trader-memory-core

Track investment theses by registering screener outputs as persistent YAML state files, enforcing lifecycle transitions from IDEA to CLOSED, and generating postmortems with P&L and lessons learned. This consolidates fragmented analysis into a single auditable thesis object.

MAE/MFE computation calculates Maximum Adverse Excursion and Maximum Favorable Excursion metrics using external price data. You need it during postmortem generation to analyze position outcome statistics and evaluate trade performance beyond simple P&L.

Manage position sizing by attaching position-sizer output to thesis objects, and enforce forward-only lifecycle transitions (IDEA → ENTRY_READY → ACTIVE → CLOSED) with JSON Schema validation. This ensures data integrity and prevents invalid status rollbacks.

Yes, you can ingest screener JSON outputs directly into the thesis store to register single-ticker candidates. This preserves raw provenance, classification, and evidence within persistent YAML state files for subsequent review and position attachment.

Yes, it is designed specifically for single-ticker systematic trading workflows. It handles screener ingestion, status transitions, review scheduling, and outcome reports with lightweight index queries and atomic file writes for reliable state persistence.

YAML-based thesis state persistence relies on atomic file writes and lightweight index queries without a centralized database. It is optimized for single-ticker systematic workflows and may not scale efficiently for high-frequency multi-asset portfolio management without external indexing.