Qc Order Forensics

Forensic diagnosis engine for backtest order data — trade quality, ROI attribution, monthly cashflow, drawdown root-cause analysis, and LLM-readable reports.

This skill appears to be a local Python tool that analyzes QuantConnect-style order CSVs and optional result.json. Before installing/running: 1) Run it in an isolated environment (virtualenv/container) and install pandas/numpy per requirements.txt. 2) Test on non-sensitive sample CSVs to confirm outputs; the code has some non-security bugs (e.g., cashflow sign/aggregation logic) that can affect results. 3) Review the full forensics.py file yourself (the listing provided to me was truncated) to ensure there are no unexpected network calls or hidden behavior in the unseen portion. 4) If you want to limit risk, disable autonomous invocation for this skill or require explicit user invocation. 5) Do not feed production PII or secret account files into the tool until you’ve validated the full source and outputs.

Type: OpenClaw Skill Name: qc-order-forensics Version: 1.0.0 The skill bundle is a legitimate data analysis tool designed to process QuantConnect backtest results (orders.csv and result.json). The Python code in forensics.py uses standard libraries like pandas and numpy to calculate trading metrics, ROI, and drawdown periods without any evidence of data exfiltration, malicious execution, or prompt injection.