/market
Market data operations — ingest feeds, detect patterns, and search historical data
> /plugin marketplace add ruvnet/ruflo
Command definition
market.mdname: market
description: Market data operations — ingest feeds, detect patterns, and search historical data
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__plugin_ruflo-core_ruflo__memory_store --namespace market-data` (the `memory_*` family is namespace-routed; `agentdb_hierarchical-*` routes by tier and ignores namespace strings — see skills/market-ingest/SKILL.md) 5. Do not add candles to the `ruvllm_hnsw_*` router (it holds about 11 patterns); candles are retrieved by key from the `market-data` namespace 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 the dual-path pattern (see skills/market-pattern/SKILL.md): `mcp__plugin_ruflo-core_ruflo__agentdb_pattern-store` (typed, ReasoningBank-routed — do NOT pass `namespace`) AND `mcp__plugin_ruflo-core_ruflo__memory_store --namespace market-patterns` (namespace-routed; `agentdb_pattern-*` ignores namespace strings) 5. Display: pattern name, date, direction (bullish/bearish), reliability, candle range
**`market search <pattern-name>`** -- Search for historical occurrences of a pattern. 1. Search the `market-patterns` namespace with `mcp__plugin_ruflo-core_ruflo__memory_search` for stored detections of the pattern (optionally classify a candle window against the few pattern prototypes with `ruvllm_hnsw_route`) 2. Recall the matching entries and their symbols and dates 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 between the two profiles. This is descriptive analysis of stored history, not a trading recommendation, and this plugin does not place orders
Read more
name: market description: Market data operations — ingest feeds, detect patterns, and search historical data
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__plugin_ruflo-core_ruflo__memory_store --namespace market-data` (the `memory_*` family is namespace-routed; `agentdb_hierarchical-*` routes by tier and ignores namespace strings — see skills/market-ingest/SKILL.md) 5. Do not add candles to the `ruvllm_hnsw_*` router (it holds about 11 patterns); candles are retrieved by key from the `market-data` namespace 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 the dual-path pattern (see skills/market-pattern/SKILL.md): `mcp__plugin_ruflo-core_ruflo__agentdb_pattern-store` (typed, ReasoningBank-routed — do NOT pass `namespace`) AND `mcp__plugin_ruflo-core_ruflo__memory_store --namespace market-patterns` (namespace-routed; `agentdb_pattern-*` ignores namespace strings) 5. Display: pattern name, date, direction (bullish/bearish), reliability, candle range
**`market search <pattern-name>`** -- Search for historical occurrences of a pattern. 1. Search the `market-patterns` namespace with `mcp__plugin_ruflo-core_ruflo__memory_search` for stored detections of the pattern (optionally classify a candle window against the few pattern prototypes with `ruvllm_hnsw_route`) 2. Recall the matching entries and their symbols and dates 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 between the two profiles. This is descriptive analysis of stored history, not a trading recommendation, and this plugin does not place orders
📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification. Agent = Model + Harness.
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