trader-memory-core

You track investment theses by ingesting JSON candidate tickers into a persistent YAML state file, enforcing forward-only lifecycle transitions like IDEA to ACTIVE to CLOSED, and generating postmortems with computed P&L. This maintains a structured thesis record per idea.

Yes, trading journal excursion metrics are computed by optionally fetching FMP EOD prices during the postmortem phase. This retrieves Maximum Adverse Excursion and Maximum Favorable Excursion data for closed positions when end-of-day pricing is available.

Shares-based position sizing is attached during the ENTRY_READY lifecycle state transition. The system links your sizing outputs directly to the persistent thesis object, ensuring every active trade record contains the evidence and share quantities relied upon for execution.

Yes, the state machine enforces forward-only lifecycle transitions across IDEA, ENTRY_READY, ACTIVE, CLOSED, and INVALIDATED states. This prevents backward status changes and maintains strict status history alongside scheduled review cadence using atomic YAML writes.

Scheduling trade reviews by due date is handled within the YAML persistent state file during thesis registration. The system records the review cadence and due dates alongside status history, applying atomic writes to both the state file and index.

Yes, ingesting screener output as JSON is required to register a new thesis candidate into the state. The system validates the JSON report, extracts the single-ticker candidate, and initializes the lifecycle state machine from the IDEA phase.