Can I customize the turning point detection sensitivity?

Yes, you can adjust parameters like 'pivot_left' and 'pivot_right' to define the number of candles required to confirm a local high or low.

What data source does this skill use for analysis?

The skill primarily uses Yahoo Finance (yfinance) to fetch real-time and historical futures data for Silver (SI=F) and Palladium (PA=F).

How does this skill determine if Palladium is leading Silver?

It utilizes cross-correlation analysis to find the optimal lead-lag window (k-bars) where Palladium returns most accurately predict Silver price movements.

What are 'failed moves' in this context?

Failed moves are Silver price actions that occur without the synchronized participation of Palladium, which historically suggests a higher probability of reversal or a 'fake-out'.

Palladium-Silver Lead-Lag Detector

Name: Palladium-Silver Lead-Lag Detector
Author: fatfingererr

byfatfingererr

Data Science & ML

Analyzes cross-metal correlations between Palladium and Silver to confirm trend turning points and identify failed price moves.

About

This Claude Code Skill empowers traders and analysts to validate Silver price trends by monitoring Palladium as a leading indicator of industrial demand and risk sentiment. By calculating cross-correlations and identifying turning point pivots, the skill distinguishes between confirmed market shifts and 'liquidity noise.' It provides actionable insights through statistical analysis of confirmation rates and failure probabilities, complete with Bloomberg-style visualizations for professional market interpretation.

Key Features

Identification of 'failed moves' to filter out false breakouts and liquidity traps
0 GitHub stars
Automated cross-correlation estimation for lead-lag relationships
Quantifiable participation metrics including volume expansion and directional agreement
Professional Bloomberg-style data visualization and statistical reporting
Multi-method turning point detection (Pivot, Peaks, Slope Change)

Use Cases

Validating silver price breakouts against industrial sentiment indicators
Real-time monitoring for unconfirmed silver price reversals and market noise
Backtesting the historical effectiveness of palladium as a leading indicator

About

Key Features

Identification of 'failed moves' to filter out false breakouts and liquidity traps
0 GitHub stars
Automated cross-correlation estimation for lead-lag relationships
Quantifiable participation metrics including volume expansion and directional agreement
Professional Bloomberg-style data visualization and statistical reporting
Multi-method turning point detection (Pivot, Peaks, Slope Change)

Use Cases

Validating silver price breakouts against industrial sentiment indicators
Real-time monitoring for unconfirmed silver price reversals and market noise
Backtesting the historical effectiveness of palladium as a leading indicator