IgorGanapolsky/trading
Paper-trading SPY iron condors (15-delta, 30-45 DTE). Validating strategy on 30 trades before scaling.
Implements multi-agent LLM consensus voting for trade decisions with atomic 4-leg execution via Alpaca's multi-leg API, ensuring all iron condor legs fill together or none do. Features a retrieval-augmented generation (RAG) knowledge base that captures 122+ documented trade lessons and automatically feeds them back into the decision loop for continuous learning. Built on a modular orchestrator architecture with hard-coded safety gates (5% max risk, 100% stop-loss enforcement, VIX blocking) that prevents execution until real-time data quality thresholds are met.
Stars
23
Forks
5
Language
Python
License
MIT
Category
Last pushed
Mar 28, 2026
Commits (30d)
0
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