daily-update

name

daily-update

description

Run the end-of-day update workflow: sync trades, fetch prices, regenerate cache, and summarize what changed. Use this skill when the user says "daily update", "end of day", "update everything", "what happened today", "run the daily", "catch me up", or any variation that implies they want to bring all data current and see a summary. Also use it when the user opens a session after market close and hasn't updated yet today — check the most recent dates in cache/ and suggest running the daily if data is stale.

Daily Update

The end-of-day ritual. This skill orchestrates the other skills in the right order and produces a summary of what changed. No scripts. You compose existing skills and read the results.

The sequence

1. Sync trades (if accounts exist)

Check if ~/.lafmm/accounts/ has any account directories with account.toml. If so, run the sync-trades skill for each account to import today's executions.

Skip this step if no accounts are configured. The user may not have connected a broker yet.

2. Fetch prices

Read every group.toml in ~/.lafmm/data/ to get the full ticker list. Fetch closing prices for each ticker using the fetch script. See references/fetch-prices.md for the command and options.

Run all tickers, leaders and tracked stocks across all groups. The script is idempotent — fetching a ticker that's already current is a no-op.

Parallelize: read each group.toml to get its tickers, then launch one subagent per group, all in background. Each subagent runs the fetch script for all tickers in its group. Do not loop sequentially through 90+ tickers in a single bash command.

While prices fetch in background, start gathering data for step 4 that does not depend on cache: run lafmm stats --json, read tape.md, read insights/, fetch quotes for open positions. When all fetch subagents complete, verify prices, then run step 3.

Verify prices — after all fetches complete, run these checks in parallel using subagents:

• Staleness: scan every ticker dir in data/. The latest CSV row's date must match the most recent trading day. Retry the fetch for any that are stale or missing.
• Cross-reference: pick tickers across different groups (at least one leader per macro sector). Run the quote skill for each. Compare the quote's close against the CSV's close for the same date. Any mismatch means the data may be corrupted — report to the user before proceeding.

Only proceed to step 3 after verification passes.

3. Sync cache

Regenerate all markdown in cache/ from the updated price data. See references/sync-lafmm-cache.md for the command. This is where the engine processes today's close and produces any new signals, column transitions, or pivotal points.

4. Summarize changes

Ground the summary in data before interpreting.

Data pass. Collect from these sources:

• cache/market.md for market trend and group summary
• Each cache/{group}/group.md for signals and column transitions (compare signal dates to the most recent trading day in the data)
• lafmm stats --json for current metrics, open positions, genome
• accounts/*/capital/*.csv latest row for actual account value
• Quote skill for current prices on any open position tickers
• Journal entries from step 1, with their signal column (auto-filled by the parse script)
• tape.md for the user's observations on today's trades
• insights/{YEAR}.md for last session's observations as comparison baseline
• For any significant move (5%+ daily, Key Price reversal, DANGER fired), run news-context skill to identify catalysts

Present. In this order, skip sections with nothing to report:

1. Signals. DANGER first (always prominent), then BUY/SELL with rule citations, resumption vs reversal distinguished
2. Transitions. Name them explicitly ("UT → NR", not "now in NR")
3. Market trend, if the bullish/bearish group count shifted
4. Trade alignment. Today's trades with their signal column from the journal. System-aligned or discretionary.
5. Open positions. Entry from journal, current from quote, unrealized computed in Python. System state from cache.
6. Performance delta. Key metrics from lafmm stats --json vs last session (return, win rate, Sharpe, post-system record). Note robustness (ex-best ticker expectancy).
7. News catalysts, only for significant moves flagged above

Interpret. After all data is presented, 2-4 sentences connecting it. Anchor every claim to something from the data pass. Cross-sector patterns, concentration observations, approaching pivots.

5. Mine journal and record observations

Run the journal-mine skill for each account. This is incremental — if no new observations exist since the last scorecard update, it finishes instantly. When new observations are present, it links them to today's stats output, resolves pending predictions, and updates accounts/*/scorecard.md.

After mining, if you notice a behavioral pattern over multiple sessions (consistent trading against signals, ignoring DANGER alerts, over-concentrating in one sector), append a brief observation to ~/.lafmm/insights/{YEAR}.md.

This is for patterns that emerge over time, not one-off events. One discretionary trade is normal. Ten consecutive discretionary trades with no signal alignment is a pattern worth noting.

Interaction principle

Steps 1-3 are mechanical — execute them and report progress briefly. Do not pause between steps unless an error occurs.

Step 4 is the deliverable — present the summary clearly and pause. The user may want to drill into specific groups, ask about specific signals, or discuss what to do next.

Step 5 happens only when warranted — use your judgment.

When data is stale

At the start of any session, check the most recent date in cache/market.md. If it's earlier than the last trading day, suggest: "Your data is from {date}. Want me to run the daily update?"

Weekends and holidays: the most recent trading day for US markets is the last weekday that wasn't a market holiday. Don't suggest updates on Saturday morning for Friday's data if it was already synced Friday evening.

What this skill does NOT do

• Does not make trading decisions. It presents what happened.
• Does not modify group.toml. Threshold tuning is a separate action.
• Does not add or remove groups. That's build-watchlist.
• Does not produce standalone performance analysis. That's the stats skill.