⚠️Install with caution. This skill has very few installs. Always review the source and verify it on clawhub.ai before installing. Community-built skills run with agent permissions — only install ones you trust.

📉 Stock Trading

Qc Deep Feature Forensicsv1.0.0

Name: Qc Deep Feature Forensics
Author: tltby12341

qc-deep-feature-forensics

tltby12341

12-dimensional technical feature attribution engine — compares winner vs loser trade entry conditions using RSI, Bollinger, MACD, volume surge, gap, and more...

latest

Download Package View on ClawHub

Installs (all time)

Installs (current)

Downloads

785

Stars

CreatedMar 17, 2026

UpdatedMay 9, 2026

Install & Quick Start

Install via ClawdBot CLI:

clawdbot install tltby12341/qc-deep-feature-forensics

Skill Package3 files

📋SKILL.mdmarkdown

Failed to load file.

Quality Score

B57/100

Grade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.

Market Validation4/35

· 265 downloads (low demand)
· No tracked installs (may still have real users via manual install)

Documentation20/25

· SKILL.md present
· Detailed documentation (≥3000 chars)
· Contains usage examples or trigger description
· Detailed summary

Package Completeness8/15

· skillAssets present (2 files)

💡

Usage Guide

Generated May 12, 2026

Quantitative tradersRetail stock tradersHedge fund analystsAlgorithmic trading researchersTrading educatorsintermediate

💡 Application Scenarios

Quantitative Trading Strategy OptimizationFinance

A quant fund uses this skill to analyze entry conditions of winning vs losing trades in their backtest. By extracting 12 technical features, they identify which factors (e.g., volume surge, gap percentage) most contribute to profitability, allowing them to refine their strategy and filter out low-probability setups.

Retail Trader Post-Trade AnalysisPersonal Finance / Trading

Individual traders run this skill on their own trade logs to understand why some trades performed better. The what-if simulation helps them test hypothetical rules (e.g., avoid trades with RSI > 70) to improve future entries without risking real capital.

Hedge Fund Risk ManagementFinance

Risk managers at a hedge fund use the feature attribution to detect when winning trades share common microstructures (e.g., strong gaps with volume confirmation). This insight helps set dynamic risk limits and adjust position sizing based on market conditions.

Algorithmic Trading ResearchFinance / Technology

Researchers developing trading algorithms use the skill to validate new entry signals. The statistical comparison of winners vs losers provides empirical evidence for which indicators matter, aiding in feature selection for machine learning models.

Educational Tool for Trading CoursesEducation

Trading academies incorporate this skill into their curriculum to teach students how to conduct post-trade analysis. By running it on simulated trades, students learn to objectively evaluate their decision-making and develop data-driven trading plans.

💼 Business Models

Subscription-based Trading Analytics PlatformSubscription fees ($29-$99/month for individuals; custom pricing for firms)

Offer a premium SaaS platform that integrates this skill, allowing users to upload their trade history and receive automated forensic reports. Revenue comes from monthly or annual subscriptions with tiered pricing based on report frequency and number of trades analyzed.

Performance Coaching for TradersSession fees ($150-$500/session) or monthly coaching packages ($500-$2000/month)

Provide one-on-one coaching services where a coach runs this skill on clients' trades and interprets results to recommend personalized trading improvements. Revenue is from session fees or packages.

White-Label API for Trading SoftwareLicensing fees ($0.01-$0.10 per analysis) or flat monthly fee ($1000-$5000)

License the skill as an API that other trading platforms (e.g., brokerages, portfolio management tools) can integrate to offer advanced analysis features to their users. Revenue from licensing fees per API call or flat monthly fee.

💬 Integration Tip

To integrate, ensure the Python environment meets pandas, numpy, and yfinance dependencies, and provide a user interface for uploading CSV trade logs; cache management is automatic but pre-populate cache for offline use.