Market Ingest
Skill Verified ActiveIngest and normalize market data into OHLCV vectors with HNSW indexing
To prepare raw market data for advanced analysis by normalizing it into a searchable OHLCV vector format with efficient indexing.
Features
- Ingest market data for a symbol
- Normalize OHLCV data
- Vectorize market data points
- Store data in a structured format
- Index data with HNSW for fast search
Use Cases
- Preparing historical market data for AI-driven trading strategies
- Building a searchable database of financial market patterns
- First step in a market analysis pipeline before pattern detection
Non-Goals
- Performing actual pattern detection or prediction
- Real-time streaming data ingestion
- Directly executing trading actions based on data
Workflow
- Fetch data for the specified symbol
- Normalize OHLCV data to relative values
- Vectorize each candle into a padded vector
- Store normalized data using memory tools
- Add vectors to the HNSW index
- Report summary of ingested data
Practical Utility
- info:Usage examplesWhile the SKILL.md provides a CLI alternative, specific end-to-end examples demonstrating input, invocation, and output for the skill's core functionality are limited.
- info:Edge casesThe SKILL.md briefly mentions normalization steps but lacks detailed documentation on specific failure modes and recovery steps for edge cases.
Installation
First, add the marketplace
/plugin marketplace add ruvnet/ruflo/plugin install ruflo-market-data@rufloQuality Score
VerifiedTrust Signals
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