| name | wealth-management |
| description | Wealth management, financial planning, and investment analytics skill. Compounds on finance-substrate for Colombian tax-optimized wealth building. Runs descriptive (portfolio health), predictive (compound growth projections, Monte Carlo), and prescriptive (allocation, rebalancing, tax-efficient withdrawal) analytics. Use when: (1) projecting long-term wealth growth, (2) optimizing asset allocation, (3) running scenario/stress tests on a portfolio, (4) planning tax-efficient contributions or withdrawals, (5) generating a wealth dashboard or net worth timeline, (6) goal-based financial planning (retirement, housing, education). Triggers on: 'wealth management', 'compound interest', 'asset allocation', 'portfolio projection', 'net worth forecast', 'retirement planning', 'investment strategy', 'rebalancing', 'monte carlo', 'financial plan'.
|
Wealth Management
Long-term wealth building, investment analytics, and financial planning engine.
Builds on finance-substrate for data ingestion (bank certificates, patrimonio,
TRM rates, salary history) and adds forward-looking projection, optimization,
and scenario analysis.
Architecture
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:
from patrimonio_calc import compute_patrimonio
from tax_projection import project_tax
from budget_planner import estimate_annual_tax
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
