---name: market-ingestdescription: Ingest and normalize market data into OHLCV vectors with HNSW indexingargument-hint: <symbol> [--source api]allowed-tools: Bash mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_create mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add mcp__plugin_ruflo-core_ruflo__embeddings_generate---

Market Ingest

Fetch market data for a symbol, normalize to OHLCV vectors, and store with HNSW indexing for fast pattern search.

When to use

When you need to ingest raw market data (price and volume) for a symbol and prepare it for pattern detection and similarity search. This is the first step before running pattern detection or comparison.

Steps

1. Fetch data -- retrieve OHLCV data for the symbol from the configured data source (REST API, CSV file, or manual input)
2. Normalize -- convert raw prices to relative values:
   • Open: (open - prev_close) / prev_close
   • High: (high - open) / open
   • Low: (low - open) / open
   • Close: (close - open) / open
   • Volume: Z-score against rolling mean/std
3. Vectorize -- encode each candle as a 64-dimension padded vector (5 normalized OHLCV values + padding). For semantic embeddings of pattern descriptions, use mcp__plugin_ruflo-core_ruflo__embeddings_generate (NOT embeddings_embed — that tool name does not exist).
4. Store -- call mcp__plugin_ruflo-core_ruflo__memory_store --namespace market-data to persist normalized OHLCV data with symbol+date keys. The memory_* tool family routes by namespace; the agentdb_hierarchical-* family routes by tier (working|episodic|semantic) and ignores namespace strings, so use memory_* here.
5. Index -- call mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add to add vectors to the HNSW index for nearest-neighbor search.
6. Report -- summarize: candles ingested, date range, price range, average volume

CLI alternative

npx @claude-flow/cli@latest memory store --namespace market-data --key "symbol-SYMBOL-DATE" --value "OHLCV_JSON"