entry-signals

Evaluate historical pattern data to output entry signal metrics for crypto trading decisions.

Updated Nov 25, 2025

Search Tags:#risk management #data analytics #confidence #crypto #trading signals #entry signals #pattern history

Ready to run

npx skills add https://github.com/Cambixx/bot-trading --skill entry-signals-cambixx

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: entry-signals
Source: https://github.com/Cambixx/bot-trading/tree/main/.agents/skills/entry-signals
Command: npx skills add https://github.com/Cambixx/bot-trading --skill entry-signals-cambixx

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Traders often rely on intuition or noisy heuristics to enter positions. This skill provides data-driven entry signals derived from historical pattern performance, enabling better-informed trading decisions.

Core Features & Use Cases

Historical signal metrics: displays success rates, samples, and confidence for each pattern.
Multi-pattern evaluation: evaluates multiple entry patterns to support risk-aware decisions.
Decision support: helps determine when to enter positions based on verified signals and seen counts.

Quick Start

Prompt: "Show me active entry signals with confidence above 0.6 and propose entry opportunities for BTCUSDT."

Frequently Asked Questions about entry-signals

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

FAQPage Schema

How do I identify high-confidence crypto trading signals from historical patterns?▼

To identify high-confidence crypto trading signals, you need a tool that evaluates historical pattern performance metrics, including success rates, sample counts, and confidence levels, to filter active signals and support entry decisions across multiple timeframes.

How do I use historical pattern data to decide when to enter a crypto position?▼

To decide when to enter a crypto position, you can query active signals using a confidence threshold, such as 0.6, to retrieve pattern names, success rates, and seen counts, which provide data-driven decision support for your trades.

What data is required to evaluate crypto entry signals for risk management?▼

Evaluating crypto entry signals for risk management requires access to an active signals dataset and historical pattern metadata, which are processed to output signal names, success rates, samples, confidence, and seen counts.

What is the best way to compare multiple crypto entry patterns before trading?▼

The best way to compare multiple crypto entry patterns is by evaluating historical success rates and sample counts across multiple timeframes, which helps you make risk-aware trading decisions instead of relying on intuition or noisy heuristics.

Does this approach to entry signals work for all cryptocurrency pairs?▼

This approach to entry signals works for cryptocurrency pairs like BTCUSDT by applying historical pattern performance data, but it requires the active signals dataset and pattern metadata to be available for the specific trading pair you are analyzing.