Skip to main content

Market Pattern

Skill Verified Active

Detect and classify candlestick patterns from ingested OHLCV data

Purpose

To provide automated detection and classification of candlestick patterns within financial market data, aiding in trading analysis and decision-making.

Features

  • Detects single, two, three, and multi-candle patterns
  • Classifies patterns by name, type (reversal/continuation), and direction (bullish/bearish)
  • Assigns a reliability score to detected patterns
  • Stores detected patterns in agent databases (pattern-store, memory-store)

Use Cases

  • Identifying bullish or bearish reversal patterns in historical market data
  • Detecting continuation patterns to predict price trends
  • Automating the process of scanning large datasets for specific technical indicators
  • Storing and referencing detected patterns for backtesting or further analysis

Non-Goals

  • Ingesting raw market data (requires `market-ingest` skill)
  • Providing direct trading execution (suggests actions based on patterns)
  • Performing complex financial modeling beyond candlestick pattern recognition

Workflow

  1. Load normalized OHLCV data for a symbol and period
  2. Iterate through candle sequences to identify predefined patterns
  3. Classify each detected pattern with name, type, direction, and reliability
  4. Rank patterns by reliability score
  5. Store patterns in the recommended pattern-store or a plain memory store
  6. Display pattern details including name, date range, direction, and suggested action

Practices

  • Data analysis
  • Pattern recognition
  • Technical analysis

Prerequisites

  • OHLCV market data ingested and available via `market-data` namespace
  • Access to Claude Code environment with necessary Ruflo tools

Installation

First, add the marketplace

/plugin marketplace add ruvnet/ruflo
/plugin install ruflo-market-data@ruflo

Quality Score

Verified
99 /100
Analyzed about 17 hours ago

Trust Signals

Last commitabout 18 hours ago
Stars50.2k
LicenseMIT
Status
View Source

Similar Extensions

Survey Insect Population

100

Design and execute insect population surveys covering survey design, sampling methods, field execution, specimen identification, diversity index calculation including Shannon-Wiener and Simpson indices, statistical analysis, and reporting. Covers defining survey objectives, selecting study sites, determining sampling intensity and replication, choosing sampling methods appropriate to target taxa, standardizing collection effort, recording environmental covariates, identifying specimens to the lowest practical taxonomic level, calculating species richness, Shannon-Wiener diversity (H'), Simpson diversity (1-D), evenness, rarefaction curves, multivariate ordination, and producing survey reports with species lists and conservation implications. Use when conducting baseline biodiversity assessments, monitoring insect populations over time, comparing insect communities across habitats or treatments, assessing environmental impact, or supporting conservation planning with quantitative ecological data.

Skill
pjt222

Fit Drift Diffusion Model

100

Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.

Skill
pjt222

Measure Experiment Design

100

Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.

Skill
product-on-purpose

PyDESeq2

100

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

Skill
K-Dense-AI

Ops Yolo

100

YOLO mode. Spawns 4 parallel C-suite agents (CEO, CTO, CFO, COO). Each analyzes the business from their perspective using ALL available data. Produces unfiltered Hard Truths report. After user types YOLO, autonomously runs the business for a day using /loop.

Skill
Lifecycle-Innovations-Limited

Pipeline Forecasting

100

Generate predictive pipeline forecasts with confidence intervals and scenario modeling for revenue planning

Skill
guia-matthieu

© 2025 SkillRepo · Find the right skill, skip the noise.