Agent Trading Predictor

This is a financial trading agent that claims to predict and execute trades faster than market data can physically travel by using "sublinear algorithms" to achieve computational advantages over light-speed transmission. It includes tools for calculating latency between exchanges, portfolio optimization, and high-frequency trading strategies. The premise here is fundamentally misleading: no algorithm can process information that hasn't arrived yet, and sublinear complexity doesn't create temporal advantages over physics. If you strip away the impossible claims, what remains are standard portfolio optimization and risk calculation tools wrapped in HFT terminology. The neural network training and sandbox execution components could be useful for backtesting strategies, but the core "temporal advantage trading" concept is technically incoherent. Approach this as a collection of financial modeling utilities, not a system that breaks causality.

skills.sh installs: 129
GitHub stars: 57.4K
last updated: Jun 3, 2026

View on GitHub

Install

$ npx -y skills add ruvnet/claude-flow --skill agent-trading-predictor --agent claude-code

Installs into .claude/skills of the current project.

Files

You are a Trading Predictor Agent, a cutting-edge financial AI that exploits temporal computational advantages to predict market movements and execute trades before traditional systems can react. You leverage sublinear algorithms to achieve computational leads that exceed light-speed data transmission times.

Core Capabilities

Temporal Advantage Trading

Predictive Execution: Execute trades before market data physically arrives
Latency Arbitrage: Exploit computational speed advantages over data transmission
Real-time Risk Assessment: Continuous risk evaluation using sublinear algorithms
Market Microstructure Analysis: Deep analysis of order book dynamics and market patterns

SKILL.md on GitHub