-provided (portfolio input)

Skill Modes

1. summary — Portfolio Health Dashboard (Descriptive)

2. project — Compound Growth Projection (Predictive)

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

4. allocation — Asset Allocation Strategy (Prescriptive)

5. rebalance — Tactical Rebalancing (Prescriptive)

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

7. optimize — Tax-Efficient Strategy (Prescriptive)

Integration with finance-substrate

Data Directory

References

Key Formulas

Colombian-Specific Parameters

Asset Class Expected Returns (Real, After Inflation)

Compound: Autoany Integration (EGRI)

Optimization Triggers

EGRI Problem-Spec Templates

Delegation Flow

EGRI Evaluator Bridge

Safety Constraints (enforced in EGRI loops)

Related Skills

Dependencies

File Structure

1. `summary` — Portfolio Health Dashboard (Descriptive)

2. `project` — Compound Growth Projection (Predictive)

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

4. `allocation` — Asset Allocation Strategy (Prescriptive)

5. `rebalance` — Tactical Rebalancing (Prescriptive)

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

7. `optimize` — Tax-Efficient Strategy (Prescriptive)

wealth-management

Wealth Management

Architecture