market-data-source 0.3.0

High-performance synthetic market data generator with financial precision. Generate unlimited OHLC candles, tick data, and realistic trading scenarios for backtesting and research.
Documentation
# PRP-33: CAPM (Capital Asset Pricing Model) Implementation

## Context & Motivation

**Integration Goal**: Implement CAPM for systematic risk-based return generation.

**User Requirement**: Generate asset returns based on market beta and risk-free rate.

**Technical Challenge**: Maintain consistent beta relationships across multiple assets.

## Requirements

### CAPM Components
1. **Risk-Free Rate**: Configurable base rate
2. **Market Return**: Systematic market return generation
3. **Beta Calculation**: Asset-specific systematic risk
4. **Alpha Generation**: Excess returns beyond CAPM prediction

### Multi-Asset Support
1. **Beta Matrix**: Support multiple assets with different betas
2. **Correlation Preservation**: Maintain market correlations
3. **Dynamic Beta**: Support time-varying betas
4. **Portfolio Analytics**: CAPM-based portfolio metrics

## Implementation Blueprint

### Phase 1: CAPM Core
1. Create `src/factors/capm.rs`
2. Define `CAPMModel` struct
3. Implement expected return calculation
4. Add beta configuration options

### Phase 2: Market Generation
1. Generate market return series
2. Apply asset betas
3. Add idiosyncratic risk component
4. Implement alpha generation

### Phase 3: Analytics
1. Add beta estimation tools
2. Create Sharpe ratio calculator
3. Implement security market line
4. Add performance attribution

## Success Criteria

### Validation Gates
```bash
# Test CAPM implementation
cargo test capm_model
cargo test portfolio_beta

# Validate returns
cargo test capm_expected_returns
```

### Implementation Metrics
- [ ] Beta preservation accuracy > 95%
- [ ] Market correlation maintained
- [ ] Sharpe ratios realistic
- [ ] Alpha generation configurable

## Dependencies & References

**Prerequisites**:
- Can build on PRP-32 factor infrastructure
- Requires market return generator

**Research Sources**:
- Modern Portfolio Theory references
- CAPM empirical studies
- Beta estimation methodologies

**Rust Libraries**:
- Leverage digifi's CAPM functions
- Use existing statistics modules
- Build on factor model framework

## Implementation Tasks

### Phase 1: Core CAPM (2-3 hours)
1. Implement CAPM model
2. Add return calculation
3. Create beta management
4. Write tests

### Phase 2: Generation (2-3 hours)
1. Generate market returns
2. Apply beta transformation
3. Add idiosyncratic risk
4. Test correlations

### Phase 3: Analytics (1-2 hours)
1. Add metrics calculation
2. Create analysis tools
3. Document usage
4. Add examples

## Risk Mitigation
- Validate beta ranges (typically 0.5-2.0)
- Ensure positive risk premiums
- Handle edge cases (zero beta)
- Provide sensible defaults

## Success Score
**8/10** - Well-established model with clear implementation path and existing library support.