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