Michael Unogwu @Michael-tech-create

TrendHunter AI is an autonomous, risk-aware, and explainable AI trading agent designed for options-selling. Its goal is not to maximize the number of trades it makes, but to identify opportunities where the potential reward justifies the risk—and confidently reject trades when it does not. The system focuses on cash-secured put opportunities and combines generative AI reasoning with deterministic software-based risk controls. Market data is analyzed to identify potential opportunities, Gemini evaluates and challenges the trading thesis, and a deterministic Python risk engine acts as the final authority before execution. The Problem Options trading involves complex trade-offs between premium, probability, downside exposure, volatility, position size, and market conditions. An AI model can identify patterns and produce convincing reasoning, but convincing reasoning does not guarantee that a trade is safe or economically attractive. A major weakness of many AI trading concepts is giving the language model too much authority. An LLM can reason about a trade, but it should not be trusted to enforce critical financial constraints by itself. TrendHunter AI solves this by separating AI reasoning from deterministic risk enforcement. How It Works The system follows a structured decision pipeline: Market Data → Opportunity Detection → AI Analysis → Thesis Challenge → Risk Gate → Execution → Trade Autopsy First, TrendHunter gathers market information and searches for potential cash-secured put opportunities. When an opportunity is detected, Gemini analyzes the trade and generates a structured thesis. The agent then challenges its own conclusion by actively searching for weaknesses, downside scenarios, contradictory signals, and reasons the opportunity should be rejected. This self-challenge stage is important because the system is designed not only to ask, “Why should we take this trade?”, but also “Why should we avoid it?”

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