trader-memory-core

name

trader-memory-core

description

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".

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

Subcommands: store → thesis_store.py, ingest → thesis_ingest.py, review → thesis_review.py. Everything after the subcommand is forwarded verbatim, so existing argument flags (--state-dir, transition, open-position, etc.) work unchanged.

If the launcher reports that jsonschema is not importable AND uv is not on PATH, the actionable fixes (in priority order) are:

1. Install uv (https://docs.astral.sh/uv/) and re-run the launcher.
2. Install the project's dependencies into the current interpreter:

   uv pip install -e /path/to/claude-trading-skills
   # or, as a last resort:
   python3 -m pip install jsonschema
   

Do not treat the thesis store as unavailable and do not mutate state/theses/*.yaml by hand to work around a missing dependency — schema validation is part of thesis state integrity.

Workflow

1. Register — Ingest screener output as thesis

Read the screener's JSON output and convert to thesis using the appropriate adapter.

python3 skills/trader-memory-core/scripts/trader_memory_cli.py ingest \
  --source kanchi-dividend-sop \
  --input reports/kanchi_entry_signals_2026-03-14.json \
  --state-dir state/theses/

Supported sources: kanchi-dividend-sop, earnings-trade-analyzer, vcp-screener, pead-screener, canslim-screener, edge-candidate-agent, manual.

Each thesis starts in IDEA status.

Manual brokerage entry (fractional shares)

For trades that did not come from a screener — e.g. fractional-share brokers (IBKR, Robinhood, IBI Smart, Alpaca, eToro) or hand journaling — use the manual source with a free-form JSON file (a single object or an array):

{
  "ticker": "AMD",
  "thesis_statement": "AMD AI accelerator momentum, fractional IBI Smart position",
  "thesis_type": "growth_momentum",
  "entry_price": 142.10,
  "entry_date": "2026-05-02",
  "shares": 7.86,
  "stop_price": 128.00
}

python3 skills/trader-memory-core/scripts/trader_memory_cli.py ingest \
  --source manual --input amd.json --state-dir state/theses/

Required: ticker, thesis_statement, thesis_type (one of dividend_income, growth_momentum, mean_reversion, earnings_drift, pivot_breakout). stop_price/stop_loss and target_price/take_profit map to exit.stop_loss/exit.take_profit; entry_price/entry_date/shares are kept in origin.raw_provenance — the authoritative entry price/date and share count are set when you open the position (below). shares may be fractional (the schema accepts any positive number). Like every adapter, manual ingest creates an IDEA thesis only — it never mutates status directly.

To record an already-open broker position, run the explicit lifecycle sequence (the --event-date flags backdate the history so it stays chronological):

# 1. ingest → IDEA (stamped at entry_date)
python3 .../trader_memory_cli.py ingest --source manual --input amd.json --state-dir state/theses/
# 2. IDEA → ENTRY_READY (backdated)
python3 .../trader_memory_cli.py store --state-dir state/theses/ transition <id> ENTRY_READY \
  --reason "existing IBI Smart position" --event-date 2026-05-02
# 3. ENTRY_READY → ACTIVE (fractional shares, backdated)
python3 .../trader_memory_cli.py store --state-dir state/theses/ open-position <id> \
  --actual-price 142.10 --actual-date 2026-05-02 --shares 7.86 --event-date 2026-05-02

2. Query — Search and list theses

python3 skills/trader-memory-core/scripts/trader_memory_cli.py store \
  --state-dir state/theses/ list --ticker AAPL --status ACTIVE

Filter by --ticker, --status, or --type.

3. Update — Transition, attach position, link reports

Each lifecycle operation is available both as a Python function and as a thesis_store.py CLI subcommand. --event-date / --actual-date accept a plain YYYY-MM-DD (widened to midnight UTC) or a full ISO timestamp.

State transition (IDEA → ENTRY_READY only):

python3 skills/trader-memory-core/scripts/trader_memory_cli.py store --state-dir state/theses/ \
  transition <id> ENTRY_READY --reason "validated" [--event-date YYYY-MM-DD]

--event-date backdates status_history.at (use it when backfilling an existing position so the later backdated open-position stays chronological). Python: thesis_store.transition(state_dir, thesis_id, "ENTRY_READY", reason, event_date=...).

Open position (ENTRY_READY → ACTIVE — the only path to ACTIVE):

python3 .../trader_memory_cli.py store --state-dir state/theses/ open-position <id> \
  --actual-price 142.10 --actual-date 2026-05-02 [--shares 7.86] [--event-date 2026-05-02]

--shares accepts fractional quantities. Python: thesis_store.open_position(state_dir, thesis_id, actual_price, actual_date, shares=..., event_date=...).

Trim — partial close (ACTIVE/PARTIALLY_CLOSED → PARTIALLY_CLOSED, or → CLOSED when the whole remainder is sold):

python3 .../trader_memory_cli.py store --state-dir state/theses/ trim <id> \
  --shares-sold 4 --price 120.00 --date 2026-05-10

position.shares is the original opened quantity (immutable); position.shares_remaining tracks what is still open. Each trim appends a status_history ledger entry (shares_sold / price / proceeds / realized_pnl). outcome.pnl_dollars is the cumulative realized P&L (Σ all trims + final close); outcome.pnl_pct = pnl_dollars / (entry_price × original_shares) × 100. A trim that sells the entire remainder closes the thesis (default exit_reason: manual, overridable with --exit-reason). --date is the ledger timestamp (override with --event-date). Python: thesis_store.trim(state_dir, thesis_id, shares_sold, price, date, ...).

Status invariants: ACTIVE ⇒ shares_remaining == shares; PARTIALLY_CLOSED ⇒ 0 < shares_remaining < shares; CLOSED ⇒ shares_remaining == 0. Legacy theses (no shares_remaining) are treated as fully open at runtime.

Close or invalidate (→ CLOSED or INVALIDATED):

python3 .../trader_memory_cli.py store --state-dir state/theses/ close <id> \
  --exit-reason target_hit --actual-price 165.00 --actual-date 2026-06-01
python3 .../trader_memory_cli.py store --state-dir state/theses/ terminate <id> \
  --terminal-status INVALIDATED --exit-reason "thesis broke"

close accepts an ACTIVE or PARTIALLY_CLOSED thesis; from PARTIALLY_CLOSED it adds the final leg and reports the cumulative outcome.

Python: thesis_store.terminate(state_dir, thesis_id, terminal_status, exit_reason, actual_price, actual_date). For CLOSED, delegates to close() which computes P&L (fractional-share aware). For INVALIDATED, P&L is computed if entry/exit prices are available.

Record review (any non-terminal):

Use thesis_store.mark_reviewed(state_dir, thesis_id, review_date=..., outcome="OK"|"WARN"|"REVIEW") to advance next_review_date and record alerts.

Attach position-sizer output:

python3 .../trader_memory_cli.py store --state-dir state/theses/ attach-position <id> \
  --report reports/position_report.json

Python: thesis_store.attach_position(state_dir, thesis_id, report_path) to link position sizing data. Validates that the report mode is "shares" (not budget).

Link related reports:

Use thesis_store.link_report(state_dir, thesis_id, skill, file, date) to cross-reference analysis documents.

4. Review — Check due dates and monitoring status

python3 skills/trader-memory-core/scripts/trader_memory_cli.py review \
  --state-dir state/theses/ review-due --as-of 2026-04-15

List theses with next_review_date <= as_of. Use with kanchi-dividend-review-monitor triggers (T1-T5) for systematic review.

5. Postmortem — Close and reflect

python3 skills/trader-memory-core/scripts/trader_memory_cli.py review \
  --state-dir state/theses/ postmortem th_aapl_div_20260314_a3f1

Generate a structured postmortem in state/journal/. If FMP API key is available, includes MAE/MFE (Maximum Adverse/Favorable Excursion) metrics.

Summary statistics:

python3 skills/trader-memory-core/scripts/trader_memory_cli.py review \
  --state-dir state/theses/ summary

Shows win rate, average P&L%, and per-type breakdown across all closed theses.

Output Format

Thesis YAML (state/theses/)

Each thesis is a YAML file with:

• Identity: thesis_id, ticker, created_at
• Classification: thesis_type, setup_type, catalyst
• Lifecycle: status, status_history
• Entry/Exit: target prices, actual prices, conditions
• Position: shares (fractional supported), value, risk (from position-sizer or open-position --shares)
• Monitoring: review dates, triggers, alerts
• Origin: source skill, screening grade, raw provenance
• Outcome: P&L, holding days, MAE/MFE, lessons learned

Index (state/theses/_index.json)

Lightweight index for fast queries without loading full YAML files.

Journal (state/journal/)

Postmortem markdown reports: pm_{thesis_id}.md.

Key Principles

• Forward-only transitions: IDEA → ENTRY_READY → ACTIVE → CLOSED (no backtracking)
• Raw provenance: All original screener data preserved in origin.raw_provenance
• Atomic writes: All file operations use tempfile + os.replace
• Git-tracked state: state/ directory is committed, providing audit trail
• Phase 1 scope: Single-ticker theses only (pair trades and options in Phase 2)

Resources

• references/thesis_lifecycle.md — Status states and valid transitions
• references/field_mapping.md — Source skill → canonical field mapping
• schemas/thesis.schema.json — JSON Schema for thesis validation
• ../../examples/workflows/trade-memory-loop/sample-run-full-path/ — Worked end-to-end Plan → Trade → Record → Postmortem → Backtest → Journal example