- Add "Indicator Coverage" tab alongside existing "Gap Detection" tab
- Display coverage summary with total/complete/incomplete combinations
- Show detailed coverage table with progress bars and status badges
- Add backfill buttons for individual symbol/interval pairs
- Add bulk backfill option for all incomplete indicators
- Include filter and search functionality for both tabs
- Show missing indicator counts and details per combination
- Real-time refresh capabilities for both gaps and indicators
- Maintain all existing gap detection functionality
- Provide visual progress bars showing coverage percentages
- Support batch operations with confirmation dialogs
This integrates indicator coverage monitoring into the existing gaps
interface, providing a unified data quality dashboard for monitoring
both OHLCV gaps and technical indicator completeness.
- Add GET /api/indicators/coverage/{symbol}/{interval} to check coverage
- Add GET /api/indicators/coverage/all for system-wide coverage status
- Add POST /api/indicators/backfill/{symbol}/{interval} to backfill missing indicators
- Add POST /api/indicators/backfill-all for bulk backfill operations
- Add GET /api/indicators/missing/{symbol}/{interval} to list incomplete records
- Add GET /api/indicators/summary for aggregate coverage statistics
- Support configurable batch_size and min_coverage_threshold parameters
- Return detailed results including before/after coverage percentages
- Provide summary statistics with coverage ranges and lowest coverage pairs
- Enable UI integration for monitoring and managing indicator completeness
These endpoints expose the db.py indicator coverage methods through the
web API, allowing users to monitor and maintain 100% technical indicator
coverage across all trading pairs via the web interface.
- Add _ensure_indicator_coverage() to verify and backfill after data collection
- Add start_indicator_coverage_monitor() background task for periodic checks
- Configure coverage monitoring with ensure_100_percent_coverage flag
- Set coverage_check_interval_hours (default: 6 hours) for monitoring frequency
- Set backfill_batch_size (default: 200) for efficient backfilling
- Call coverage check after bulk downloads, gap fills, and candle generation
- Start indicator_coverage_monitor task in continuous collection mode
- Log coverage percentages and backfill results for transparency
- Ensure all OHLCV records have complete technical indicator coverage
This integrates the new db.py indicator coverage methods into the main
data collection workflow, ensuring 100% coverage is automatically
maintained across all symbol/interval combinations.
- Add check_indicator_coverage() to detect OHLCV records missing indicators
- Add get_ohlcv_missing_indicators() to identify specific gaps
- Add backfill_missing_indicators() to fill missing indicator data
- Add get_ohlcv_data_range() helper for fetching historical data
- Add get_all_indicator_coverage_status() for system-wide monitoring
- Define REQUIRED_INDICATORS constant with all 16 required indicators
- Process backfills in configurable batches to manage memory
- Calculate indicators using existing utils.calculate_technical_indicators()
- Track coverage statistics before/after backfill operations
- Support for automated indicator completeness verification
This ensures every crypto_ohlcv record has all 16 technical indicators
(adx_14, atr_14, bb_lower, bb_middle, bb_upper, ema_12, ema_26,
macd_histogram, macd_line, macd_signal, rsi_14, sma_20, sma_200,
sma_50, stoch_d, stoch_k) calculated and stored
