Neuro Scalp

AI-driven high-frequency crypto scalping bot for OKX with reinforcement learning, dynamic risk control, and real-time market data monitoring.

This repository appears to implement the advertised OKX scalping bot, but I recommend caution before installing or running it with real credentials. Key actions to consider before use: - Do not run with real OKX API keys on any machine you care about until you audit and test in isolation. The code will execute live orders when keys are present. - Treat model checkpoint files as a trust boundary. torch.load can execute arbitrary code when loading malicious .pth files — only load checkpoints you or a trusted party produced. Consider disabling automatic model loading or sandboxing the process. - The registry metadata is inconsistent: the bundle fails to declare required environment variables (.env.example is missing). Expect to supply OKX_API_KEY, OKX_SECRET, OKX_PASSPHRASE and secure your Redis instance (do not expose ports publicly). - Resolve dependency mismatches (ccxt.pro import vs ccxt in requirements) and test in a disposable, network-isolated environment (e.g., throwaway VM or container) using OKX testnet credentials first. - If you plan to expose the dashboard, put it behind authentication and do not bind it to 0.0.0.0 on an internet-facing host without proper access controls. If you want, I can: (1) list the exact places to harden (code lines to change) to mitigate torch.load risk; (2) produce an installation checklist to run safely in testnet; or (3) produce a minimal audit patch that refuses to load model files unless signed/trusted.

Type: OpenClaw Skill Name: neuro-scalp Version: 1.0.2 The skill bundle implements a functional AI-driven cryptocurrency trading bot for the OKX exchange, featuring real-time data ingestion, feature engineering, and a reinforcement learning model. It follows security best practices by utilizing environment variables for API credentials (main.py) and lacks any indicators of data exfiltration, backdoors, or malicious prompt injection. The inclusion of a detailed system architecture prompt in the README.md and a FastAPI dashboard (dashboard/app.py) suggests a well-structured, legitimate quantitative trading tool.