# Hidden Regime MCP Server - Implementation Roadmap

**Status:** Phase 1-4 Complete (4 of 5 MCP interfaces implemented)
**Version:** 0.1.0
**Last Updated:** 2025-11-18

---

## Executive Summary

The Hidden Regime MCP (Model Context Protocol) server brings HMM-based market regime detection to AI assistants like Claude Desktop. This roadmap outlines the implementation strategy, current status, and future enhancements.

**Current Implementation:**
- ✅ **Tools Interface** (3 tools) - Operational regime detection
- ✅ **Resources Interface** (2 resources) - URI-based data access
- ✅ **Prompts Interface** (7 prompts) - Expert-level analysis workflows
- ✅ **Logging Interface** (implemented) - Structured logging and transparency
- ⏳ **Sampling Interface** (optional) - Server-initiated LLM requests

**Key Achievement:** Users can now get sophisticated regime analysis with a single command, democratizing access to expert-level HMM analysis.

---

## MCP Interface Overview

The Model Context Protocol defines 5 standard interfaces for AI-server integration. Each interface serves a distinct purpose:

| Interface | Purpose | Status | Count | Priority |
|-----------|---------|--------|-------|----------|
| **Tools** | Execute operations and return data | ✅ Complete | 3 | Critical |
| **Resources** | URI-based data access | ✅ Complete | 2 | High |
| **Prompts** | Pre-configured analysis workflows | ✅ Complete | 7 | High |
| **Logging** | Structured logging and transparency | ✅ Complete | - | Medium |
| **Sampling** | Server-initiated LLM requests | ⏳ Optional | - | Low |

---

## Phase 1: Foundation (COMPLETED ✅)

**Timeline:** Completed November 2025
**Goal:** Establish core MCP capabilities for regime detection

### 1.1 Tools Interface ✅

**Implemented:** 3 core tools

#### detect_regime
- **Purpose:** Current regime detection with temporal context
- **Arguments:** ticker, n_states (2-5), start_date, end_date
- **Returns:** Comprehensive regime analysis with:
  - Basic info: regime, confidence, mean return, volatility
  - Temporal context: days in regime, expected duration, status
  - Price context: YTD/30d/7d performance
  - Stability: transitions, previous regime
  - Interpretation: human-readable explanation

#### get_regime_statistics
- **Purpose:** Historical regime analysis and statistics
- **Arguments:** ticker, n_states, start_date, end_date
- **Returns:** Per-regime statistics:
  - Mean return, volatility, duration
  - Win rate, observation count
  - Analysis period summary

#### get_transition_probabilities
- **Purpose:** Regime transition matrix and forecasting
- **Arguments:** ticker, n_states
- **Returns:** Transition analysis:
  - Transition probability matrix
  - Expected durations per regime
  - Steady state probabilities

**Success Metrics:**
- ✅ All 3 tools implemented and tested
- ✅ Comprehensive error handling and validation
- ✅ Response times < 5 seconds (with caching)
- ✅ User-friendly error messages

### 1.2 Resources Interface ✅

**Implemented:** 2 URI-based resources

#### regime://{ticker}/current
- **Purpose:** Quick access to current regime via URI
- **Format:** JSON response with detect_regime output
- **Use Case:** Programmatic access, bookmarks, sharing

#### regime://{ticker}/transitions
- **Purpose:** Quick access to transition probabilities
- **Format:** JSON response with transition matrix
- **Use Case:** Regime forecasting, risk assessment

**Success Metrics:**
- ✅ Both resources accessible via URI
- ✅ Graceful error handling
- ✅ JSON formatting for easy parsing

### 1.3 Prompts Interface ✅

**Implemented:** 7 curated analysis workflows

#### Quick Analysis
1. **regime-quick-check** - Fast market health check (1 tool call)
   - 3-4 sentence summary
   - Regime, confidence, maturity, implications

#### Comprehensive Analysis
2. **regime-deep-dive** - Multi-tool comprehensive report (3 tool calls)
   - Current state, historical context, transitions
   - Statistical summary, trading implications

#### Specialized Workflows
3. **regime-strategy-advisor** - Trading strategies by regime + risk
4. **regime-multi-asset-comparison** - Cross-asset regime comparison
5. **regime-risk-assessment** - Quantitative risk scoring (0-100)
6. **regime-historical-analogs** - Pattern matching and predictions
7. **regime-portfolio-review** - Portfolio-wide regime analysis

**Success Metrics:**
- ✅ All 7 prompts implemented and documented
- ✅ Comprehensive test coverage (50+ tests)
- ✅ 350+ lines of user documentation
- ✅ 40-page technical specification

**Key Benefits:**
- One command replaces complex multi-step queries
- Standardized expert-level analysis
- Educational value for users learning regime detection
- Improved discoverability of capabilities

---

## Phase 2: Enhanced Prompts (RECOMMENDED NEXT STEP)

**Timeline:** 1-2 weeks
**Priority:** HIGH ⭐⭐⭐
**Effort:** Medium

### 2.1 User Feedback Integration

**Objective:** Refine existing prompts based on real-world usage

**Tasks:**
1. **User Testing**
   - Deploy prompts to Claude Desktop
   - Gather feedback from 5-10 users
   - Identify pain points and gaps

2. **Prompt Refinement**
   - Adjust prompt text based on feedback
   - Optimize output format and structure
   - Improve clarity of instructions

3. **Documentation Updates**
   - Add more usage examples
   - Create video tutorials or screenshots
   - FAQ section for common questions

**Success Metrics:**
- 80%+ user satisfaction with prompt quality
- <5% error rate in prompt execution
- Positive feedback on output format

### 2.2 Additional Prompts (Phase 2A)

**Objective:** Add 3-5 advanced prompts based on user demand

**Potential Prompts:**

#### regime-sector-rotation
- **Purpose:** Sector-specific regime analysis
- **Arguments:** sector (e.g., 'XLF', 'XLE', 'XLK')
- **Workflow:** Compare sector ETF regime to SPY/market
- **Output:** Sector rotation opportunities, relative strength

#### regime-options-strategy
- **Purpose:** Options strategies based on regime + volatility
- **Arguments:** ticker, option_type ('calls', 'puts', 'spreads')
- **Workflow:** Combine regime with implied volatility
- **Output:** Optimal options strategies (straddles, spreads, etc.)

#### regime-backtest
- **Purpose:** Historical performance of regime-based strategies
- **Arguments:** ticker, strategy_type, lookback_period
- **Workflow:** Simulate trading based on regime signals
- **Output:** Backtest results, Sharpe ratio, drawdown

#### regime-watchlist
- **Purpose:** Monitor multiple tickers for regime changes
- **Arguments:** watchlist (list of tickers), alert_conditions
- **Workflow:** Check regime status across portfolio
- **Output:** Actionable alerts, tickers near transition

#### regime-correlation-analysis
- **Purpose:** Analyze regime synchronization across assets
- **Arguments:** tickers (list)
- **Workflow:** Compare regime timing and transitions
- **Output:** Correlation matrix, leading/lagging indicators

**Success Metrics:**
- 3+ new prompts added
- Positive user adoption (>20% try new prompts)
- Fills identified gaps in workflow coverage

### 2.3 Prompt Composition

**Objective:** Enable combining multiple prompts into workflows

**Implementation:**
```python
async def regime_full_report(ticker: str) -> str:
    """Comprehensive analysis combining multiple prompts"""
    # Combines: quick-check + deep-dive + risk-assessment
```

**Use Cases:**
- Weekly portfolio review (portfolio-review + multi-asset-comparison)
- Pre-trade analysis (quick-check + strategy-advisor + risk-assessment)
- Research report (deep-dive + historical-analogs + backtest)

**Success Metrics:**
- 2-3 composite prompts implemented
- User feedback on workflow efficiency
- Reduced time for complex analysis

---

## Phase 3: Sampling Interface (OPTIONAL)

**Timeline:** 2-3 weeks
**Priority:** MEDIUM ⭐⭐
**Effort:** High

### 3.1 Autonomous Analysis Workflows

**Objective:** Enable server-driven multi-step analysis

**Concept:** MCP server can request LLM completions from Claude to orchestrate complex workflows autonomously.

**Use Cases:**

#### regime-narrative-builder
- Server calls detect_regime
- Server uses Sampling to generate natural language narrative
- Claude contextualizes within broader market conditions
- Result: Rich prose summary of regime with context

#### regime-alert-system
- Server monitors regimes periodically (cron job)
- Server detects significant changes
- Server uses Sampling to generate alert messages
- Claude provides actionable recommendations

#### regime-strategy-synthesizer
- Server combines multiple tool calls
- Server uses Sampling to synthesize coherent strategy
- Claude produces trading plan document
- Result: Publication-ready strategy report

### 3.2 Implementation Considerations

**Benefits:**
- Server can drive sophisticated multi-step workflows
- Natural language generation for reports
- Autonomous monitoring and alerting

**Challenges:**
- Requires Claude API access from server
- Additional complexity in error handling
- API token costs for each sampling request
- May not add value if user already chatting with Claude

**Technical Requirements:**
- Claude API integration
- Cost controls and rate limiting
- Workflow state management
- Testing autonomous behaviors

**Decision Point:** Implement only if:
1. Users request autonomous monitoring
2. Server-driven reports are valuable
3. API costs are acceptable

**Success Metrics:**
- 1-2 autonomous workflows implemented
- Positive user feedback on autonomous features
- API costs remain reasonable (<$10/month per user)

---

## Phase 4: Logging Interface (COMPLETED ✅)

**Timeline:** Completed November 2025
**Priority:** MEDIUM
**Effort:** Low

### 4.1 Structured Logging - IMPLEMENTED ✅

**Objective:** Provide transparency into MCP server operations

**Implemented Features:**

#### Cache Operations ✅
- Cache hit logging with tool name
- Cache miss logging before analysis
- Result cached confirmation

#### Data Loading ✅
- Data fetching notifications ("Fetching data for {ticker} from Yahoo Finance...")
- Observation count logging ("Downloaded {n} observations for {ticker}")
- Date range logging (start to end dates)

#### Model Training ✅
- Training start notifications ("Training {n}-state HMM model for {ticker}...")
- Training completion confirmations
- Model configuration (number of states)

#### Analysis Operations ✅
- Operation-specific logging for each tool (detect_regime, get_regime_statistics, get_transition_probabilities)
- Completion confirmations with caching status

### 4.2 Implementation Details

**Logging Points Implemented:**
1. ✅ Data loading phase - fetch notifications and completion with counts
2. ✅ Model training phase - start and completion logging
3. ✅ Cache operations - hit/miss/set operations
4. ✅ Tool operations - per-tool operation logging
5. ✅ Result caching - confirmation when results are cached

**Log Levels Used:**
- INFO: All normal operations (cache, data loading, training, completion)
- WARNING: Error recovery (already in handle_pipeline_error)
- ERROR: Failures (already in error handlers)

**Implementation:**
- Added logging throughout `hidden_regime_mcp/tools.py`
- Uses Python's standard `logging` module
- FastMCP automatically captures and sends to MCP client
- Logs appear in Claude Desktop during execution

**Testing:**
- Comprehensive test suite in `tests/test_mcp/test_logging.py`
- Tests for cache operations, data loading, model training, result caching
- Tests for log message content and sequencing
- 20+ test cases covering all logging scenarios

**Documentation:**
- Added "Logging & Transparency" section to README_MCP.md
- Example log output
- Explanation of why logging is useful
- Log viewing instructions for Claude Desktop

**Success Metrics:**
- ✅ Logging implemented across all 3 tools
- ✅ Test coverage >95% for logging code paths
- ✅ Documentation complete with examples
- ⏳ User feedback on transparency (gathering)

---

## Phase 5: Performance Optimization

**Timeline:** 1-2 weeks
**Priority:** MEDIUM (after Phase 2)
**Effort:** Medium

### 5.1 Caching Enhancements

**Current State:**
- In-memory cache with 15-minute TTL
- Keyed by ticker + n_states + date range + tool name

**Enhancements:**

#### Persistent Cache
- Implement disk-based cache (SQLite or Redis)
- Survive server restarts
- Shared across sessions

#### Intelligent Cache Invalidation
- Invalidate only during market hours
- Keep after-hours data fresh for next day
- Configurable TTL per tool

#### Cache Warming
- Pre-fetch popular tickers (SPY, QQQ, etc.)
- Background refresh during low-load periods
- Reduce cold-start latency

**Success Metrics:**
- 80%+ cache hit rate
- <1s response time for cached queries
- Reduced API calls to Yahoo Finance

### 5.2 Batch Operations

**Objective:** Enable efficient multi-ticker analysis

**Implementation:**
```python
async def detect_regime_batch(tickers: List[str]) -> List[Dict]:
    """Parallel regime detection for multiple tickers"""
    # Uses asyncio.gather for concurrent requests
```

**Use Cases:**
- Portfolio analysis (10+ tickers)
- Sector comparison (all XL* ETFs)
- Watchlist monitoring (50+ tickers)

**Success Metrics:**
- 3-5x speedup for batch operations
- Linear scaling with ticker count
- No degradation for single ticker queries

### 5.3 Model Persistence

**Objective:** Cache trained HMM models

**Current State:**
- Models retrained on every request
- 1-2s overhead per request

**Enhancement:**
- Cache trained models in memory/disk
- Invalidate when new data available
- Incremental model updates

**Success Metrics:**
- 50%+ reduction in response time
- Same accuracy as fresh models
- Efficient memory usage

---

## Phase 6: Advanced Features

**Timeline:** 3-4 weeks
**Priority:** LOW (future enhancement)
**Effort:** High

### 6.1 Real-Time Regime Updates

**Objective:** Enable intraday regime monitoring

**Challenges:**
- Current implementation uses daily data only
- Intraday data requires different source
- Higher frequency = more noise

**Implementation:**
- Integrate with real-time data provider (Alpha Vantage, IEX Cloud)
- Implement intraday HMM (1-min, 5-min, hourly)
- WebSocket support for streaming updates

**Success Metrics:**
- Intraday regime detection working
- <10s latency for updates
- Acceptable accuracy vs daily models

### 6.2 Custom Regime Definitions

**Objective:** Allow users to define custom regimes

**Current State:**
- Fixed regime labels (bull, bear, sideways)
- Labels assigned by model characteristics

**Enhancement:**
- User-defined regime criteria
- Custom threshold configuration
- Regime naming conventions

**Example:**
```python
custom_regimes = {
    "high_volatility_bull": {"min_return": 0.01, "min_volatility": 0.03},
    "low_volatility_bear": {"max_return": -0.005, "max_volatility": 0.015}
}
```

**Success Metrics:**
- User adoption of custom regimes
- Positive feedback on flexibility
- Maintained model accuracy

### 6.3 Machine Learning Enhancements

**Objective:** Improve regime detection accuracy

**Enhancements:**

#### Fat-Tailed Distributions
- Replace Gaussian emissions with Student-t
- Better capture of market tail risk
- Improved crisis detection

#### Duration Modeling
- Add explicit duration models
- Better regime persistence estimation
- Improved transition timing

#### Multi-Asset HMMs
- Joint regime detection across assets
- Capture cross-asset dynamics
- Regime contagion analysis

**Success Metrics:**
- Measurable improvement in accuracy
- Better crisis detection (2008, 2020)
- Positive user feedback on insights

---

## Phase 7: Enterprise Features

**Timeline:** 4-6 weeks
**Priority:** LOW (enterprise-only)
**Effort:** Very High

### 7.1 Multi-User Support

**Features:**
- User authentication and authorization
- Per-user caching and preferences
- Usage tracking and quotas
- Admin dashboard

### 7.2 API Rate Limiting

**Features:**
- Configurable rate limits per user/tier
- Graceful degradation under load
- Queue management for high-demand periods

### 7.3 Custom Deployment

**Features:**
- Docker containerization
- Kubernetes deployment manifests
- Cloud hosting guides (AWS, GCP, Azure)
- Enterprise support tier

---

## Success Metrics & KPIs

### User Adoption
- **Target:** 100+ active users within 3 months
- **Metric:** Monthly active users in Claude Desktop
- **Current:** N/A (just launched)

### User Satisfaction
- **Target:** 4.5+ stars average rating
- **Metric:** User feedback and surveys
- **Current:** N/A (gathering feedback)

### Performance
- **Target:** <3s average response time
- **Metric:** P50, P95, P99 latencies
- **Current:** ~2-5s (varies by query)

### Reliability
- **Target:** 99.5%+ uptime
- **Metric:** Error rate and availability
- **Current:** 99%+ (early monitoring)

### Feature Usage
- **Target:** 50%+ users try prompts
- **Metric:** Prompt vs tool usage ratio
- **Current:** N/A (just launched)

---

## Decision Framework

When evaluating next steps, consider:

### Must Have (Do Immediately)
- ✅ Core Tools (detect_regime, statistics, transitions)
- ✅ Basic Resources (current, transitions)
- ✅ Essential Prompts (quick-check, deep-dive)
- 🎯 User feedback integration
- 🎯 Bug fixes and stability

### Should Have (Do Soon)
- ✅ Comprehensive prompts (7 total)
- 🎯 Additional prompts based on demand
- 🎯 Performance optimization (caching)
- 🎯 Documentation improvements

### Could Have (Nice to Have)
- Sampling interface (if autonomous workflows needed)
- Logging interface (if transparency requested)
- Batch operations
- Model persistence

### Won't Have (Not Now)
- Real-time intraday regimes
- Custom regime definitions
- Enterprise features
- Advanced ML enhancements

---

## Recommended Next Steps (Priority Order)

### Immediate (Next 2 Weeks)

1. **User Testing & Feedback** ⭐⭐⭐
   - Deploy to 5-10 test users
   - Gather structured feedback
   - Identify critical gaps

2. **Bug Fixes & Stability** ⭐⭐⭐
   - Fix any issues discovered in testing
   - Improve error messages
   - Enhance edge case handling

3. **Documentation Enhancement** ⭐⭐
   - Add usage examples to README
   - Create quickstart video/GIF
   - FAQ section

### Short Term (2-4 Weeks)

4. **Prompt Refinement** ⭐⭐⭐
   - Improve prompt text based on feedback
   - Optimize output formatting
   - Add 2-3 high-demand prompts

5. **Performance Optimization** ⭐⭐
   - Implement persistent caching
   - Add batch operations
   - Optimize model training

### Medium Term (1-2 Months)

6. **Advanced Prompts** ⭐⭐
   - regime-sector-rotation
   - regime-options-strategy
   - regime-backtest

7. **Prompt Composition** ⭐
   - Composite workflows
   - Pre-built analysis pipelines
   - User customization

### Long Term (3+ Months)

8. **Sampling Interface** ⭐ (if justified)
   - Evaluate user demand
   - Implement 1-2 autonomous workflows
   - Monitor API costs

9. **Logging Interface** ⭐ (if requested)
   - Add structured logging
   - Progress indicators
   - Debugging support

---

## Risk Assessment

### Technical Risks

| Risk | Impact | Probability | Mitigation |
|------|--------|-------------|------------|
| API rate limits (yfinance) | High | Medium | Caching, rate limiting, fallback data sources |
| Poor prompt adoption | Medium | Low | User testing, refinement, documentation |
| Performance degradation | Medium | Medium | Monitoring, caching, optimization |
| Breaking MCP changes | High | Low | Pin MCP version, test updates before deploy |

### User Experience Risks

| Risk | Impact | Probability | Mitigation |
|------|--------|-------------|------------|
| Confusing prompt outputs | High | Medium | User testing, clear formatting, examples |
| Slow response times | High | Medium | Caching, optimization, progress indicators |
| Inaccurate regime detection | High | Low | Validation, documentation of limitations |
| Poor discoverability | Medium | Medium | Documentation, tutorials, examples |

---

## Conclusion

The Hidden Regime MCP server has successfully implemented the core MCP interfaces (Tools, Resources, Prompts), providing users with powerful regime detection capabilities through a simple, conversational interface.

**Current State:**
- ✅ 3 of 5 MCP interfaces complete
- ✅ 7 expert-level analysis prompts
- ✅ Comprehensive documentation and tests
- ✅ Production-ready foundation

**Recommended Path Forward:**
1. **Gather user feedback** on existing prompts
2. **Refine and optimize** based on real-world usage
3. **Add high-demand prompts** (2-3 additional workflows)
4. **Evaluate optional interfaces** (Sampling, Logging) based on demand

**Success Criteria:**
- User adoption and satisfaction
- Performance and reliability
- Feature coverage and completeness

The MCP implementation is well-positioned for growth and enhancement based on user needs and feedback.

---

**Document Version:** 1.0
**Last Updated:** 2025-11-18
**Next Review:** 2025-12-18
