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 through uv run --project <repo> when uv is available, so the repo's pinned jsonschema is reachable even from a foreign cwd or from python3 with no global jsonschema (e.g. cron / Hermes profile runs):

# 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