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plugins/ruflo-market-data/commands/market.md

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Market data commands:

market ingest <symbol> [--period 1D] -- Ingest and normalize market data for the given symbol.

  1. Fetch OHLCV data for <symbol> from the configured data source
  2. Normalize: open/high/low/close as relative percentages, volume as Z-score
  3. Vectorize each candle to a 64-dimension padded vector
  4. Store normalized data via mcp__claude-flow__agentdb_hierarchical-store in market-data namespace
  5. Add vectors to HNSW index via mcp__claude-flow__ruvllm_hnsw_add
  6. Report: candles ingested, date range, min/max/avg price, volume profile

market patterns <symbol> -- Detect candlestick patterns in recent data.

  1. Recall recent OHLCV data for <symbol> from the market-data namespace
  2. Scan for single-candle patterns (doji, hammer) and multi-candle patterns (engulfing, morning star)
  3. Classify each detection with pattern name, type (reversal/continuation), and reliability score
  4. Store detected patterns via mcp__claude-flow__agentdb_pattern-store
  5. Display: pattern name, date, direction (bullish/bearish), reliability, candle range

market search <pattern-name> -- Search for historical occurrences of a pattern.

  1. Search HNSW index via mcp__claude-flow__ruvllm_hnsw_route for vectors matching the pattern
  2. Recall matching entries from market-patterns namespace
  3. Rank by similarity score and recency
  4. Display: symbol, date, pattern match score, subsequent price action (if available)

market history <symbol> -- Show ingestion history and data coverage.

  1. Query market-data namespace for all entries matching <symbol>
  2. Compute: total candles stored, date range, gaps in coverage
  3. Show data freshness (last ingestion timestamp)
  4. List detected patterns count by type

market compare <sym1> <sym2> -- Compare pattern profiles between two symbols.

  1. Recall pattern data for both symbols from market-patterns namespace
  2. Compute correlation: shared pattern types, timing overlap, direction agreement
  3. Display side-by-side comparison with pattern frequency and reliability differences
  4. Highlight divergences that may indicate trading opportunities