# Robo-Advisor Package

## Overview

Automated investment advisory platform with portfolio allocation, rebalancing, tax-loss harvesting, and goal-based planning.

## Features

### Portfolio Allocation
- **Modern Portfolio Theory (MPT)**: Mean-variance optimization
- **Black-Litterman Model**: Bayesian asset allocation
- **Risk Parity**: Equal risk contribution
- **Factor-Based**: Multi-factor smart beta
- **Goal-Based**: Allocation aligned with client goals

### Rebalancing
- **Threshold-Based**: Rebalance when drift exceeds 5%
- **Periodic**: Monthly, quarterly, or annual rebalancing
- **Tax-Aware**: Minimize capital gains taxes
- **Cash-Flow**: Rebalance with new contributions

### Tax Optimization
- **Tax-Loss Harvesting**: Automated loss harvesting
- **Tax Lot Optimization**: HIFO, LIFO, specific ID
- **Asset Location**: Tax-efficient account placement
- **Municipal Bonds**: Tax-advantaged income
- **Qualified Dividends**: Minimize ordinary income

### Risk Assessment
- **Risk Questionnaire**: 10-question assessment
- **Behavioral Analysis**: Loss aversion detection
- **Time Horizon**: Goal-specific time frames
- **Risk Capacity**: Financial ability to take risk
- **Risk Tolerance**: Psychological willingness

## Regulatory Compliance

### Investment Adviser Act
- **Form ADV Part 2**: Disclosure brochure
- **Fiduciary Duty**: Best interest standard
- **Conflicts of Interest**: Disclosure and mitigation

### Regulation Best Interest (Reg BI)
- **Care Obligation**: Reasonable diligence
- **Disclosure Obligation**: Material facts
- **Conflict of Interest Obligation**: Mitigation
- **Compliance Obligation**: Policies and procedures

### FINRA Rules
- **Rule 2111**: Suitability requirements
- **Rule 3110**: Supervision
- **Rule 4512**: Customer account information

## API Reference

### Client Onboarding

```typescript
POST /api/v1/clients
{
  "name": "string",
  "email": "string",
  "age": number,
  "riskProfile": {
    "riskScore": 1-10,
    "timeHorizon": number,
    "liquidityNeeds": "low" | "medium" | "high"
  },
  "goals": [
    {
      "type": "retirement" | "education" | "purchase",
      "targetAmount": number,
      "targetDate": "YYYY-MM-DD"
    }
  ]
}
```

### Portfolio Management

```typescript
GET /api/v1/portfolios/{portfolioId}
Response: {
  "totalValue": 100000,
  "cashBalance": 5000,
  "allocation": {
    "stocks": 60.0,
    "bonds": 30.0,
    "alternatives": 10.0
  },
  "performance": {
    "ytd": 8.5,
    "oneYear": 12.3,
    "inception": 45.6
  }
}

POST /api/v1/portfolios/{portfolioId}/rebalance
{
  "method": "threshold" | "periodic",
  "taxAware": true
}
```

### Tax Optimization

```typescript
GET /api/v1/portfolios/{portfolioId}/tax-loss-harvesting
Response: {
  "opportunities": [
    {
      "asset": "VFIAX",
      "unrealizedLoss": -2500,
      "replacement": "VTI",
      "harvestAmount": 2500
    }
  ],
  "totalHarvestable": 12500
}
```

## SPARQL Queries

15 pre-built templates for:
- Portfolio summary
- Goal progress tracking
- Risk profile assessment
- Allocation analysis
- Rebalancing opportunities
- Tax-loss harvesting
- Performance metrics

## Allocation Models

### Modern Portfolio Theory
```
max: E[R] - (λ/2)σ²
subject to: Σw_i = 1, w_i ≥ 0
```

### Black-Litterman
```
E[R] = [(τΣ)⁻¹ + P'Ω⁻¹P]⁻¹[(τΣ)⁻¹Π + P'Ω⁻¹Q]
```

### Risk Parity
```
RC_i = w_i × (∂σ/∂w_i) = constant
```

## Implementation Examples

### Rust
```rust
use robo_advisor::*;

let client = Client::new("John Doe", 35, RiskScore::Moderate);
let portfolio = Portfolio::create_for_client(&client)?;
portfolio.rebalance(RebalanceMethod::TaxAware)?;
```

### TypeScript
```typescript
import { Client, Portfolio } from '@ggen/robo-advisor';

const client = new Client('Jane Smith', 45);
await client.assessRisk();
const portfolio = await Portfolio.allocate(client);
```

### Python
```python
from robo_advisor import Client, Portfolio

client = Client('Bob Johnson', age=50)
portfolio = Portfolio.from_risk_profile(client.risk_profile)
tlh_opportunities = portfolio.find_tlh_opportunities()
```

## Tax-Loss Harvesting Strategy

1. **Identify Losses**: Daily scan for positions with unrealized losses >$1,000
2. **Wash Sale Avoidance**: 30-day rule compliance
3. **Replacement Selection**: Similar but not substantially identical
4. **Harvest Execution**: Sell at loss, immediately buy replacement
5. **Benefit Tracking**: Report tax savings to client

## Performance Metrics

- **Total Return**: Time-weighted return
- **Sharpe Ratio**: Risk-adjusted return
- **Sortino Ratio**: Downside risk-adjusted
- **Maximum Drawdown**: Peak-to-trough decline
- **Alpha & Beta**: Benchmark-relative performance

## Testing

Comprehensive test suite:
- Unit tests (allocation, rebalancing)
- Integration tests (full client flow)
- Tax calculation tests
- Performance tests

## Best Practices

1. **Annual Review**: Review client goals and risk tolerance
2. **Quarterly Rebalancing**: Unless threshold exceeded
3. **Daily TLH Scanning**: Automated loss harvesting
4. **Fee Disclosure**: Clear, upfront fee structure
5. **Performance Reporting**: Transparent, regular reports

## License

MIT License
