skills.rest
amitpo23
amitpo23COMMUNITY
Explore all skills by this author →
GitHub

price-visualization

Visualize price data with interactive Plotly charts for OHLC, trends, and comparisons.

Updated Feb 24, 2026
Search Tags:#visualization#comparison#plotly#price#trend#heatmap#candlestick
One-click install
npx skills add https://github.com/amitpo23/medici-price-prediction --skill price-visualization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: price-visualization
Source: https://github.com/amitpo23/medici-price-prediction/tree/main/skills/price-visualization
Command: npx skills add https://github.com/amitpo23/medici-price-prediction --skill price-visualization

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Price data visualization enables quick insights into price movements, volatility, and cross-entity comparisons, reducing decision latency for pricing and trading teams.

Core Features & Use Cases

  • Candlestick (OHLC) charts to show daily price movement (open, high, low, close).
  • Trend lines with confidence bands to reveal direction and uncertainty.
  • Multi-entity price comparison across offices, products, or regions.
  • Waterfall decomposition to attribute price changes to contributing factors.
  • Price heatmaps and box plots to analyze distribution and intensity across time and groups.
  • Fully interactive charts powered by Plotly for zoom, hover details, and exporting.

Quick Start

Load your price data into a DataFrame and call the appropriate visualization function to render the chart.

Frequently Asked Questions about price-visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I visualize OHLC price data with an interactive candlestick chart?▼

You can visualize OHLC price data by loading your pricing data into a DataFrame and calling the candlestick visualization function to render daily open, high, low, and close movements interactively.

What is the best way to compare pricing trends across multiple products or regions?▼

The best way to compare pricing trends across multiple entities is using multi-entity comparison charts, which plot price trend lines with confidence bands to reveal direction and uncertainty across different groups.

Can I use Plotly to create a heatmap for analyzing price distribution over time?▼

Yes, you can use Plotly to create price heatmaps that analyze distribution and intensity across time and groups, providing fully interactive features like zoom, hover details, and exporting.

How do I decompose price changes to understand contributing factors?▼

You can decompose price changes by generating a waterfall chart, which attributes overall price movements to individual contributing factors to aid pricing decision-making.

Does this price visualization approach support box plots for volatility analysis?▼

Yes, this approach supports box plots to analyze price distribution and volatility, alongside candlestick, trend, waterfall, and heatmap charts for comprehensive pricing analytics.

Related Skills

Explore More Data & Analytics Skills→
tradingstrategy-aitradingstrategy-ai
823

extract-test-set

Extract a vault price dataframe into a test module and parquet file.

Official
Intermediate
LeonChaoXLeonChaoX
783

plotly

Generate interactive Plotly visualizations in Python with hover, pan/zoom, and HTML export.

Community
Intermediate
agiprolabsagiprolabs
266

regime-detection

Classify market regimes using volatility and trend indicators on OHLCV data.

Community
Advanced
h4vzzh4vzz
33

data-visualization

Generate static and interactive charts from structured data using matplotlib, seaborn, and plotly.

Community
Intermediate