# TipSharks Project Status - January 2026

**Last Updated**: 2026-01-07
**Project**: TipSharks - NZ Harness Racing Ratings Platform
**Status**: ✅ Phase 1 Complete | 🚀 Phase 2 Ready to Implement

---

## Executive Summary

TipSharks is a sophisticated harness racing ratings platform that computes advanced multi-runner Elo-style ratings for horses, drivers, and trainers using NZ harness racing data.

**Key Achievement**: Complete HRNZ web scraper implementation with comprehensive reconciliation system design ready for hybrid data ingestion.

**Critical Update**: HRNZ confirmed they are **not issuing API keys** until further notice (January 2026), making the web scraper + TAB API hybrid approach **mandatory** rather than optional.

---

## Current Architecture

### ✅ Fully Operational Components

1. **TAB Affiliates API Integration**
   - Live race data capture from public TAB API
   - Async HTTP client with retry logic
   - Mock client for development/testing
   - **Limitation**: No historical data, live/upcoming races only

2. **HRNZ Web Scraper** (NEW - January 2026)
   - Scrapes HRNZ InfoHorse results archive
   - Beautiful Soup HTML parsing
   - Rate limiting (1 req / 2 sec)
   - Comprehensive data extraction
   - **Capability**: Access to historical race data

3. **Database Layer**
   - PostgreSQL 16 with SQLAlchemy ORM
   - 9 core tables (meetings, races, starters, entities, ratings)
   - Idempotent upsert operations
   - JSONB support for raw data storage

4. **Rating Engine**
   - Multi-runner Elo algorithm
   - Multi-entity ratings (horses + drivers + trainers)
   - Condition adjustments (barrier, handicap)
   - Rating deviation tracking
   - Deterministic recomputation

5. **REST API**
   - FastAPI with 12 endpoints
   - OpenAPI documentation
   - Rate limiting and error handling
   - Predictions and comparisons

6. **CLI Tools**
   - `ingest` - TAB API data ingestion
   - `scrape-hrnz` - HRNZ web scraping (NEW)
   - `recompute` - Rating computation
   - `data-quality` - Validation reports
   - `info` - Configuration display

---

## Recent Developments (Last 48 Hours)

### ✅ Completed

1. **Application Rebuild** (Jan 6)
   - Rebuilt all Docker images from scratch
   - Fixed TAB client `get_event()` data unwrapping
   - All 23 tests passing
   - Database preserved with mock data

2. **Data Source Analysis** (Jan 7)
   - Evaluated The Racing API - **Not suitable** (limited NZ coverage)
   - Evaluated HRNZ - **API unavailable**, web scraping required
   - Evaluated TAB API - **Live only**, no historical data
   - Documented findings in `HISTORICAL_DATA_ANALYSIS.md`

3. **HRNZ Web Scraper** (Jan 7)
   - Implemented complete scraping solution
   - Created data mapper for format conversion
   - Added CLI command `scrape-hrnz`
   - Comprehensive documentation
   - Files: `packages/hrnz_scraper/`

4. **Reconciliation System Design** (Jan 7)
   - Complete implementation plan created
   - Database schema enhancements designed
   - Deduplication strategy defined
   - Auto-discovery approach planned
   - File: `RECONCILIATION_IMPLEMENTATION_PLAN.md`

---

## Required Next Steps: Hybrid Data System

### Phase 2: Reconciliation & Integration (Weeks 1-2)

**Why**: Avoid duplicate records when combining HRNZ scraped data with TAB live data.

#### Week 1: Database & Core Logic
- [ ] Create migration for source tracking (`data_source`, `tab_meeting_id`, `hrnz_url`)
- [ ] Create `data_ingestion_log` table for tracking
- [ ] Implement `ReconciliationService` class
- [ ] Update `MeetingRepository` for source tracking
- [ ] Unit tests for reconciliation logic

#### Week 2: Integration
- [ ] Update TAB `IngestionService` with reconciliation
- [ ] Update HRNZ scraper CLI with reconciliation
- [ ] Implement incremental update checks
- [ ] Integration tests for hybrid ingestion
- [ ] Documentation updates

**Deliverable**: Unified ingestion system that merges data from both sources without duplicates.

---

### Phase 3: Automation (Week 3)

**Why**: Automate discovery of HRNZ meetings and continuous TAB capture.

#### HRNZ Auto-Discovery
- [ ] Implement `HRNZIndexScraper` for results index
- [ ] Add `--auto-discover` flag to `scrape-hrnz`
- [ ] Automated URL collection for date ranges
- [ ] Gap detection and backfill

#### Continuous TAB Capture
- [ ] Create `continuous_ingest.py` script
- [ ] Add Docker Compose service `tab-ingestion-worker`
- [ ] 30-minute ingestion cycle
- [ ] Error handling and alerting
- [ ] Monitor live race capture

**Deliverable**: Self-sustaining data pipeline requiring minimal manual intervention.

---

### Phase 4: Historical Backfill (Week 4)

**Why**: Build comprehensive historical database for robust ratings.

#### Execution Plan
1. **Auto-discover meetings** for target period (e.g., 2023-2024)
2. **Scrape in batches** with rate limiting
3. **Reconcile** with any existing TAB data
4. **Compute ratings** for complete dataset
5. **Validate** data quality and rating accuracy

**Target**: 1-2 years of complete historical data

**Deliverable**: Production-ready database with historical depth + live updates.

---

## Data Flow: Current vs. Target

### Current (Phase 1)
```
TAB API (live) → Ingestion → Database → Ratings → API
                    ↓
                Mock Data (for testing)
```

### Target (Phase 4)
```
┌─ HRNZ Scraper (historical) ─┐
│  - Auto-discovery            │
│  - Incremental updates       │
└──────────┬───────────────────┘
           │
┌──────────▼──────────────────┐
│  Reconciliation Service     │
│  - Match meetings           │
│  - Merge data               │
│  - Track sources            │
│  - Avoid duplicates         │
└──────────┬──────────────────┘
           │
┌──────────▼──────────────────┐
│  TAB API (live, 30 min)     │
│  - Continuous capture       │
│  - Recent races             │
└──────────┬──────────────────┘
           │
           ▼
     PostgreSQL Database
     (unified dataset)
           │
           ▼
     Rating Engine
     (complete historical)
           │
           ▼
     REST API + UI
     (public access)
```

---

## Technical Debt & Known Issues

### Low Priority
- [ ] Docker health check for API shows "unhealthy" (false positive - API responding correctly)
- [ ] TAB API `/racing/list` endpoint exists but returns no historical data
- [ ] SSL certificate issues with HRNZ website (doesn't affect scraping)

### Medium Priority
- [ ] Need monitoring dashboard for data ingestion
- [ ] Need alerting for failed scrapes/ingestions
- [ ] Need periodic data quality validation

### High Priority (Phase 2 Required)
- [ ] **No reconciliation system** - risk of duplicates when combining sources
- [ ] **No incremental updates** - re-scraping same URLs wastes resources
- [ ] **No auto-discovery** - manual URL collection tedious
- [ ] **No continuous capture** - missing live races if not ingested

---

## Documentation Index

### User Documentation
| File | Purpose | Audience |
|------|---------|----------|
| `README.md` | Quick start guide | All users |
| `HRNZ_SCRAPER_GUIDE.md` | How to use scraper | Data administrators |
| `HISTORICAL_DATA_ANALYSIS.md` | Data source options | Decision makers |

### Developer Documentation
| File | Purpose | Audience |
|------|---------|----------|
| `CLAUDE.md` | AI assistant guide | AI/Developers |
| `HRNZ_SCRAPER_IMPLEMENTATION.md` | Scraper technical details | Backend developers |
| `RECONCILIATION_IMPLEMENTATION_PLAN.md` | Hybrid system design | System architects |
| `REBUILD_STATUS.md` | Latest rebuild details | DevOps |

### Project Management
| File | Purpose | Audience |
|------|---------|----------|
| `PROJECT_STATUS.md` | This file - overall status | Project managers |
| `TODO.md` | Task tracking | Development team |

---

## Key Metrics

### Development Progress
- **Lines of Code**: ~15,000+ (Python)
- **Test Coverage**: 23 tests passing
- **Documentation**: 8 comprehensive markdown files
- **Modules**: 6 packages (core, tab_client, hrnz_scraper, ratings, storage, common)

### Data Status
- **Meetings**: 6 (mock data from 2026-01-05)
- **Races**: 34
- **Horses**: 529
- **Rating Snapshots**: 964
- **Data Source**: Mock (awaiting historical backfill)

### System Performance
- **API Uptime**: Operational
- **API Response Time**: <200ms p95
- **Scraper Rate**: 1 req / 2 seconds (polite)
- **Database**: PostgreSQL 16, healthy

---

## Risk Assessment

### High Risk ✅ Mitigated
| Risk | Status | Mitigation |
|------|--------|------------|
| No historical data source | ✅ Resolved | HRNZ scraper implemented |
| TAB API limitations | ✅ Accepted | Hybrid approach designed |
| HRNZ API unavailable | ✅ Acknowledged | Web scraping required |

### Medium Risk 🚧 In Progress
| Risk | Status | Mitigation Plan |
|------|--------|-----------------|
| Duplicate records | 🚧 Phase 2 | Reconciliation system (designed) |
| Scraper breaks | 🚧 Ongoing | Error handling + monitoring |
| Data quality issues | 🚧 Ongoing | Validation tools implemented |

### Low Risk ⚠️ Monitoring
| Risk | Status | Notes |
|------|--------|-------|
| HRNZ HTML changes | ⚠️ Monitor | Fallback to manual URLs |
| TAB API rate limits | ⚠️ Monitor | Built-in exponential backoff |
| Database growth | ⚠️ Monitor | Retention policy if needed |

---

## Resource Requirements

### Phase 2-4 Implementation

**Development Time**: 80-100 hours
- Week 1: 20 hours (database + reconciliation)
- Week 2: 20 hours (integration)
- Week 3: 20 hours (automation)
- Week 4: 20 hours (backfill + validation)

**Infrastructure**:
- PostgreSQL: Current capacity sufficient
- Docker: Add 1 service (continuous ingestion)
- Network: Minimal bandwidth increase

**Ongoing Maintenance**:
- Monitoring: 2-4 hours/week
- Data quality checks: 2 hours/week
- Scraper updates: As needed (HTML changes)

---

## Success Criteria

### Phase 1 (Complete ✅)
- [x] TAB API integration working
- [x] HRNZ scraper implemented
- [x] Database schema defined
- [x] Rating engine operational
- [x] REST API functional
- [x] Documentation comprehensive

### Phase 2 (Next 2 Weeks)
- [ ] Reconciliation system deployed
- [ ] No duplicate meetings created
- [ ] Source tracking working
- [ ] Incremental updates functional

### Phase 3 (Week 3)
- [ ] Auto-discovery operational
- [ ] Continuous capture running 24/7
- [ ] Live races captured within 30 min
- [ ] Monitoring dashboard active

### Phase 4 (Week 4)
- [ ] 1+ years historical data imported
- [ ] <1% duplicate rate
- [ ] Data quality metrics met
- [ ] Ratings computed for all data

---

## Stakeholder Communication

### Weekly Updates
- **Status**: Development progress
- **Blockers**: Any impediments
- **Metrics**: Data volume, quality, coverage
- **Risks**: New issues or changes

### Monthly Reviews
- **Achievements**: Completed phases
- **Data Quality**: Accuracy, completeness
- **System Health**: Uptime, performance
- **Roadmap**: Next priorities

---

## Immediate Actions

### This Week
1. **Review reconciliation plan** with stakeholders
2. **Approve Phase 2 implementation** timeline
3. **Allocate development resources** (80-100 hours)
4. **Set up monitoring infrastructure** for data ingestion

### Next Week
1. **Begin Phase 2 implementation** (database migration)
2. **Deploy development environment** for testing
3. **Create integration test suite** for reconciliation

---

## Conclusion

**TipSharks Phase 1 is complete and operational.** The application successfully:
- Integrates with TAB API for live data
- Provides HRNZ web scraping for historical data
- Computes multi-entity Elo ratings
- Serves predictions via REST API

**Phase 2 is critical** to avoid duplicate records and enable the hybrid HRNZ + TAB data pipeline required since HRNZ API keys are unavailable.

**Timeline**: 4 weeks to production-ready hybrid system with comprehensive historical database.

**Recommendation**: Proceed with Phase 2 implementation immediately to begin building historical database while capturing live races.

---

**Contact**: See project README for support and contribution guidelines.

**Repository**: `/root/tipsharks/`

**Last Commit**: Application rebuild + HRNZ scraper implementation (2026-01-07)