trader-memory-core
Track investment theses across their lifecycle — from screening idea to closed position with postmortem. Register theses from screener outputs, manage state transitions, attach position sizing, review due dates, and generate postmortem reports with P&L and MAE/MFE analysis. Trigger when user says "register thesis", "track this idea", "thesis status", "review due", "close position", "postmortem", or "trading journal".
npx skills add BaggaT236/AI-Trading-Skills --skill trader-memory-core --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Trader Memory Core ## Overview Persistent state layer that bundles screening → analysis → position sizing → portfolio management outputs into a single "thesis object" per investment idea. Tracks what you thought, what happened, and what you learned — across conversations. Phase 1 supports single-ticker theses: dividend_income, growth_momentum, mean_reversion, earnings_drift, pivot_breakout. ## When to Use - After a screener (kanchi, earnings-trade-analyzer, vcp, pead, canslim, edge-candidate-agent) produces candidates - When transitioning a thesis from IDEA → ENTRY_READY → ACTIVE → CLOSED - When attaching position-sizer output to a thesis - When checking which theses are due for review - When closing a position and generating a postmortem with lessons learned ## Prerequisites - Python 3.10+ - `pyyaml` (already in project dependencies) - `jsonschema` (already in `pyproject.toml`; required by `thesis_store.py` and every command that imports it, including `thesis_ingest.py` and `thesis_review.py`) - FMP API key (optional, only for MAE/MFE calculation in postmortem) ### How to invoke the CLI Use the stdlib-only launcher `trader_memory_cli.py` for all CLI work. It transparently routes
- Overview
- When to Use
- Prerequisites
- How to invoke the CLI
- Workflow
- 1. Register — Ingest screener output as thesis
- 2. Query — Search and list theses
- 3. Update — Transition, attach position, link reports
- 4. Review — Check due dates and monitoring status
- 5. Postmortem — Close and reflect
- Output Format
- Thesis YAML (state/theses/)
- Index (state/theses/index.json)
- Journal (state/journal/)
From inside the repo python3 skills/trader-memory-core/scripts/trader_memory_cli.py store --state-dir state/theses list From any other cwd (cron, profile, distribution runner) — point the launcher at the repo export CLAUDE_TRADING_SKILLS_REPO=/path/to/claude-trading-skills python3 "$CLAUDE_TRADING_SKILLS_REPO/skills/trader-memory-core/scripts/trader_memory_cli.py" \ store --state-dir /path/to/state/theses list uv pip install -e /path/to/claude-trading-skills python3 -m pip install jsonschema python3 skills/trader-memory-core/scripts/trader_memory_cli.py ingest \ python3 .../trader_memory_cli.py ingest --source manual --input amd.json --state-dir state/theses/
What does the trader-memory-core skill do?
Track investment theses across their lifecycle — from screening idea to closed position with postmortem. Register theses from screener outputs, manage state transitions, attach position sizing, review due dates, and generate postmortem reports with P&L and MAE/MFE analysis. Trigger when user says "register thesis", "track this idea", "thesis status", "review due", "close position", "postmortem", or "trading journal".
How do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill trader-memory-core --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.