journal-mine

name

journal-mine

description

Mine journal observations for behavioral patterns, hypothesis accuracy, and ticker-specific edge. Links free-text observations to trade outcomes via lafmm stats, accumulates intelligence in a scorecard that other skills consume. Use this skill when the user says "mine my journal", "update scorecard", "what have I learned", "reflect", "behavioral patterns", "observation accuracy", or any variation implying they want to extract intelligence from their trading observations.

Journal Mine

Extract intelligence from journal observations. The journal is a training set — each observation becomes a labeled sample once its trade resolves. This skill links prose to outcomes and accumulates evidence in a scorecard that informs future decisions.

The agent IS the NLP. No script can classify "I panicked and bailed" as behavioral/panic. The agent reads, understands, links, and writes. Python handles the arithmetic (via lafmm stats --json).

Scorecard vs insights: insights/{YEAR}.md captures qualitative session-over-session observations (informal, append-only). scorecard.md quantifies them — links observations to outcomes with numbers, tracks resolution, computes hit rates. Insights notice; scorecard proves.

When to run

• Automatically as part of daily-update (after step 4, before the final summary). Run every time — incremental passes are cheap when no new observations exist.
• User explicitly asks ("mine my journal", "what have I learned")
• Weekly reflection sessions (full rescan for deeper analysis)

Account scoping

This skill operates on one account at a time. If the user specifies an account name, use it. If only one account exists in ~/.lafmm/accounts/, use it implicitly. If multiple exist and none is specified, ask.

Inputs

All paths relative to ~/.lafmm/.

1. lafmm stats --json — quantitative backbone. Positions with P&L, per-symbol stats, genome codes, win rates. Never recompute what stats already provides.
2. Journal observations — accounts/{name}/journal/{YEAR}/*.md, the ## Observations sections. Read only entries since the scorecard's last-updated date (incremental mode).
3. Existing scorecard — accounts/{name}/scorecard.md. The cumulative state from prior mining passes. If it doesn't exist, this is a first run — read all journals (full scan).
4. Current prices — use the quote skill to resolve pending time-bound predictions. Fall back to the latest CSV row only when the quote skill fails or markets are closed.
5. profile.md — reference for what has already graduated. Specifically check ## Edge Profile, ## Leak Profile, and ## Known Tendencies sections. Don't duplicate patterns that are already there.

Process

Step 1: Anchor

Run lafmm stats --json for the account. This gives you:

• All closed positions with P&L, hold days, entry/exit prices
• Per-symbol breakdown (wins, losses, win rate)
• Genome classification per position (Trend/Setup/Cadence/Volume)
• Signal vs discretionary split

These numbers are truth. Use them directly. Never hand-count.

Step 2: Scope

Read the scorecard's Last updated line. Identify which journal entries are new since then. Read only those files.

If no scorecard exists or user requests full rescan: read all journal files. This is expensive but self-healing.

Step 3: Link

For each observation in scope:

1. Identify which ticker(s) it references (explicit mention or contextual — "sold everything" in a file dated 05-01 with an ACME sell in the trade table means the observation is about ACME)
2. Match to a position from stats output by date + ticker
3. Note the outcome: P&L, hold days, genome code, signal column

Some observations won't link to a specific trade (market-wide thoughts, sector commentary). That's fine — classify them but mark as unlinked.

Step 4: Classify

For each linked observation, determine:

Type (what kind of observation is this):

• hypothesis — directional call or prediction ("will rally", "headed to 125", "uptrend will resume")
• exit_reasoning — why the user exited ("momentum too weak", "selling pressure overhead")
• behavioral — emotional state driving action ("panicked", "FOMO'd back in", "couldn't pull the trigger")
• self_critique — post-hoc assessment ("that was a mistake", "should have held")
• market_read — tape/context observation ("range-bound", "accumulation pattern", "sector rotation into semis")

State (emotional/conviction tone):

• panic, FOMO, conviction, uncertain, frustrated, neutral, confident, greedy

Testable claim (if applicable):

• Extract the prediction: ticker, direction, target, timeframe
• If no explicit timeframe, infer from context (swing = 2 weeks, position = 3 months) or mark as indefinite

Step 5: Resolve pending

Read the scorecard's ## Pending section. For each entry:

1. Check current price against the target/direction
2. If deadline has passed: resolve as accurate or inaccurate
3. If target hit before deadline: resolve as accurate
4. If neither: leave as pending

Move resolved predictions into the appropriate tally (Hypothesis Accuracy section).

Step 6: Update scorecard

Rewrite accounts/{name}/scorecard.md incorporating new evidence. Each section updates incrementally — new observations add to existing counts, they don't replace them.

Use numbers from lafmm stats --json directly for quantitative claims. The agent's role is linking observations to those numbers and spotting correlations, not recomputing statistics.

Important: Write concrete numbers, not vague assessments. "8 observations, 87% accurate" not "mostly accurate". The stats output gives you the P&L — use it.

Step 7: Graduation check

Compare scorecard patterns against profile.md. If a pattern has:

• N >= 10 resolved observations
• Clear statistical significance (hit rate meaningfully above/below 50%, or behavioral correlation with consistent direction)
• Not already in profile.md

Flag it in ## Ready to Graduate. Do not auto-promote — present to the user and let them confirm.

Scorecard format

# Scorecard

Last updated: YYYY-MM-DD | Evidence base: N resolved observations

## Ticker Edge

Per-symbol observation accuracy. Cross-reference with stats
per-symbol data. Only include tickers with 3+ observations.

Format: TICKER: N observations, X% accurate. [one-line insight]

## Behavioral Correlations

Emotional state → outcome patterns. Use stats P&L data for the
numbers.

Format: state → avg outcome $X vs hold (N=Y). [interpretation]

## Hypothesis Accuracy

Directional/thesis call hit rates, broken down by type when enough
data exists (mean-reversion calls, breakout calls, sector rotation
calls).

Format: category: X% hit rate (N=Y). [insight if notable]

## Pending

Unresolved time-bound predictions. One per line.

Format: YYYY-MM-DD | TICKER | "claim text" | check by YYYY-MM-DD

## Pattern Log

Newly discovered patterns (N >= 3). Date discovered, evidence
count, brief description. These are candidates for graduation.

## Ready to Graduate

Patterns with N >= 10 and statistical significance, awaiting user
confirmation for promotion to profile.md.

Integration with other skills

qualify

When qualify runs, it should read the scorecard for:

• Ticker-specific edge ("user reads SNXX exceptionally well")
• Behavioral warnings ("user tends to FOMO into this type of setup")
• Hypothesis track record ("user's mean-reversion calls are 78% accurate — weight their conviction higher")

daily-update

Step 5 (record observations) can trigger journal-mine when it notices the gap between scorecard's last-updated date and current date spans 5+ closed positions.

profile.md

Graduated patterns flow from scorecard → profile. The scorecard is evidence; the profile is conviction. Both are read by the agent at decision time, but profile carries more weight (it's confirmed).

Full rescan mode

If the user says "rescan everything" or the scorecard is missing:

• Read ALL journal files regardless of date
• Rebuild scorecard from scratch
• This handles corruption, format changes, or fresh starts

What this skill does NOT do

• Does not compute trade statistics (that's lafmm stats)
• Does not modify journal entries (journals are immutable truth)
• Does not auto-promote to profile.md (human confirms graduation)
• Does not make trading recommendations (that's qualify)
• Does not predict future outcomes from patterns