agentleFS
tusharagg1tusharagg1/aifolimizer/.claude/skills/pead-tracker/SKILL.md

pead-tracker

Skill2 starsChanged 5 months agoMITView on GitHub

Track Post-Earnings Announcement Drift (PEAD) across ALL portfolio holdings - which held names beat/missed last quarter and are still drifting in the surprise direction. Use for "post-earnings-surprise drift", "is PEAD still active for X?", "which holdings are still drifting after earnings?", "earnings beats still drifting", "earnings-drift overlay across my book". This is a whole-book drift scan - NOT a single-name post-report verdict (use earnings-postmortem) and NOT price-momentum ranking (use momentum-scanner).

The file

Show its rules and text

We split this skill into the rules it gives an agent

14 rules. About 0 are common, 6 are distinctive, and 8 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 8

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Before analysis, load prior decisions so verdicts stay consistent across sessions:

    line 10
  2. Very specific:

    Call mcp__aifolimizer__get_profile - account types and capital context

    line 24
  3. Very specific:

    Call mcp__aifolimizer__get_portfolio - full holdings list

    line 25
  4. Very specific:

    Call mcp__aifolimizer__get_earnings_results with all held symbols, quarters=2 - get report dates, surprise %, outcome for last 2 quarters

    line 26
  5. Very specific:

    Call mcp__aifolimizer__get_fundamentals for all symbols - market cap (determines expected drift magnitude), analyst target

    line 27
  6. Very specific:

    Call mcp__aifolimizer__get_technicals for symbols with active drift - pct_from_52w_high, RSI, trend to assess whether drift is still …

    line 28
  7. Distinctive:

    Call steps 3-5 in parallel after step 2 resolves.

    line 30
  8. Distinctive:

    Use ONE clock - calendar days - throughout, to avoid mixing trading-day and calendar-day units.

    line 34
  9. Distinctive:

    Expected Edge: use market cap to estimate remaining drift.

    line 56
  10. Distinctive:

    Only holdings still in drift window.

    line 61
  11. Very specific:

    If the action is ride (ADD), first call mcp__aifolimizer__get_positioning_signals with symbols=[ticker].

    line 84
  12. Very specific:

    Always use get_profile first - never hardcode accounts or capital

    line 90
  13. Very specific:

    Never fabricate report dates - only use dates from get_earnings_results

    line 91
  14. Distinctive:

    If fewer than 3 holdings have earnings results in last 85 days, say so explicitly - don't pad

    line 94

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

PEAD Tracker (Post-Earnings Announcement Drift)

Stage 0 - Decision Memory (load FIRST)

Before analysis, load prior decisions so verdicts stay consistent across sessions:

  • mcp__aifolimizer__get_cross_ticker_lessons with max_lessons=3 - portfolio-level win/loss patterns
  • For any name you issue a per-ticker BUY/SELL/TRIM/HOLD on, also load mcp__aifolimizer__get_ticker_decision_history (ticker=…, max_decisions=5) and mcp__aifolimizer__get_ticker_reflection (symbol=…, n=3).

Reconciliation rule: if a prior decision exists and your new read flips it, state explicitly WHY it changed (new data / catalyst / price move). Never silently contradict a logged decision - that drift is exactly what this prevents.

Research basis

Bernard & Thomas (1989): stocks keep moving in direction of earnings surprise after earnings are public. Drift window: ~60 trading days (~85 calendar days) from report date. Expected abnormal return by size: small firms +5.1%, mid +4.3%, large +2.8%.

How to run

  1. Call mcp__aifolimizer__get_profile - account types and capital context
  2. Call mcp__aifolimizer__get_portfolio - full holdings list
  3. Call mcp__aifolimizer__get_earnings_results with all held symbols, quarters=2 - get report dates, surprise %, outcome for last 2 quarters
  4. Call mcp__aifolimizer__get_fundamentals for all symbols - market cap (determines expected drift magnitude), analyst target
  5. Call mcp__aifolimizer__get_technicals for symbols with active drift - pct_from_52w_high, RSI, trend to assess whether drift is still running or exhausted

Call steps 3-5 in parallel after step 2 resolves.

Drift window logic

Use ONE clock - calendar days - throughout, to avoid mixing trading-day and calendar-day units. The ~60-trading-day Bernard-Thomas window ≈ 85 calendar days; we measure everything against that 85-day calendar window.

For each holding with a recorded earnings report:

  • Compute calendar days since report date (use today's date)
  • Active window: 0-85 calendar days since report
  • Late window: 55-85 calendar days (drift fading - last chance to ride or exit)
  • Expired: > 85 calendar days (no PEAD edge remaining)

Drift direction:

  • outcome == "beat" + positive surprise_pct → positive drift expected, bias HOLD/ADD
  • outcome == "miss" + negative surprise_pct → negative drift expected, bias TRIM/EXIT
  • outcome == "meet" → no directional drift signal

Output structure

1. Drift summary table

Render markdown table. Columns:

TickerReport DateDays SinceSurprise %OutcomeDrift WindowExpected EdgeAction
  • Expected Edge: use market cap to estimate remaining drift. Large-cap (>$100B): 2.8% over full window. Mid ($2B-$100B): 4.3%. Small (<$2B): 5.1%. Pro-rate on the same 85-day calendar clock: days_remaining = max(0, 85 - calendar_days_since), then edge_remaining = full_edge × days_remaining / 85. The max(0, …) clamp means an expired name shows 0% remaining edge, never a negative number.
  • Action: ride (beat, early window), exit soon (beat, late window), trim (miss, any window), flat (meet or expired)

2. Active plays (≤5 bullets)

Only holdings still in drift window. Format per line:

TICKER · beat/miss X.X% surprise · Day N of ~85 (calendar) · ~Y% drift remaining · action

3. Expired positions check

List holdings where PEAD window just closed (85-100 days). Note: "PEAD edge gone - hold/trim on fundamentals only."

4. No-data gaps

List symbols where get_earnings_results returned empty or null dates. "TSX coverage gap" if .TO ticker. Do not fabricate dates.

5. Recommendation (Canadian tax-aware)

For each ride or trim action that conflicts with current portfolio weight:

  • Which account to act in (TFSA > Non-Reg for gains, Non-Reg for harvesting losses)
  • Max position cap: if adding via PEAD signal, cap at 5% incremental add - this is a momentum overlay, not a conviction change

After output - log decisions

For each Active play with action ride (ADD) or trim/exit soon (TRIM/EXIT):

  • If the action is ride (ADD), first call mcp__aifolimizer__get_positioning_signals with symbols=[ticker]. If crowding_score >= 70 the drift is already consensus-crowded (late entry = negative expected alpha) - downgrade to HOLD or cap the incremental add hard; this is a momentum overlay, not a conviction buy. Favor names with crowding_score <= 30.
  • Optional drift confirmation: call mcp__aifolimizer__get_earnings_commentary with ticker=<ticker>. Use mgmt_tone_trend (relative vs prior quarters), NOT the absolute tone. improving after a beat (or deteriorating after a miss) = the language confirms the drift direction → keep conviction. Divergence (beat but deteriorating tone) → downgrade one notch. Skip if relative is false or source is None.
  • Then call mcp__aifolimizer__log_recommendation with skill="pead-tracker" (the param is skill, not skill_used - that belongs to log_trade_decision), ticker, action (ADD/HOLD/TRIM/SELL), conviction (HIGH/MED/LOW per surprise magnitude + days remaining), target_pct + stop_pct (% from entry - the schema takes percentages, not absolute prices), rationale (1-line citing surprise % + drift days remaining). Skip flat (no edge). Feeds forward win-rate / track-record loop.

Rules

  • Always use get_profile first - never hardcode accounts or capital
  • Never fabricate report dates - only use dates from get_earnings_results
  • Expected drift is statistical, not guaranteed - present as probabilistic edge, not certainty
  • Under 500 words
  • If fewer than 3 holdings have earnings results in last 85 days, say so explicitly - don't pad

Gotchas

  • get_earnings_results returns yfinance earnings_history - has date field but TSX (.TO) tickers sparse. Note gap, don't fabricate.
  • "Days since report" requires comparing report date to today. Use currentDate from context. Do not assume.
  • Large-cap beats often get priced faster (institutional speed) - 2.8% drift estimate is conservative and may already be gone by day 5 for mega-caps. Flag if stock already ran >2% since report.
  • Miss + negative drift = same logic inverted. Don't add to a PEAD-miss name just because you like the company.
  • surprise_pct from yfinance is EPS-only. Revenue miss can override EPS beat (stock sold off despite beat). Cross-check get_news_headlines if price moved opposite to EPS outcome.
  • get_technicals cached 1h - RSI/trend signal may lag. Mention cache timestamp.
  • PEAD effect strongest in first 20 trading days post-report, fades after. Strongest entry = day 1-14.
  • Crypto holdings: no earnings. Skip. List in section 4.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.