Market Ingest

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__claude-flow__embeddings_generate (NOT embeddings_embed — that tool name does not exist). 4. Store -- call mcp__claude-flow__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__claude-flow__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"