Options trading automation bot — rule-based execution engine

Context

Options trading automation lives in the space between strict rule-based execution and discretionary trader judgment. The build is a deliberate counterexample to the “throw an LLM at it” pattern: a deterministic rule engine where every execution path is auditable.

Problem

LLM-driven trading bots are interesting research but bad operating reality. They fail in ways that aren’t reproducible, and risk controls have to be bolted on around them. A rule-based engine inverts that: rules are explicit, backtestable, and reviewable.

Approach

A Python rule engine with backtest harness as a first-class subsystem: every new rule passes through the backtest before it gets a live-execution path. Risk controls (position size, daily loss caps, instrument filters) sit at the engine level, not as ad-hoc per-rule checks.

Solution

• Python rule engine with deterministic execution paths.
• Backtest harness for validating new rules against historical data before live deployment.
• Configurable risk controls (position size, daily loss caps, instrument filters) at the engine layer.
• Audit log of every execution decision for post-trade review.

Outcome

Above. The rule-based posture is the methodology contribution; deterministic execution and backtest-first validation are the operating discipline.