Finance — Jarvis

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VIX Futures Term Structure

Compare dates:

Dot color = contract expiry (tracks across dates) · Hover Spot History below to preview curves

Contango Mode:

VIX Spot History

Contango / Backwardation History

Related Volatility Indices

Volatility Compression Watch — Vixologist Squeeze Template 📄 docs

TTM Squeeze Status

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Computing...

BBand Width %

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vs 20d rolling mean

StdDev(10) Compression

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current vs 20d MA

SPX Trend (3d)

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Setup requires uptrend

6-Month Realized Volatility (StdDev 10-Day) — Compression Zones Highlighted

Light red zones = StdDev below 25th percentile (compression state). Thrasher framework: compression under buy pressure → explosive release. Cross-ref: Thrasher 2017 paper + Lakha correlation divergence

Docs: Vixologist Template (what & why) · Thrasher 2017 Paper (full methodology) · Compare both
Memory: reference_vixologist_thrasher_squeeze.md · reference_options_insight_imran_lakha.md · Sentinel: svix-squeeze-check (4:00 PM ET)

VIX Dispersion (Thrasher 2017) — Paper-Faithful Signal 📄 docs

VIX Signal

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StdDev(20) vs 15th pct

VIX 20-Day StdDev

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threshold: —

StdDev Percentile

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vs full history

VVIX Signal (secondary)

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15th pct of VVIX StdDev(20)

VIX 20-Day Standard Deviation vs Threshold (1Y) — Signal Days Marked

Blue line = VIX 20d StdDev. Dashed = 15th-percentile threshold (adaptive to full history). Red markers = signal-fired days (StdDev ≤ threshold with 10-day dedup).

Docs: Thrasher 2017 Paper (full methodology) · Vixologist Template (comparison) · Compare both
Memory: reference_thrasher_paper_methodology.md · Source PDF: /var/lib/jarvis/personal/research/papers/thrasher-2017-forecasting-volatility-tsunami.pdf · Sentinel: vix-dispersion-check (4:05 PM ET)

Active Strategy Signals

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Library: finance/strategies/vix_library.json · Evaluator: finance/scripts/eval_strategies.py · Pipeline stage: strategy_eval (daily, depends on rv_compute + skew_capture + bubble_bounds) · API: /api/finance/strategies

VIX Spikes (Last 5 Years)

Short-End Term Structure & Contract Roll

Today: Spot & Near-Dated Futures

Spot + M1 + M2, including first contract past 60 DTE. Dot color = contract identity (shared with spread charts below).

M1 − Spot Spread (Front Month vs VIX)

M2 − Spot Spread (2nd Month vs VIX)

M1 − M2 Spread (Short-End Contango, Raw Points)

Raw vol points (M2 settle − M1 settle). M1/M2 identity rolls on the Wednesday exactly 14 calendar days before the front contract's expiry — earlier than the actual-expiry roll used in the M1/M2 vs Spot charts above.

Line color flips at each contract roll — segments are colored by which contract was the M1/M2 on that date, matching dot colors on the short-end curve above and the full term structure chart at top.

KEY RATES —

CURVE SNAPSHOT

Treasury constant-maturity par yields (FRED CMT), all 11 published nodes 1M–30Y. REAL overlay: TIPS par real yields (DFII5/10/20/30) — 4 nodes, 5–30Y. BEI overlay: breakeven inflation = Nominal − TIPS real at those 4 tenors (par−par approximation; small coupon residual vs zero-coupon Fisher). Rates 2 serves the Fed’s GSW zero-coupon fit where the exact Fisher identity holds.

BREAKEVEN INFLATION —

MACRO CONTEXT —

EQUITY REPO —

FedWatch Probability Table

Layer 1 — Policy Anchors

Target Range

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IORB

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ON RRP Rate

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Layer 2 — Market Reality

EFFR

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SOFR

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SOFR − EFFR

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cash↔collateral regime gauge

EFFR vs SOFR — 90d

Layer 3 — Plumbing Flows

ON RRP Balance

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Reserves

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TGA

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ON RRP Balance — 180d

Reserves & TGA — 365d

Source: FRED CSV endpoints (EFFR/SOFR/IORB/RRPONTSYAWARD/RRPONTSYD/DFEDTARU/DFEDTARL/WRESBAL/WTREGEN/DTB4WK/DTB3/DTB6). Refresh: 7AM ET weekdays. Framing: Conks · conks.plumbing.

2/10 YIELD CURVE — SPREAD, DIRECTION & REGIME —

2Y Yield

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10Y Yield

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2s10s Spread

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Δ1d —

3m10y Spread

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recession-predictor

Direction (5d)

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Δ5d — | Δ20d —

52w Percentile

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vs prior 252 sessions

Regime (4-quadrant)

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Inversions

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SPREAD HISTORY (2s10s bp)

RECENT LOG (last 20 sessions)

Date	2s10s (bp)	Δ1d	Δ5d	Direction	52w %ile	Regime	Inv?
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2s10s INVERSION EPISODES — RECESSION LEAD TIMES (1976–present)

Start	End	Days	Max Depth (bp)	Next Recession	Lead (mo)
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Source: FRED (DGS2/DGS5/DGS10/DGS3MO/T10Y2Y/T10Y3M) via finance/rates/ · 4-quadrant regime = 20-day Δlevel × Δslope · Direction = 5-day change (>2bp steep, <-2bp flat) · 52w %ile = where today's spread ranks vs prior 252 sessions · Gromen regime thesis deadline: 2026-06-30.

CARRY-TO-RISK RANKING — "SWEET SPOT" (12m horizon) —

Tenor	Carry (bp)	Roll-down (bp)	Total C+R (bp)	Breakeven (bp)	Carry-to-Risk
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Best risk-adjusted tenor highlighted. Breakeven = yield rise (bp) that wipes the cushion over the horizon. Funded carry uses SOFR (pre-2018: 3m bill). Static-curve roll-down approximation.

MARKET-IMPLIED FORWARD RATES

Forward	Rate (%)
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Annual-coupon zero bootstrap from par CMT yields. Forwards ≥ spot on an upward curve.

EXPECTED 12m EXCESS RETURN (CP vs Fama-Bliss)

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RICH / CHEAP — PCA-ON-LEVELS RESIDUAL z —

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z > +1 = CHEAP (yields more than the curve's own structure implies) · z < −1 = RICH · trailing-252d rolling fit.

BUTTERFLY RELATIVE VALUE — z (252d)

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fly = 2·belly − wings (bp). +z = belly cheap to wings (mean-reverting → fade); −z = rich.

Source: FRED CMT yields + SOFR via finance/rates/positioning.py (cron stage 4) · Carry/roll · forwards + Cochrane-Piazzesi/Fama-Bliss expected excess returns · PCA-on-levels rich/cheap + butterflies · Factor Lab L3 registration of these signals is DEFERRED (gated).

TREASURY SUPPLY / DEMAND — COMPOSITE SIGNAL —

supply_score

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>+0.5 cheapening · <−0.5 rich

Components (sign-aligned: + = cheapening pressure)

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SUPPLY SIGNAL HISTORY (month-end composite score)

RECENT NOMINAL-COUPON AUCTIONS (Note/Bond — tail signal)

Date	Tenor	High Yld %	Tail (bp)	Bid-to-Cover	Indirect %	Dealer %
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Tail = stop-out (high) − median accepted yield (WI-proxy), bp. Positive/large tail = weak demand → cheapening. High bid-to-cover & indirect (foreign) takedown = strong demand → richening.

QUARTERLY COUPON-ISSUANCE PROXY (Note+Bond, $bn)

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Source: TreasuryDirect auction API + raw-archive issuance via finance/rates/supply.py (cron stage 5) · Composite = equal-weight z[tail, −btc, −indirect, QoQ issuance] standardized on trailing-6mo means · Backtest verdict: OOS net Sharpe −0.127 — a slow risk-premium DESCRIPTOR, not a standalone strategy. Known limit: equal tenor pooling can mask long-end stress (duration-weighting deferred).

CROSS-ASSET CONFLUENCE — CURVE × BREADTH × CREDIT —

confluence_score

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Curve leg

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Breadth leg

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Credit leg

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CONFLUENCE_SCORE — HISTORY (full-leg days, red shading = risk-off)

BACKTEST LIFT — CONFLUENCE vs CURVE-ALONE (net of cost, walk-forward)

Window	Signal	Sharpe (net)	Hit rate	Max DD	t-stat	Ann ret (bp)	N
No data

P1 scoring — confluence_score = mean of available legs ∈[−1,+1]; conviction = high_risk_off (≤−0.66) / high_risk_on (≥+0.66) / mixed / low. Legs in {−1 risk_off · 0 neutral · +1 risk_on}. Source: finance/rates/confluence.py · curve=curve_regime slope+inversion override · breadth=SPX McClellan osc ±40 (universe.sqlite) · credit=HY OAS BAMLH0A0HYM2 rolling z-score vs 60d. Credit overlap starts 2023-06 (ICE licensing caps FRED OAS depth).

REAL RATES vs INFLATION EXPECTATIONS — ATTRIBUTION —

Nominal (10Y)

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Real (TIPS)

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Breakeven (Inflation Exp.)

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Today's Move (1d)

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Δnom — = Δreal — + Δbe —

1-Month Regime (20d)

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Δnom — = Δreal — + Δbe —

Equity Read (framework)

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primer lens, not a signal

NOMINAL · REAL · BREAKEVEN (10Y)

Single axis

From To

DRIVER MATRIX — ALL TENORS

Tenor	r* HLW	r* calc	Nominal	Real	Breakeven	Driver 1d	Regime 20d	Equity Read

Source: finance/rates/attribution.py → rate_attribution. Identity nominal = real + breakeven (breakeven = nominal − real, cross-checked vs FRED T5YIE/T10YIE). Driver: real-driven |Δreal|≥2·|Δbe|, breakeven-driven |Δbe|≥2·|Δreal|, else mixed; quiet if |Δnom|<3bp. Real yields are TIPS CMT (FRED floors at 5Y — no 2Y TIPS point). Equity read is the Capital Flows primer lens — rising real rates = restrictive headwind for multiples, falling = easing tailwind — not a trade signal.

Signal Backtest Harness —

⚠ METHODOLOGY — read before trusting these numbers

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Signal	Sharpe full	Sharpe OOS net ★	Hit-rate OOS	Max-DD	Turnover /mo	n OOS	OOS start
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Net equity curves (OOS window, multiplicative bp index, base 1.0)

Source: Capital Flows STIR Replication Playbook · Live data — Yahoo chart API (SR3 + ZQ futures) + NY Fed EFFR/SOFR CSV + FRED (DGS2/5/10/30, T5YIE/T10YIE, PSAVERT). Refresh: weekdays 16:30 ET.

Treasury Curve — Nominal · Real · Breakeven

GSW ZERO-COUPON —

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CURVE SNAPSHOT

Zero-coupon yields fitted by the Fed (Svensson). Nominal − Real = Breakeven holds exactly on this basis — it does not on the par basis, which carries a coupon residual. Nominal is fitted 1–30Y; the Fed fits TIPS only 2–20Y, so the real and breakeven curves end short by construction, not by data loss. FWD overlay adds instantaneous forward curves (SVENF/TIPSF/BKEVENF) from the same model.

HISTORY Tenor:

SUPPLY & HOLDINGS

FED HOLDINGS — TREASURIES

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MoM —

— Details →

TOTAL US TREASURY DEBT

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MoM —

— Details →

FED MATURITY CONCENTRATION

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— —

Peak bucket — Fed % of total outstanding

NY Fed SOMA · ex-Bills Details →

DOCUMENTATION & METHODOLOGY

PRIMARY SOURCES

📄 GSW: U.S. Treasury Yield Curve (FEDS 2006-28)

Gürkaynak, Sack & Wright (2006). Svensson-fit nominal zero-coupon curve, 1961–present. Source of all SVENY/BETA/TAU parameters and the nominal fitted yields shown here.

Federal Reserve FEDS · Nominal curve · Zero-coupon basis

📄 GSW TIPS Extension (FEDS 2008-05)

Gürkaynak, Sack & Wright (2008). Svensson-fit TIPS real zero-coupon curve using the same framework. Source of TIPSY/TIPSF columns. Fitted 2–20Y by construction.

Federal Reserve FEDS · TIPS real curve · Zero-coupon basis

📊 Kim-Wright Term Premium (FEDS 2005-33)

Kim & Wright (2005). 3-factor affine model decomposing nominal yields into rate expectations + term premium. Source of THREEFYTP10 (10Y TP). Updated weekly via FRED.

Federal Reserve FEDS · Term premium model · FRED: THREEFYTP10

KNOWLEDGE BASE

📋 Treasury Par Curve Methodology

HS (Hermite spline, pre-2021-12-06) vs MC (Monotone Convex, current). Par basis vs zero-coupon basis. 14 tenors, 1Mo–30Yr. MC uses secondary market prices, not yields, as primary input.

KB · knowledge/finance/treasury-methodology.md

📋 Treasury Data Source & Provenance

Canonical CSV endpoint, publication time (~17:30 ET same-day), schema breaks (MC/HS transition), series-level provenance, supersession vs revision distinctions.

KB · knowledge/finance/treasury/SOURCE.md

DESIGN & IMPLEMENTATION

📄 Data Hygiene — Immutable Historical Design

Frozen design doc (JDEC-20260817-011). Provenance schema, write-behavior rules, archive blob structure, INSERT-OR-IGNORE vs UPDATE partition, rollback strategy.

plans/data-hygiene-immutable-historical.md · FROZEN

📄 Data Hygiene — Implementation Status

Phase 0–3, 5 complete. Phase 4 (archive dedup) gated on loop #1981. Open: #1980 (fred_revisions PK), #1990 (DDL renames), #1902 (real-time feed). Verification queries included.

plans/data-hygiene-rates-implementation.md · Live

📄 Formula Verification Audit (2026-08-18)

Audited against live data: GLD gold constant corrected (factor rises, not falls), Kim-Wright confirmed as zero-coupon yields (not forwards), inversion depth unit bug fixed. Two expert findings reversed.

plans/rates-formula-verification.md · 2026-08-18

📄 Two-Tier Data Model Design

Provisional real-time lane (Treasury.gov same-day) alongside immutable historical record (FRED T+1). Supersession vs revision distinction. Gated on loop #1937 (Rob review).

plans/rates-two-tier-data-model.md · Design draft

Synthetic ATM Straddle — Bloomberg Methodology

Underlying: Spot ($): IV (%): Rate (%): Div (%): DTE:

Options Chain Snapshot

Underlying:

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Put/Call Ratio & Volume

IV Overview

Open Interest by Strike

Data Sources

Active: Polygon.io (via Massive MCP) — chains, historical OHLC, contract reference

Available: Theta Data ($80/mo — tick-level + Greeks), ORATS ($99/mo — backtester), DeltaNeutral ($575/yr — 20yr EOD)

Libraries: py_vollib (pricing/Greeks), Optopsy (backtesting), polygon-api-client

Full reference: /var/lib/jarvis/personal/finance/research/options/OPTIONS_DATA_SOURCES.md

Paper Trading Account

Open Positions

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Submit Order

Symbol: Qty: Side: Type:

Recent Orders

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Equity Curve

Kalman Pairs Signal — TLT/IEI

Status

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Rolling Sharpe vs OOS Benchmark (0.670)

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Paper Equity Curve

Paper equity from Alpaca account (strategy + cash). Phase 3 tracking started 2026-06-26. Graduation gate: 8-week rolling Sharpe > 0.50.

Intraday 1-Min Bars

Symbol: Custom: Date:

Stored Data Summary

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Intraday Breadth — 9 Indices, 2 Methodologies

Date:

A = probe vs yesterday's adj close · B = vs prior 5-min bar · Auto-refresh 60s · raw JSON

Live Signal Monitor

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Pareto Frontier — Non-Dominated Solutions

Full Rankings (Top 20)

Signal Changes

No changes detected

Improvement History

Risk Monitor — Correlation & Volatility

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Post-2010 calibration only. Gate G2: 8/16 = 50% on full 1957-2026 precedent set. The 4 live combos (C2/C4/C6/C10) have demonstrated efficacy in recent regime. Use with care — blocked combos (C1/C3/C5/C7/C8/C9/C11) excluded from production.

Methodology & Sources

Internal docs

Plan: berg-atomic-indicators.md Indicator catalog (30k+ → 11 combos) Combos source (berg_combos.py) Atoms source (berg_atoms.py) Gate G2 harness

External (Berg's own material)

Shapiro/Berg interview (YouTube) @bergmilton (Twitter/X) miltonbergedge.com (retail $10/mo) miltonberg.com (institutional)

Today's Signal Status

Combo Charts (click to view price + fire markers)

C2 chart → C4 chart → C6 chart → C10 chart →

Historical Fires

Suggested Stop (live signals)

Calculated from signal close minus (historical max_dd + 0.5% buffer). Only shown for signals where max_dd_60d is available.

Forensic Accounting Screener

⚠ Threshold Breaches (literature thresholds — dashboard-only)

Index

Four deterministic forensic scores per 10-K (annual): Beneish M (earnings manipulation likelihood), Altman Z (bankruptcy proximity), Sloan accruals (earnings quality), Piotroski F (financial strength). Source: SEC EDGAR via edgartools; computed nightly. Literature thresholds — Beneish M>-1.78 = aggressive; Altman Z<1.81 = distress; Sloan >+0.10 = high accruals; Piotroski ≤2 = weak. Plan: forensic-scores-nightly.md.

Latest 10-K Scores

Flagged only

Ticker	Period	Beneish M	Altman Z	Sloan	Piotroski F	Quality

Ticker

Enter a ticker to see all scored filings.

Plan & Lessons

Methodology — all 4 scores, formulas, thresholds, caveats (view inline)
Deep dive: NVDA — compound red explained as real-demand boom, not manipulation
Deep dive: DELL — post-LBO Z-recovery + FY26 DSRI=1.44 receivables flag
Deep dive: STX — storage cycle textbook; FY23 trough vs FY25 recovery DSRI=1.61
Deep dive: SATS — Z=−0.78, F=2 — legitimate distress validation case
CSCO 2000 vs NVDA 2026 — actual data pull; corrects the Cisco analog (Sloan was −14.5%, not positive)
Deep dive: LU 1999 — Sloan +16.3% 🔴 — vendor-financing fraud is the analog NVDA's positive Sloan echoes
Deep dive: SUNW 2000-2002 — clean-books demand crash; the negative control to Lucent
forensic-scores-nightly.md — approved plan, D1-D4 resolved (view inline)
Source thread (2026-06-02): @gemchanger — "Read Any Company Like an Analyst"
Migration 011 — DB schema (view inline)
Python package: finance/forensics/ (edgar_fetcher / beneish / altman / sloan / piotroski / screener)
References: Beneish (1999), Altman (1968), Sloan (1996), Piotroski (2000)

Recent Screener Runs

Earnings Tracker

This Week’s Earnings

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Historical Reaction Profile

Enter a ticker above to see historical earnings reaction data.

Hypothesis Matrix — All Tickers

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Phase Tracker

Status	Phase	Scope	Completed
✓ DONE	Phase 0	Placeholder tab + open questions captured for Rob	2026-07-23
✓ DONE	Spec	7/7 design questions locked (JDEC-20260803-010 through -018); vintage-data principle + dedup falsification	2026-08-03
✓ DONE	Phase 1	Data pipeline: schema (migration 047), EDGAR 8-K fetcher, reaction computer (1D/5D/20D), RSS real-time poller + daily sweep cron (A+C hybrid, JDEC-20260803-016)	2026-08-03
✓ DONE	Phase 2	Calendar UI: this-week earnings table (ticker, timing BMO/AMC, EPS actual, 1D/5D/20D reactions); live from earnings_actuals + earnings_calendar via API	2026-08-03
✓ DONE	Phase 3	Reaction profile: per-ticker historical EPS prints + 1D/5D/20D returns; quintile bucket engine (≥5 prints); ticker lookup UI	2026-08-03
✓ DONE	Phase 4	Hypothesis matrix: 5-bucket EPS-surprise × 3-horizon aggregate table; per-bucket ticker list; auto-refines as pipeline populates data	2026-08-03

Open Questions — answer to unblock the build

Click any question to expand it. Supporting context inside each one is drawn from our existing data sources, conventions, and trade-offs — everything you need to make the call. Resume on Telegram when ready; we'll walk these one at a time.

1. Companies scope ✓ ANSWERED — FULL UNIVERSE▼

✓ Rob, 2026-08-03 — FULL UNIVERSE. Verbatim: “track every ticker that has earnings… all tickers, all symbols and all earnings tracked and recorded.” Equities not currently in a portfolio or watchlist need to be built out — universe expansion is in scope, not a side effect. None of the four options below apply; they are kept for provenance. Decision JDEC-20260803-010.

⚠ Gap: research.db holds only 3,295 distinct tickers vs ~11k US listed; factor_universe is 0 rows. Open loop #766.

Which set of companies should this page track? A specific hand-picked watchlist, or auto-derive from an existing dashboard/portfolio universe?

Why it matters: Every other design decision (data volume, refresh cost, display density) scales with this number. A 20-name focused watchlist is very different from "all names in the Factor Lab universe."

Options

Hand-pickedYou maintain a curated list (e.g. 20–50 names you actively follow). Simplest; lowest data cost; nothing slips in accidentally.

Portfolio-derivedAuto-pull from whatever is currently in the Alpaca portfolio + watchlist. Always in sync; zero manual maintenance.

HybridHand-picked core + auto-supplement from portfolio. Recommended if the watchlist and live positions overlap but aren't identical.

Index-basedAll NDX-100 or S&P 500 names — broadest coverage; highest data volume; less focused.

What we have today: Full R3K universe (2,929 tickers) in research.db. Alpaca portfolio positions live via API. NDX-100 and SPX constituent lists available. A hand-picked watchlist would need a new small config file (e.g. data/earnings-watchlist.json).

2. Earnings data source ✓ ANSWERED — EDGAR-FIRST▼

✓ Rob, 2026-08-03 — EDGAR-first + multi-source corroboration. Verbatim: “start with EDGAR first for earnings… also look for other public announcements. Find the most up-to-date sources including news or places such as Bloomberg.com or Google Finance, Yahoo Finance, etc. Capture the data as soon as possible and also make sure that it’s verified and correct.” Two requirements beyond source choice: (a) latency — capture ASAP; (b) verification — cross-source confirmation. Consensus estimates DEFERRED (paid at full-universe scale). Decision JDEC-20260803-011.

Build to the standing data principles, not a bespoke scheme: sources independent (own fetcher + raw archive each, reconcile on backend, missing is missing); provenance on every row (source + fetched_at + raw_path, corrections supersede never overwrite); batched into shared windows. Reuse the wired EDGAR client finance/forensics/edgar_fetcher.py — do not build a second one.

⚠ The “Recommended path” below is FALSIFIED. Probed live 2026-08-03 — it was written from vendor docs, never tested against our key: /benzinga/v1/earnings 403, /tmx/v1/corporate-events 403, /stocks/financials/v1/income-statements 403. Only /v3/reference/tickers works, and the tier 429s after ~5 rapid calls (~5 req/min). Full-universe scope kills any per-ticker source. Kept below for provenance. Lesson: lesson_spec_recommended_source_is_untested_hypothesis.

Where do the consensus EPS estimate, estimate range (low/high), and the report date/time come from? How current does it need to be — real-time, daily refresh, or manual?

Why it matters: The data source determines what's free vs paid, how fresh the estimates are, and how much maintenance it takes to keep them live. Stale estimates make the calendar misleading.

Options

yfinanceFree, automated. ticker.calendar returns next earnings date + EPS estimates. ticker.earnings_dates gives historical prints. Works today with no new keys. Risk: Yahoo changes its internal API without notice (broken several times in the past 3 years).

Polygon/MassiveAlready wired. /vX/reference/financials provides historical EPS actuals but not forward consensus estimates on our free/starter tier. Good for backtesting past surprises; can't source next-quarter estimates.

Bloomberg CSVInbox at /var/lib/jarvis/inbox/bloomberg/ — Rob drops a CSV, Jarvis ingests it. Highest quality; fully manual; no automation possible without a paid Bloomberg API.

Manual entryRob types the key numbers into a config file or dashboard UI before each earnings week. Lowest dependency; highest friction.

Recommended path: yfinance for schedule + estimates (daily cron), Polygon for historical actual EPS (to backtest surprises). yfinance covers the estimate-range question out of the box (earningsLow, earningsHigh, earningsAverage, analyst count). Manual Bloomberg drop as an override path if yfinance data looks wrong.

3. Price-reaction hypothesis basis ✓ ANSWERED — DATA-DRIVEN▼

✓ Rob, 2026-08-03 — DATA-DRIVEN with iterative refinement. Verbatim: “It needs to be primarily data driven. We will need to research and build models for expectations, basically reaction functions in which we expect the price to do a specific thing tied to what the earnings number is. Then we can test the hypothesis against reality to improve over time.” Method: for each ticker, backtest historical EPS actuals (EDGAR) against next-day/next-week price returns, compute empirical reaction per earnings tier, surface the baseline to Rob for annotation and refinement. Iterative: patterns confirm or refute as new prints land. Decision JDEC-20260803-012.

What we have today: Full daily price history in research.db. EDGAR 8-K filings already wired via edgar_fetcher.py — historical EPS actuals parseable from Item 2.02 exhibits. The backtest engine is ~200 lines of Python given existing infrastructure. Polygon /vX/reference/financials available as a cross-check for large-caps.

4. Earnings surprise buckets ✓ ANSWERED — FIVE-LEVEL▼

✓ Rob, 2026-08-03 — FIVE-LEVEL bucketing. Verbatim: “We also need ways to quantify things such as possibly a five-level system much worse than expected, slightly worse than expected in line with expectations, slightly better than expected, much better than expected.” Buckets (mapped to YoY EPS growth): 1. Much worse (<−15% YoY), 2. Slightly worse (−15% to −5%), 3. In-line (−5% to +5%), 4. Slightly better (+5% to +15%), 5. Much better (>+15% YoY). These become the rows of the hypothesis matrix; price reaction is computed per bucket per horizon (Q5). Decision JDEC-20260803-013.

Data available: EDGAR actuals going back 10+ years for most issuers via edgar_fetcher.py. YoY computation is a single join on (ticker, fiscal_quarter). research.db has 3,295 tickers with daily price history for backtest correlation. Threshold refinement via iteration as more prints land.

5. Time horizons ✓ ANSWERED — 1D + 5D + 20D▼

✓ Rob, 2026-08-03 — 1D + 5D + 20D. Immediate reaction (1D), short-term follow-through (5D), and one-month reversion check (20D). These become the columns of the hypothesis matrix; rows are the five earnings buckets from Q4. No new data fetch required — daily closes in research.db cover all three horizons. Decision JDEC-20260803-014.

6. Pre-market / after-close timing▼

Does the reaction measurement account for when a company reports? "Before open" (BMO) vs "after close" (AMC) changes what "1-day reaction" even means.

Why it matters: If NVDA reports after close on Tuesday, the first chance the regular-hours market can react is Wednesday's open. Measuring from Tuesday's close to Wednesday's close is 1D. But if AAPL reports before open on Wednesday, the market reacts at Wednesday's 9:30 open and you measure Wednesday's close vs Tuesday's close — same calendar-day but different "event day 0" logic.

Options

Event-day alignedRecommended. Define "event day 0" as the first trading session where the market can react: for AMC reports that's the next calendar day; for BMO that's the report day itself. All returns measured from that session's open (or prior close). Consistent across the universe.

Calendar-day naiveAlways measure from the date the report is filed, regardless of time. Simple but mixes AMC/BMO effects — an AMC report's "1D" return would capture only 1 actual trading session of reaction, not 1 full day of reaction.

Display only, no backtestShow the report time on the calendar as context, but don't incorporate timing into the hypothesis math — use calendar-day naive for the backtest and note the caveat. Faster to build; less rigorous.

Data available: Report time (BMO/AMC/unconfirmed) is typically included in earnings-calendar feeds (yfinance ticker.calendar, Polygon earnings events). We can capture and store it alongside the date. Aligning the event-day baseline adds ~10 lines to the backtest logic.

7. Data maintenance cadence▼

Who keeps the earnings schedule + consensus estimates fresh, and how often? This decides whether the page is a living dashboard or a manual one.

Why it matters: Earnings dates shift (companies reschedule), estimates move as analysts update throughout the quarter, and whisper numbers diverge from consensus in the days before the print. A stale calendar is actively misleading.

Options

Automated dailySentinel cron (e.g. nightly at 11 PM ET) re-fetches schedule + estimates for the next 2 weeks via yfinance. Zero manual work after setup. Handles date changes and estimate revisions automatically. Risk: yfinance reliability (see Q2).

Weekly on SundayPull the upcoming week's calendar every Sunday morning. Less overhead than daily; fine if estimates don't move much intra-week for the names you follow. One missed Sunday = stale week.

On-demand (manual trigger)Rob sends a command or clicks a refresh button before each earnings week. Highest reliability (you decide when to pull); most friction.

Manual CSVRob drops a CSV into the inbox before each earnings week. Same pattern as Bloomberg ingestion. Simple, offline-capable; entirely manual.

Our automation patterns: We already run ~40 sentinel crons. Adding an earnings-calendar-refresh cron is a 1-hour build. The nightly universe refresh and breadth pipelines show this pattern works reliably. Recommended: automated daily at 11 PM ET, with a manual override button on the dashboard to force an immediate refresh.

Damodaran Valuation Integration

Valuation Surface — Live

Decision surface: cross-section Screener → Per-Ticker Valuation → Industry / Country browsers → EV-Premium tracker. Nightly RUA run (02:30 ET): WACC 2,929/2,929, DCF ~88 (Financials/REITs excluded correctly since Approach A; was 121 pre-guards), multiples 171 (gated by XBRL backfill). ✓ DCF input guards + XBRL unit-writer fix shipped 2026-07-13 (sector-aware growth cap, WACC floor, FCF scale guard, Financials/REIT exclusion; XBRL unit column corrected). Screener shows clean / suspect / flagged tiers. Remaining: XBRL value-scale errors on systematic wrong-scale filers (AEP, CALM — unit now correct, value still wrong for this subset).

Aswath Damodaran's intrinsic-valuation toolkit applied across the full equity universe: DCF (four value drivers: growth, margins, reinvestment, discount rate), WACC bottom-up from industry betas, implied ERP, relative multiples with peer z-scores, country risk premium. Source: NYU Stern annual dataset release.

Domain boundary: the existing Forensic tab answers "can I trust the numbers?" — this tab will answer "given the numbers, what is it worth?". Forensics is a quality gate; valuation is a decision input. Orthogonal domains sharing the EDGAR source data.

Phase Tracker

Status	Phase	Scope	Completed
✓ DONE	Phase 0	Memory reference + plan + placeholder tab	2026-06-04
✓ DONE	Phase 1	Static datasets ingest — 4 tables in `research.db` (industry metrics 94, country risk 178, ERP history 66, asset returns 98); sentinel cron `damodaran-datasets-annual` Jan 15 09:00 ET	2026-06-04
✓ DONE	Phase 1.5	Historical archive ingest + verification harness — 17 vintages (2008-2024) backfilled (1,439 industry rows, 2,271 country rows). Ticker-industry map from `indname.xls` (48k rows). NDX-100 reconciliation runs end-to-end — 49 rows, 37% within ±0.15 (method gap, no data corruption). Gate partially passed — proceed cautiously to Phase 2 with diff disclosures.	2026-06-05
✓ DONE	Phase 2a	DCF + WACC engine (NDX-100 baseline) — `finance/damodaran/dcf.py`; migration 023 `valuation_snapshots`/`valuation_runs`; reusable writer `finance/common/snapshot_log.py`. NDX-100 first run: WACC 93/97, DCF 2/97 — DCF gap = xbrl_concepts coverage (only 14/97 NDX tickers cached). Backfill blocker for Phase 2a′.	2026-06-05
✓ DONE	Phase 2a′	R3K expansion — WACC 2900/2900 (100%); DCF 84/2900 (2.9%) gated by edgartools XBRL backfill (no_xbrl=2347/83.3%). 2816 gaps in `valuation_gaps` classified by root cause: no_xbrl/no_ocf/no_shares/financial_sector/reit/holdco/tracking_stock.	2026-06-06
✓ DONE	Phase 2b	Relative multiples + peer z-scores — migration 032 `damodaran_industry_multiples` (94 industries × 24 cols: P/E + EV + Sales + Book/IC); `finance/damodaran/multiples.py` (model `multiples_v1`) computes trailing_pe, ev_ebitda, p_s, p_b, ev_ic per ticker + winsorized peer Z-scores across same-industry run peers (min 5). EV metrics suppressed for Banks/Insurance/REITs (Damodaran convention). Gaps logged uniform with DCF.	2026-06-06
✓ DONE	Phase 3	Sensitivity tables + ERP overlay — 5×5 WACC×growth grid per ticker; ERP regime panel (66-year history, percentile, sparkline); backfilled 62 existing DCF rows; API: `/api/finance/damodaran/erp` + `/api/finance/valuation/sensitivity-grid`	2026-06-06
✓ DONE	Phase 4	Real Valuation UI — per-ticker DCF card + industry/country browsers (2026-06-06); cross-section Screener added 2026-07-06 with plausibility quarantine	2026-06-06
✓ DONE	Phase 7	EV Premium / Discount Tracker — market-vs-Damodaran premium per ticker, rolled to sector + index; nightly cron @ 03:30 ET	2026-06-07
✓ MVP	Phase 5	Damodaran VEP — Blogspot RSS wired into expert-content-watcher; YouTube/backfill deferred (P3)	2026-06-06
✓ DONE 2026-07-13	DCF integrity (Approach A)	Sector-aware growth cap (Utilities=5%, Financials=8%, default=10%), WACC floor rf+50bps, WACC−g ≥ 150bps guard, shares magnitude-continuity check, FCF scale guard. Screener now tracks clean / suspect / flagged tiers (G1, live counts above). Financials/REITs (banks — FCF-DCF inapplicable) now gap explicitly instead of ranking garbage (ASB was the #1 result). T1 synthetic tests 15/15. Pending (Approach B): XBRL unit fix for systematic wrong-scale filings (AEP, CALM). Commits `ab1b0d0b`·`05f1d3cc`	2026-07-13
☐ PLAN	Phase 6	Narrative-to-numbers tool (P3) — thesis → cash-flow projection → implied price	—

Valuation Screener — cross-section /api/finance/valuation/screen

show flagged regime …

⚠ Directional only — not a buy list. Input guards shipped 2026-07-13 (sector-aware growth caps, WACC floor, shares continuity check, FCF scale guard). Three tiers: clean (computed without adjustments) · suspect (passed plausibility but a growth cap or spread floor was applied — treat as directional) · flagged (gross outliers, hidden by default). Remaining: XBRL value-scale errors on systematic wrong-scale filers (e.g. AEP, CALM) — unit column corrected 2026-07-13 (Approach B, 4f0ea571); value itself still wrong for this subset and will appear as suspect/flagged in the screener.

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Ranks the latest nightly RUA valuation run by DCF margin-of-safety = (intrinsic − price)/price. Click any row to open it in the Per-Ticker card below. Coverage: live counts shown above; ~88 names have DCF intrinsic as of 2026-07-14 (Financials/REITs gap correctly since Approach A; was 121 pre-exclusion). WACC covers 2,929/2,929 names.

DIAGNOSTICS & PROVENANCE — beta reconciliation, per-run DCF/WACC dumps, ERP history. Foundation checks, not stock-picking tools.

Reconciliation — damodaran_reconciliation

Our median trailing-2y daily-return beta vs Damodaran's published cross-section. Universe: NDX-100 (N≥3 constituents per industry). Tolerance ±0.15.

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Industry	N	Vintage	Ours β	Theirs β	Δ	±0.15
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Method: finance/damodaran/reconcile_report_2026-06-04.md · Code: finance/damodaran/verify.py · Triage: 31 misses are method gaps (simple-median vs Damodaran's cap-weighted bottom-up), 0 are data corruption. Tighten by widening universe to R3K and switching to cap-weighted aggregation (Phase 1.5b, deferred).

DCF & WACC — valuation_snapshots (Phase 2a)

5y explicit + Gordon terminal. WACC from industry weights + ticker-level beta vs SPY. Δβ = ours − Damodaran industry β. Universe: NDX-100, latest run.

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Ticker	Industry	WACC	β	Δβ	Value/share	TV %
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Code: finance/damodaran/dcf.py · Writer: finance/common/snapshot_log.py · DCF cells empty = xbrl_concepts coverage gap (FCF inputs not cached for that ticker; Phase 2a′ backfill blocker). Run a fresh baseline: finance/venv/bin/python -m finance.damodaran.dcf --universe NDX-100 --as-of YYYY-MM-DD.

ERP Overlay — Equity Risk Premium

Damodaran implied ERP (DCF on S&P 500 cash flows) · 1960–present · Regime: percentile vs history

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Current ERP

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Riskfree (T-Bond)

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Expected Return

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Percentile (1960–)

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Historical Median

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Year	ERP (FCFE)	T-Bond Rate	Exp. Return	S&P 500	Regime
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Source: damodaran_erp_history (Damodaran NYU Stern annual release) · Method: DCF/IRR on S&P 500 dividends + buybacks. Regime bands: <P25 = LOW (expensive), P25–P75 = NORMAL, >P75 = HIGH (cheap). ERP used as discount-rate input for all DCF valuations via finance/damodaran/dcf.py → cost_of_equity = rf + β × ERP.

Sensitivity Grid — 5×5 WACC × Growth

Per-ticker DCF value/share across (−200 → +200 bps) WACC × growth offsets. Base case highlighted. Color = relative value (green = higher, red = lower).

Ticker

Enter a ticker and click Load Grid.

Grid cells = value/share ($). Rows = WACC offset (top = WACC −200 bps, bottom = +200 bps). Cols = growth offset (left = growth −200 bps, right = +200 bps). Base case (WACC offset=0, growth offset=0) is center cell, outlined in blue. Heatmap: color scaled within the 25-row grid; cells >P75 green, <P25 red, mid yellow. API: GET /api/finance/valuation/sensitivity-grid?ticker=AAPL

Per-Ticker Valuation — /api/finance/valuation/ticker

Composes DCF + WACC + multiples + ERP regime + forensic status. Click any row to drill in; intrinsic value is DCF-derived (5y explicit + Gordon TV), MoS is (intrinsic - market) / market.

Industry Snapshot — damodaran_industry_metrics × damodaran_industry_multiples

Default sort: cost_of_capital DESC (highest discount-rate industries first). Click headers to sort. Vintage shown is the latest Damodaran release.

Country Risk — damodaran_country_risk

Default sort: total_erp DESC (highest country-risk first). Country risk premium = base ERP + default-spread × risk multiplier. Apply when valuing non-US ops.

Plan & Memory

plans/damodaran-valuation-integration.md — locked 6-phase plan, §7 decisions, §8 execution order
memory/reference_damodaran_valuation_framework.md — canonical reference (DCF, ERP, country risk, narrative+numbers, lifecycle, sources)
memory/reference_damodaran_erp_2026.md — start-of-2026 ERP data point (4.23% at riskfree 4.18%; expected stock return 8.41%)
memory/MEMORY_finance_index.md — satellite index pointer

NYU Stern Datasets

EV Premium / Discount Tracker — Market vs Damodaran intrinsic

Daily Market Cap and Market EV vs Damodaran-implied intrinsic EV/equity, rolled up to GICS sector and index (SPX/NDX/RUI/RUT/RUA). Positive = market trades at premium; negative = discount. Bridge: market_ev = market_cap + total_debt + preferred + minority + leases - cash. APIs: /api/finance/valuation/ev-premium/{aggregate,snapshot,ticker}. Coverage % surfaces the XBRL backfill gap as a first-class signal — rises as ticker coverage expands.

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Sector Heatmap — latest

Sector	N	Cov	$T MC	EV vs Damo	MC vs Damo	EV vs Peer
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Per-Ticker Premium — /api/finance/valuation/ev-premium/ticker

Phase 7 shipped 2026-06-07 — EV premium tracker live. Daily Market-vs-Damodaran premium %s per ticker, rolled up to sector and index. Nightly cron valuation-ev-premium-daily @ 03:30 ET (4-stage: bridge → market_ev → ev_premium → aggregate). APIs: /api/finance/valuation/ev-premium/{aggregate,snapshot,ticker}. Coverage today: SPX 88/503 with market_ev, 2/503 with Damodaran intrinsic; expands with XBRL backfill. Phase 4 shipped 2026-06-06 — valuation-snapshots-nightly wired (DCF + WACC + multiples across R3K @ 02:30 ET). Next: Phase 5 (Damodaran VEP — blog/Substack/YouTube ingest).

ETF Trend Rotation

Momentum + trend-strength rotation across 12 ETFs (GLD, AGG, SPY, QQQ, TLT, UUP, VXX, SHY, VONV, VONG, VTV, VUG). Signal: 12-1 risk-adjusted momentum (skip last month) × trend filter (SMA-200). Top 4 = BUY. VXX excluded. Runner: python -m finance.scripts.run_etf_rotation

▲ BUY Signals

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↻ Last Rebalance

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Full Rankings —

Rank	Ticker	Signal	Composite	Momentum 12-1	Trend	Vol 21d	Ret 1m	Ret 3m	Ret 6m	Ret 12m	Price	vs SMA200

BUY-Set Change History (last 30 days)

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Regime Call Accuracy SPY fwd return · risk-on→+ret correct · risk-off→−ret correct

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Date	Flip	SPY	5d ret	5d ✓	10d ret	10d ✓	20d ret	20d ✓	Src

Permutation Entropy (Bandt-Pompe)

Per-ticker complexity classifier — window=63 trading bars on adjusted close. Low PE → structured / trend-friendly. High PE → near-random / mean-reversion regime. Source: finance/indicators/entropy.py · gemchanger (@gemchange_ltd) ~2026-06-02 · Bandt & Pompe (2002).

Ticker

Most structured (low PE·dim3)

Most random (high PE·dim3)

Factor Lab — Layer 4 Walk-Forward Leaderboard

Walk-forward cross-sectional IC + Sharpe for all Layer 3 factors (monthly eval dates, 12-month forward return). Regime split (mig 039): risk-on = n_buy ≥ 3 AND equity ETF in BUY, recomputed from prices_daily full history. NULL = bucket < 6 eval dates or < 4 months. Δ IC = risk-on minus risk-off (positive = factor stronger in risk-on). Regime columns fill as risk-off eval dates accumulate — risk-off periods are sparse so columns show — until ≥6 risk-off dates exist per metric. Runner: python -m finance.factor_lab.validate

View:

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Metric	Dir	Gate	IC Mean	IC t	Sharpe	N	IC risk-on	IC risk-off	Δ IC	Sh risk-on	Sh risk-off	N on	N off
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ETF Factor Exposures — Holdings-Weighted

Holdings-weighted average factor score per ETF, built from SEC NPORT-P filings (fund_holdings) joined to metric_values on resolved ticker. Exposure = Σ(weight × score) / Σ(weight) over covered holdings. Cov% = fraction of ETF weight with a metric value. Top-3 dominant factors shown per ETF (by |exposure|). Large-cap blends with extreme factor tilt = crowding risk.

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Leveraged-Pair Options — IV / Strategy Tracker

Daily EOD log of implied vol, realized vol and the IV−RV spread (the real edge) for the top leveraged pair (SOXL/SOXS), plus paper-tracked option structures marked-to-market so the data picks the winner. The edge is the variance risk premium (IV > RV) + IV mean-reversion, NOT the mechanical decay (~2.8% net CAGR — gross 9·σ² is a fantasy ceiling). Source: yfinance EOD chains (Massive options feed is not entitled on our key). Observational + paper-track only — no alerts, no orders (below the Factor-Lab L3 gate); defined-risk only (Rob's Rollover IRA). IV Rank accrues from 2026-06-22 (yfinance gives only current IV; RV-rank shown as a labelled day-one proxy).

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Current snapshot

Leg	Spot	%Chg	ATM IV (front)	ATM IV (next)	RV 20d	IV−RV	IVR	RV-rank*	Exp. move
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IV / RV history

Paper-tracked structures — marked-to-market

Structure	DTE	Credit/Debit	Max loss	P&L	P&L / risk
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Leveraged 3× Pairs — Decay-Harvest Tracker

Strategy: short both legs of each 3× bull/bear pair, dollar-neutral, held with periodic rebalance — harvests daily-rebalance volatility decay (regime-agnostic; not mean-reversion). GROSS decay is a theoretical ceiling; NET CAGR is post-borrow and after rebalance + trend drawdowns — the gap is large and real. Thin legs (⚠ below the $20M liquidity floor) are likely hard-to-borrow, so realized net is worse still. Re-ranked daily from prices_daily (Bloomberg history + yfinance daily). Observational only — no alerts, no trading.

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Bubble Composite Monitor

Five-signal qualitative+quantitative composite anchored around the same bubble cluster: Michael Green passive-flow thesis · Scott Goodwin / Diameter Capital BDC credit risk · Ed Zitron AI no-business-model · BAB spread high-beta crowding proxy (flow) · BAB z-score crowding level (SPLV/SPHB ratio vs 1Y) · Thrasher VIX compression. Traffic light: 0–1 active = 🟢 LOW · 2 = 🟡 ELEVATED · 3 = 🟠 HIGH · ≥4 = 🔴 EXTREME.

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⬜ BAB Spread SPLV − SPHB

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⬜ VIX Dispersion Thrasher 2017

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⬜ BDC Credit Stress ARCC vs SPY

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⬜ AI No-Biz-Model Thesis Zitron / Goodwin

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⬜ BAB Z-Score SPLV/SPHB ratio vs 252d — crowding level

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⬜

Howell G3 Liquidity × Bubble Confluence cross-system trigger

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Integration hooks · BAB spread (flow) feeds ETF Rotation regime gate (risk-on = SPHB > SPLV) · BAB z-score (level) corroborates SpaceX/NDX QQQ entry thesis — high crowding = late cycle · BDC stress threshold (−5%) wires to Goodwin thesis trigger · Zitron flag is manual — edit /var/lib/jarvis/data/macro/zitron-flag.json to suppress · VIX dispersion: sentinel vix-dispersion-daily refreshes at 4:15 PM ET · Howell confluence: sentinel bubble-howell-confluence-daily at 6:30 PM ET weekdays (after rates-complex + global_liquidity)

📚 The Shelf

Each page: Overview · Core Theories · Formulas & Metrics · Key Ideas · Application to Jarvis · Known Issues · Sources

Where the real math is — prospect theory (λ ≈ 2.25) → Thinking, Fast and Slow · Brier score + Murphy decomposition → Superforecasting · power-law tail estimation (Clauset–Shalizi–Newman) → Ubiquity · allometric scaling (Kleiber ¾, urban β ≈ 0.85 / 1.15) → Scale · fat-tail moment conditions → Fooled by Randomness · valuation mechanics → Margin of Safety · capital-cycle factors → Capital Returns

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Corpus: knowledge/finance/books/ · seeded from Mauboussin's recommended reading · open master index · schema

📐 McCullough / Hedgeye — GIP Framework

@KeithMcCullough · Hedgeye Risk Management · updated 2026-08-28

Keith McCullough — Founder & CEO, Hedgeye Risk Management (2008). Buy-side PM at Carlyle-Blue Wave, Magnetar Capital, Falconhenge Partners & Dawson-Herman; Yale economics, captained Yale hockey. 40+ analysts covering the top 50 economies (~90% of global GDP). Framework: GIP (Growth · Inflation · Policy) — measures the year-over-year rate of change (2nd derivative), not the level, of Growth & Inflation. Free eBook: "Master the Market: A Hedge Fund Manager's Guide to Process and Profit" (54 pp) — ✅ PDF processed 2026-08-22. Paid tier (Hedgeye TV) has the full daily model; this tab tracks public content only.

📘 Landing: info.hedgeye.com/master-the-market · 📄 Download the actual PDF · 📝 Process KB · 🗞️ Hedgeye Daily archive — source links, parsed reads & captured PDFs

📐 Process / GIP 🗞️ Hedgeye Daily 📄 Daily Deck 📋 Weekly Notes 🗂️ Work Log

GIP Nowcast — Current Reading

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📹 Video-confirmed (2026-08-28): Hedgeye runs a two-speed rate-of-change model — the YoY is the Quad classifier; a separate sequential (QoQ/MoM) signal is the early-warning/timing layer that generates the 3–6 month lead. Our GIP replica currently uses YoY only — the sequential component is the primary accuracy gap. Source: McCullough 2021-11-01 (n91gg9LBy9E @2:17) · KB §3

Nowcast Projections — CPI & GDP

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Current Quad Read — Monthly vs Quarterly

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Hedgeye's published call vs our GIP-replica nowcast, split monthly (Macro Week Summary cadence) and quarterly. A ⚠ flags where the two lenses disagree on the regime.

Quad Positions — Hedgeye's Regime Map

The canonical Hedgeye map: Growth (vertical) × Inflation (horizontal), each measured as rate of change (2nd derivative). Where the economy sits dictates the positioning theme. Faithful replica of Hedgeye's published Quad Positions chart; the detailed per-quad asset grid is below.

📖 What Are The Quads? — Hedgeye's own definition (deck p.9, verbatim)

←———— GROWTH ————→

QUAD 1

Growth ↑ Inflation ↓

Pro-Growth Positioning

QUAD 2

Growth ↑ Inflation ↑

Pro-Growth + Inflation Hedges

QUAD 4

Growth ↓ Inflation ↓

Defensive + Volatility Assets

QUAD 3

Growth ↓ Inflation ↑

Stagflation Protection

←—————— INFLATION ——————→

The Four Quads — GIP Regime Grid

McCullough measures GDP growth and inflation as rate of change (2nd derivative) — not whether they are high or low, but whether they are accelerating or decelerating. Policy (the third axis) determines which Quad a regime exits to. The current Quad drives sector rotation, asset allocation, and duration bets.

Canonical Hedgeye numbering (per the eBook, stated twice): Q1 Goldilocks · Q2 Reflation · Q3 Stagflation · Q4 Deflation. Asset rows are the published archetype, not a live portfolio.

Quad 1 — Goldilocks

Growth ↑ · Inflation ↓ · Policy: Neutral

Equities: Tech (XLK), Consumer Discretionary (XLY), Industrials (XLI), Nasdaq (QQQ)
Fixed income: Underweight long duration (TLT lags)
Commodities: Broadly positive; Gold: neutral / sideways
USD: Soft
Factors: High Beta, Momentum, Growth, Mid-caps · Avoid: Utilities (XLU), Staples (XLP), Energy (XLE)

Quad 2 — Reflation

Growth ↑ · Inflation ↑ · Policy: Hawkish

Equities: Tech (XLK), Consumer Discretionary (XLY), Financials (XLF), Energy (XLE), Industrials (XLI)
Fixed income: Short duration — meaningful selloff in global duration
Commodities: Long — oil, copper, industrial metals; Gold: mixed→lower
USD: Softer vs risk currencies
Factors: High Beta, Secular Growth · Avoid: Long-duration bonds, defensives

Quad 3 — Stagflation

Growth ↓ · Inflation ↑ · Policy: "In a box"

Long: Energy (XLE), Commodities (DBC), Gold (GLD) — signature Q3 winner, TIPS
Fixed income: Duration poor (inflation erodes hedge)
USD: Firm (usually) — very strong USD can cap commodities/gold
Factors: Low Beta, Value; the edge is commodity exposure
Avoid: Growth / high-beta equities, Financials

Quad 4 — Deflation

Growth ↓ · Inflation ↓ · Policy: Dovish

Equities: Defensives — Utilities (XLU), Staples (XLP), Healthcare (XLV), REITs (VNQ)
Fixed income: Long duration (TLT, SHY) — core long
Commodities: Short (oil); Gold (GLD): core long
USD: Long (UUP) — strengthens
Factors: Low Beta, Min-Vol, Quality, Div-Yield, Large-cap · Avoid: Tech (XLK), Energy (XLE), high-beta

Risk Range™ Framework

Three Durations
Trade — immediate-term, ≤ 3 weeks
Trend — intermediate-term, ≥ 3 months (quarterly)
Tail — long-term, inside of ~3 years (secular / regime)

Inputs = Price, Volume, Volatility → a dynamic probable range (moves intraday). Buy the low end (#oversold), sell the high end (#overbought). Rooted in Mandelbrot fractal math (non-normal distribution); the exact formula is proprietary / undisclosed.

Bayesian Process
McCullough's process is explicitly Bayesian: he starts with a prior (the Quad), updates it with incoming macro data (non-farm payroll, CPI, PMI), and revises Quad calls in real time. Key signal hierarchy:
1. GDP growth rate of change (GIP: G)
2. Inflation rate of change (GIP: I)
3. Fed/policy response (GIP: P)
Quad calls typically change quarterly (aligned to data revisions), not daily.

Position Sizing & Trade Rules (from the eBook — Ch. 4)

MIN / MAX by asset class (sized to volatility)
Commodities (incl. crypto): 1% – 4%
Equities: 2% – 6%
Fixed Income: 3% – 10%
Single stocks — Long: 1% – 3% · Short: 0.5% – 2%

Stop-loss: −6% (evolved from −12%) — only after reaching MAX size. Add / trim in 50–150 bps increments; never all-in.

Buy the dip / stay in — bullish on TRADE and TREND, Risk Range printing higher-lows & higher-highs, and near the low end (LRR).

Get out — process-driven, not P&L-driven
Lower-Highs in Risk Range → reduce size
Lower-Highs + breaks TRADE support → cut to minimum
Lower-Highs + breaks TRADE + breaks TREND → out

Price + GIP-Quad Overlay

Series Q1 Goldilocks Q2 Reflation Q3 Stagflation Q4 Deflation

Regime shading behind adjusted price (view starts 2019, the earliest Quad coverage), split into quarterly and monthly reads. Hedgeye = published calls (gaps where no public call exists); Our Quad = GIP-replica nowcast (quarterly 2022Q4+, monthly ~2024+), experimental. Monthly bands are one month wide and coverage is shorter. The four toggles are independent — enable any combination; active overlays split the plot into equal horizontal strips (top→bottom in legend order). Hover for price + the active Quad on each strip.

Quad-Driven Model Portfolios

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Archetype allocations sized to McCullough's §5 MIN/MAX bands — not a live trade list. Two models (monthly and quarterly), each showing our replica beside Hedgeye's published call; holdings diverge when the lenses disagree.

Sector Rotation by Quad

Sector / ETF	Q1 Goldilocks G↑ I↓	Q2 Reflation G↑ I↑	Q3 Stagflation G↓ I↑	Q4 Deflation G↓ I↓
Technology (XLK)	✅ Best	Good	❌ Avoid	❌ Worst
Cons. Discretionary (XLY)	✅ Best	Good	❌ Avoid	❌ Avoid
Industrials (XLI)	Good	✅ Best	Neutral	❌ Avoid
Financials (XLF)	Neutral	✅ Best	❌ Avoid	❌ Avoid
Energy (XLE)	❌ Avoid	✅ Best	✅ Best	❌ Avoid
Materials (XLB)	Neutral	✅ Good	Good	❌ Avoid
Cons. Staples (XLP)	Avoid	Neutral	Neutral	✅ Best
Utilities (XLU)	❌ Avoid	Avoid	Neutral	✅ Best
Healthcare (XLV)	Neutral	Neutral	Neutral	✅ Best
Real Estate (VNQ/XLRE)	Neutral	Avoid	❌ Avoid	✅ Best
Long Bonds (TLT)	❌ Avoid	❌ Avoid	❌ Avoid	✅ Best
Gold (GLD)	Neutral	Neutral	✅ Best	✅ Best
Commodities (DBC)	Good	✅ Best	✅ Best	❌ Avoid
Nasdaq (QQQ)	✅ Best	Good	❌ Avoid	❌ Avoid
Small Caps (IWM)	Neutral	Good	❌ Avoid	❌ Worst
USD (UUP)	Avoid	Neutral	Neutral	✅ Best
Cash / T-bills	Avoid	Neutral	Good	✅ Best

Quad → Forward Equity Returns Backtest

Quarterly quad_nowcast signal vs SPY/QQQ/TLT/GLD/IWM forward returns (3mo = 63td, 6mo = 126td). Entry = first trading day of the quarter. Latest-revised vintage — ex-post, not live out-of-sample. Run: python3 finance/macro/quad/backtest_quad_returns.py

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Current Calls Tracker (last 30d · parsed from X posts + video transcripts · re-run: parse_mcc_calls.py --backfill 30)

Date	Call Type	Asset / Sector	Direction	Quad Context	Source	Status
No calls logged yet. Seed this table from @KeithMcCullough X posts and Hedgeye free content. Rob: start populating as you read public calls — I'll wire a watcher once there's a baseline.

Video Library — YouTube Round 1 (2026-08-28)

84 videos identified · 25 processed · 14 transcribed · 78 methodology segments extracted. Full corpus: KB §13. High-priority Whisper ASR targets: 2fWVrKiDkq0 (Trade-Trend-Tail, 2015) · eOcq8Ni2YsI (Durations, 2021) · mkYqq0RYu5o (Evolution of signaling, 2021).

Year	Title	Channel	TX	Segs	Link
2016	Macro Mentoring Session 11 — Inside Hedgeye's Macro Process	Hedgeye	✅	382	▶
2017	Macro Mentoring Session 12 — How to Invest For A Growing U.S. Economy	Hedgeye	✅	317	▶
2021	The Outlook — USA Quad 2 in Q4? (30 segments — most dense)	Hedgeye	✅	1158	▶
2021	Keith McCullough's Outlook for Chinese Equities	Hedgeye	✅	178	▶
2020	Here's What's Signaling Quad 4 In U.S. Economy	Hedgeye	✅	119	▶
2023	What is the Difference Between a Shallow vs Deep Quad?	Hedgeye	✅	110	▶
2021	How To Outperform In Macro	Hedgeye	✅	98	▶
2021	How To Increase Your Conviction Using A Multi-Factor Process	Hedgeye	✅	98	▶
2019	Improving Your Investment Process — The One Thing (Real Vision)	Real Vision	✅	92	▶
2024	Growth + Inflation Slowing: China a Perfect Example of Quad 4	Hedgeye	✅	79	▶
2026	How Hedgeye's Global Process Is Beating Consensus	Hedgeye	✅	75	▶
2020	How My Personal Account Changes, When The Quads Change	Hedgeye	✅	63	▶
2019	Macro Show — Stay With This Exposure In Quad 3	Hedgeye	✅	39	▶
2019	Macro Show Highlights: GDP↓ Inflation↑ = Quad 3	Hedgeye	✅	32	▶
2015	The Thinking Behind My "Trade-Trend-Tail" Process ⚠️ Whisper target	Hedgeye	❌	—	▶
2016	How To Think About Volume	Hedgeye	❌	—	▶
2018	Quad 4 Is Here. Where Can You Still Get Long?	Hedgeye	❌	—	▶
2018	What's Behind The Quad 4 Market Hurricane	Hedgeye	❌	—	▶
2019	How to "A/B Test" Your Investing Positions	Hedgeye	❌	—	▶
2019	Quad 4 = Short Tech	Hedgeye	❌	—	▶
2019	Quad 3 = Long Cannabis?	Hedgeye	❌	—	▶
2021	How To Understand Trade and Trend Durations ⚠️ Whisper target	Hedgeye	❌	—	▶
2021	Evolution Of Keith's Signaling Process Since Calling the 2008 Crash ⚠️ Whisper target	Hedgeye	❌	—	▶
2018	The Macro Show (4/2/2018): A Hedge Fund Manager's Playbook	Hedgeye	❌	—	▶

📘 Resolved by processing the eBook

Quad numbering corrected — canonical is Q1 Goldilocks · Q2 Reflation · Q3 Stagflation · Q4 Deflation. The prior grid had the numbers scrambled; fixed above (source states it twice).
Risk Range formula — the eBook does not disclose it. Inputs are Price / Volume / Volatility; basis is Mandelbrot fractal math; the weighting is proprietary. A faithful replica isn't possible from public material — only a volatility-band approximation.
GIP data series — the eBook does not name the specific series (just "predictive tracking algorithms" for the top 50 economies). An independent nowcast would use FRED / BLS / BEA (GDP, CPI/PCE, PMI, NFP) + market proxies.
Position sizing, stops, durations, daily workflow — fully captured (sections above + KB).

📹 Resolved by video corpus (2026-08-28 — 14 transcripts, 78 segments)

GIP axis layout verbatim — Growth YoY on Y-axis, CPI YoY on X-axis; Q1=top-left, Q2=top-right, Q4=bottom-left, Q3=bottom-right. Confirmed by Director of Research Jones (2017-01-23). Source: NBVin93Y4M4 @0:40
Two-speed RoC ("sequential AND year over year") — McCullough explicitly tracks BOTH a YoY Quad classifier AND a sequential (QoQ/MoM) early-warning signal. Our engine is YoY-only — this is the primary replica gap. Source: n91gg9LBy9E @2:17 (McCullough 2021-11-01)
Bayesian overlay confirmed — after Quad-based factor ranking, apply a Bayesian overlay: Quad → factor ranking → Risk Range confirmation gate. Quad is necessary but not sufficient; the Signal must confirm. Source: NBVin93Y4M4 @3:28 (Jones 2017-01-23)
Gold Quad cross confirmed verbatim — "flip from Quad 3 to Quad 2 → go from long gold in Quad 3 to short gold in Quad 2." Source: n91gg9LBy9E @4:05
Quad 4 → Policy dovish confirmed verbatim — "when you have neither growth or inflation... Policy goes dovish." Source: n3u8fMYKmwA @1:10
Risk Range vol windows confirmed — 30/60/90-day realized vol + implied vol. Source: NBVin93Y4M4 @8:13
No contradictions with KB — all 11 key findings corroborate existing keith-mccullough-gip.md; new nuances (two-speed RoC, base effects, Bayesian gate) added to KB §13.

New gaps found: (a) base effect modeling — Hedgeye explicitly flags easy/hard comps; our engine doesn't. (b) 11 no-transcript videos — especially "Trade-Trend-Tail Process" (2015) and "Trade and Trend Durations" (2021), best Whisper ASR targets.

⚠️ Open — for Rob

Homemade Quad tracker? — build a live GIP nowcast from our own FRED + Polygon data to mirror his framework without the paywall. Recommend: yes — highest-leverage next step; gives a live Quad estimate to key the sector table to.
Calls tracker seeding — pull @KeithMcCullough's public posts and pre-populate the calls table. Recommend: yes — seed the last 30 days, then wire the 2h watcher.
Hedgeye TV subscription — do you have one? Determines whether coverage stays public (X + YouTube) or can flag paywall-free segments.
Whisper ASR on 11 no-transcript videos — especially "Trade-Trend-Tail Process" (2015, videoId: 2fWVrKiDkq0) and "Trade and Trend Durations" (2021, videoId: eOcq8Ni2YsI). These are the methodology-densest titles without captions. Est. 2–3h local Whisper run. Recommend: run next; highest-priority gap after sequential RoC.

Public Sources Being Monitored

X / Twitter
@KeithMcCullough — daily macro calls, Quad updates, Risk Range levels, sector rotation
Expert content watcher: highFreq: false (every 2h)
VEP expertId: keith-mccullough

YouTube — @Hedgeye ✅ Round 1 processed 2026-08-28
Channel: UCkDxvN-bcxsKkvJ3yyiGSVQ · 84 unique videos identified · 25 processed · 14 transcribed · 78 methodology segments
KB: §13 Video Corpus
Ongoing: VEP enrichment loop (30-min tick)

eBook — Master the Market (54 pp) ✅ processed 2026-08-22
KB: keith-mccullough-gip.md
📄 Actual PDF · 📘 Landing page

Hedgeye Free Content
app.hedgeye.com/feed_items — some free research items
hedgeye.com/research — free summaries
Monitoring: manual review or watcher expansion (pending Rob direction)

🗂️ Work Log — Proposed & Pending

Live from proposed_work + open_loops. Refreshed daily 18:00 ET. → Completed work & detail

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KB: keith-mccullough-gip.md · VEP ID: keith-mccullough · Framework: GIP Quads © Hedgeye Risk Management — methodology from public disclosures + the eBook · Last updated: 2026-08-28 (video corpus round 1 — 25 videos, 14 transcribed, 78 segments)

✅ Work Log — Completed

Every closed proposed_work item and resolved open loop in the McCullough/Hedgeye domain. Refreshed daily 18:00 ET alongside the pending card. ← back to pending

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🗞️ Hedgeye Daily — capture archive & parsed reads

← McCullough / GIP tab

Captured through Rob's own authenticated Hedgeye subscription via hedgeye-collect.mjs. Personal reference only — Hedgeye prohibits redistribution. Gated assets are never bypassed; blocked items are recorded as blocked (see each day's collection report).

Capture dates

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Captured artifacts

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Positions — hedgeye_positions

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Claims — hedgeye_claims

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Parsed reads — hedgeye_reads

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Collection report

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Instrument → Ticker map — hedgeye_ticker_map

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📄 Daily Deck — Macro Show slide reproduction

Each card is a rendered page from the Macro Show deck PDF — exact, auth-gated, personal-reference only.

📐 Our GIP Read Loading…

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📋 Weekly Notes — Macro Week Summary

Structured reproduction of the Macro Week Summary Notes — prose sections, ticker links, and calendar. Auth-gated, personal-reference only.

⏳ No weekly notes rendered yet — renderer is being built (Phase 1).

Series: Short SMA: Long SMA: Signal: Chart window:

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Close vs SMA(short) vs SMA(long) — — —

10/20 SMA Market Filter — Research basis
Filter is ON when Close > SMA(10) and Close > SMA(20). Otherwise OFF (hold cash/bonds). Popularized by Connors/Alvarez; validated by QuantifiedStrategies.com 20-year RSI study. Applied to SPY: ~65–70% time in market; max drawdown cut ~40–50% vs buy-and-hold with modest CAGR sacrifice (~1–3%). Most effective as a gate for other signals (RSI, momentum) rather than standalone. Crossover variant: SMA(10) > SMA(20) — same concept but smoother (fewer whipsaws, more lag). Risk-off asset when filter is OFF: cash, SHY (1-3y T-bill), or TLT (duration hedge). Source: QuantifiedStrategies.com — 20-Year RSI Study ↗

Price + SMA Filter — SPY

Shaded = signal ON (long); unshaded = signal OFF (cash)

Backtest — Full History

Signal Log — Last 50 Flips

Date	State	Close	Direction
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Policy Makers

Treasury · Federal Reserve · Key officials shaping fiscal & monetary conditions

Scott Bessent Treasury Secretary 2025–present

Founder & CIO, Key Square Group (2015–2025). Former CIO, Soros Fund Management (2011–2015). Yale BA. Confirmed Jan 27, 2025 as 79th Treasury Secretary.

⚡ 3-3-3 Fiscal Plan

Deficit / GDP by 2028

Real GDP growth

+3M

boe/day energy

Newman counter-thesis: 2-6-0. CBO gap: ~$499B/yr to close. Track: reference_bessent_333_newman_260.md

"The Fed owns over 50% of all bonds maturing between 10 and 15 years from now." — Bessent, 2023

→ Distorted price discovery in the 10-15Y bucket; fiscal dominance trajectory; structural QT constraint.

FALSIFIABLE PLEDGES — monitor for reversal

⚠No Medicaid cuts — "empowering states"; block-grant language is the falsification vector

⚠TCJA pay-fors named — not dynamic-scoring only

⚠Entitlements off-table — SS/Medicare for full 4-year term

LINKS & SOURCES

🎬2023 Video — Fed 50% 10-15Y 📰Fox Business — 3-3-3 Plan 📚KB: scott-bessent-treasury.md

VEP ID: scott-bessent

Kevin Warsh Fed Gov. 2006–2011 🎯 Chair Frontrunner

Distinguished Visiting Fellow, Hoover Institution (Stanford). Former Fed Governor; Morgan Stanley M&A; NEC adviser to George W. Bush. Stanford BA · Harvard Law JD. Resigned Fed in 2011 over QE direction disagreements. Expected nomination for Fed Chair when Powell term expires May 2026.

On QE / Balance Sheet

Consistent critic since 2010. QE = financial repression, wealth redistribution to asset holders. QT is prerequisite to credible policy.

On Framework

Rules over discretion. Modified Taylor rule. FAIT (2020 framework) should be revised. Data-dependent language = backward-looking.

On Inflation

Hawkish. Early warning on 2021 transitory narrative. Fiscal dominance is the transmission mechanism for durable inflation.

On Independence

Defends political independence; opposes accountability opacity. Fed buying Treasuries ≈ fiscal agent of Treasury = independence compromised.

"Large-scale asset purchases are fiscal operations wearing monetary clothing." — Warsh, paraphrased from Hoover/WSJ commentary

→ Aligns with Bessent 2023 (Fed owns 50%+ of 10-15Y bucket). Shared diagnosis, complementary policy roles.

IF CONFIRMED AS CHAIR — MARKET IMPLICATIONS

↑ Long-end yields (less Fed buying) ↑ Accelerated QT FAIT framework review Tighter financial conditions ↑ Rules-based credibility premium

LINKS & SOURCES

🏛️Hoover Institution Profile 📰WSJ: "The Fed's Risky Experiment" (2012) 📜FOMC Historical Transcripts 📚KB: kevin-warsh-fed-governor.md

VEP ID: kevin-warsh

🔗 BESSENT ↔ WARSH — SHARED DIAGNOSIS

Both identify the same structural problem from complementary positions: the Fed's QE programs concentrated ownership of the 10-15Y Treasury bucket to the point where price discovery is impaired, and fiscal deficits running at 6-7% GDP have advanced the US toward fiscal dominance. Bessent (Treasury) controls the supply side of that equation — issuance duration management, deficit trajectory. Warsh (potential Chair) would control the demand side — QT pace, balance-sheet composition. If Warsh is confirmed, the fiscal/monetary coordination framework becomes the central macro signal to track.

STAGING — future policy makers

Jerome Powell (Fed Chair, through May 2026) Christopher Waller (Fed Governor — hawkish) Neel Kashkari (Minneapolis Fed) Janet Yellen (predecessor, context) Lutnick (Commerce) — tariff policy

Add cards above following this page's pattern. KB in knowledge/macro/ · VEP ID in expert-chat.db.

JARVIS COMMAND CENTER

Price Series

MACD (19, 39, 1) (signal=1 ⇒ EMA(1) of MACD is MACD, so no signal line or histogram)

% from Moving Average

Current Signals

Strategy Research

Current Allocation

Current Holdings (top 8)

Performance Metrics

Equity Curve —

All Variants vs SPY

Layer 2 — Sector Rotation

Sector Signals (INACTIVE)

Layer 2 Equity Curve (INACTIVE)

Layer 2 Performance (INACTIVE)

Layer 2 — Nested Sector Engine

Current Book

Equity Curve (backtest)

Performance Metrics

Pair Trades — Relative Performance

Relative Performance —

Equity Curve — Signal Tilt vs Benchmarks

Performance Metrics

Latest Day — Advancers vs Decliners

Cumulative A/D Line

% Members Above SMA & Trend

Distance-from-SMA — Cross-Section (magnitude, not sign)

MACD (19, 39, 1) — A/D Line & Overlay (signal=1 ⇒ EMA(1) of MACD is MACD, so no signal line or histogram)

McClellan Oscillator & Summation Index (RAMO — methodology-dependent)

Constituents — Drill-Down

SMA Crossover Heatmap

EMA Crossover Heatmap

Study

Realized Volatility (21d EMA)

VONG/VONV (Russell 1000)

VUG/VTV (Vanguard)

Signal Tearsheet

Cumulative Returns

Drawdown (%)

Signal Tearsheet — Page 2

Monthly Returns (%)

Yearly Returns (%)

@smallfishmacro · "Master Dojo" STIR Sheet

Capital Flows STIR Replication Playbook

@Globalflows Build-Guide Thread

📚 The Shelf

📐 McCullough / Hedgeye — GIP Framework

QUAD 1

QUAD 2

QUAD 4

QUAD 3

🗞️ Hedgeye Daily — capture archive & parsed reads

📄 Daily Deck — Macro Show slide reproduction

📋 Weekly Notes — Macro Week Summary

Book

Price + SMA Filter — SPY

Policy Makers

Options Platform