The Indexes.

BMPI tracks Bitcoin's evolution from speculative risk asset to monetary instrument. When BTC rallies alongside gold and inversely to DXY during risk-off episodes, it exhibits 'monetary premium' — behaving more like a reserve asset than a tech stock. When BTC moves in lockstep with altcoins and risk assets, it is in 'speculative' mode. The core signal is BTC/Gold log-ratio (20%) — the cleanest measure of whether BTC is being repriced relative to the oldest monetary asset. USD inverse (15%) and BTC/ETH dominance (15%) separate BTC-specific demand from broad crypto beta. BTC vs SPY excess return (12%) and BITO momentum (8%) capture market structure. Macro overlays — VIX (10%), breakeven inflation (10%), and 10Y yields (10%) — provide the macro regime context that determines whether BTC's monetary premium is expanding or contracting.

BTCM provides macro context for Bitcoin positioning by combining BTC spot momentum, USD strength, breakeven inflation expectations, and equity risk appetite.

NAUI tracks auto dealership, service, and parts NNN properties - more cyclical than pharmacy or QSR. Dealerships - AutoNation (AN, 8%), Penske (PAG, 6%), Lithia (LAD, 6%), CarMax (KMX, 6%) - and parts retailers - O'Reilly (ORLY, 8%), AutoZone (AZO, 6%) - cover both new/used vehicle sales and the aftermarket. Agree Realty (ADC, 6%) represents the NNN landlord side. Total vehicle sales (TOTALSA, 8%) and employment (6%) drive demand, the 10-year yield (12%) reflects cap rate pressure, oil (14%, negative) is weighted heaviest among NNN indices because fuel costs directly impact vehicle purchasing decisions and dealership traffic, and VIX (14%) provides risk sentiment. The dual exposure to dealerships and parts retailers provides a built-in hedge: when new car sales slow, aftermarket parts and service volumes often increase.

NQSR tracks the health of quick-service restaurant triple-net lease properties - think McDonald's, Yum, and Domino's locations. McDonald's (MCD, 10%) and Yum Brands (YUM, 8%) anchor the operator equities, with QSR (6%), Domino's (DPZ, 6%), and Wendy's (WEN, 4%) providing QSR breadth. Realty Income (O, 8%) and NNN REIT (NNN, 6%) represent the NNN landlord side. Food-service retail sales (RSFSDPN, 8%) and employment (6%) capture consumer demand, the 10-year yield (12%) reflects cap rate pressure on NNN valuations, oil (12%, negative) is a cost headwind for consumers driving to restaurants, and VIX (14%) provides broad risk sentiment. QSR NNN properties are among the most stable in commercial real estate - fast-food chains sign 15-20 year leases with predictable rents. The component mix balances operator health (are these chains growing?) with property fundamentals (are cap rates and consumer traffic supportive?).

CBGA tracks central bank gold accumulation dynamics using market-based proxies. Direct WGC quarterly data updates slowly; this index uses gold miner outperformance, physical gold ETF flows, Shanghai/COMEX spread proxies, and gold vs USD dynamics to infer directional central bank buying pressure. Gold spot (18%) anchors physical demand, GDX vs SPY (15%) confirms miner leverage on physical buying, Gold vs UUP log-ratio (15%) captures de-dollarization demand, and Gold vs TLT (12%) signals reserve rotation from bonds to gold. Junior miners GDXJ vs GDX (8%) confirm sustained demand, while DXY (12%), breakeven inflation (10%), and VIX (10%) provide macro context. Rising CBGA = accumulation accelerating; falling = distribution or pause.

CLRI is intentionally rate-dominant because it measures the price of consumer credit, not credit quality. The largest weights sit on the most representative consumer borrowing rates: credit card APR (TERMCBCCALLNS, 13%), auto loan rates (12%), and 30-year mortgage rates (11%) - these drive the household debt-service function and are directly observable. Treasury benchmarks - the 5-year yield (10%), T10YFF spread (10%), and 10-year yield (9%) - anchor the consumer rate complex to the broader risk-free curve and term premium. Retail sales (9%, negative) and bank equity health (BAC, 7%, negative) signal the economic and banking impact. Unemployment (8%), employment (6%), and VIX (5%) provide context: if rates rise when unemployment rises, the burden is larger; if volatility rises, consumer spreads widen beyond what Treasuries explain. At 65% rate weight, CLRI is the most rate-concentrated index in the entire framework - most movement is 'rates moved,' with macro terms acting as modifiers.

ALVI is built around a single premise: if you want to measure auto credit volume, start with a direct auto-loan balance series. Consumer auto loans (CARACBW027SBOG, 15%) receive the largest weight as the closest 'quantity' measure of auto credit. The next largest weights go to the two biggest mechanical drivers of origination: auto loan rates (15%, negative) and total vehicle sales (TOTALSA, 14%), because auto demand and approvals are rate- and demand-constrained. Underwriting standards (STDSAUTO, 10%, negative) capture credit box tightness. Ford (11%) and GM (11%) equities reflect market sentiment and forward expectations. Employment (9%), the 5-year yield (6%), retail sales (5%), NFCI (3%), and VIX (1%) provide supporting macro context. The equal 15% weights on loan volumes and auto rates reflect the two most important variables: how much auto credit is being originated and at what cost.

SCMI measures 'Main Street credit oxygen' - the small business financing conditions that often diverge from large-cap corporate credit. Small business loans (SLAACBS, 12%) and regional bank equities - KRE (12%) and East West Bancorp (EWBC, 10%) - track the actual lending ecosystem, because regional banks provide the majority of small business loans and their equity performance is a direct signal of credit availability. System stress measures (STLFSI4, 11%; NFCI, 6%) and small business lending standards (DRTSCIS, 10%) capture the environment that makes banks either extend or retract. Retail sales (10%) and the Baa-10Y spread (9%) signal Main Street activity and credit conditions, the 2-year yield (8%) captures borrowing cost pressure, and employment (7%) and VIX (5%) provide supporting context. The weight spread across flow and price inputs keeps SCMI interpretable - users can attribute moves to approvals/lender capacity versus broad risk conditions.

DPRI captures the health of digital payment infrastructure - the networks, fintech platforms, and transaction rails that move money electronically. The index is intentionally multi-layered to avoid being a generic financials or consumer proxy. IPAY vs consumer discretionary (XLY) captures whether payment rails are outperforming retail - a cleaner payments premium signal. FINX vs banks (XLF) captures fintech momentum versus traditional financials. Visa and Mastercard carry the heaviest individual weights because they are the most defensible payment businesses and the best listed proxies for global transaction volume throughput. Block and PayPal vs IPAY are smaller canary sleeves reflecting cyclicality and competitive dynamics without letting idiosyncratic volatility dominate. The macro sleeve is unusually heavy (22%) because payment volumes are directly driven by consumer activity - retail sales (RSAFS) and consumer credit expansion (TOTALSL) both feed transaction flow. Short rates matter more than long rates here: DGS2 directly affects consumer credit costs and fintech funding stress, which is the core transmission mechanism for payment volumes. DTWEXBGS captures USD headwinds to cross-border volumes. HYG vs LQD and VIX capture credit risk appetite and broad risk sentiment. When consumers are spending, credit is flowing, fintech is outperforming banks, and the rails networks are beating the market, DPRI rises.

SCSI tracks the application-layer cloud and software subscription cycle - whether recurring-revenue SaaS businesses are gaining strength versus the rest of tech and the broader market. The components are intentionally layered so the index is not just tech beta: two ratio legs (IGV vs XLK and WCLD vs QQQ) isolate software and cloud outperformance inside tech rather than software versus the whole market. This is a critical distinction because IGV can fall even while XLK rallies if AI hardware (semiconductors) is dominating. CRM, ServiceNow, and Adobe are benchmarked against IGV as leadership canaries - measuring whether top platforms are outperforming the software complex without letting idiosyncratic stock risk dominate the index. The rate sleeve is unusually heavy (18%) because SaaS valuations are among the most duration-sensitive in the market: these are high-growth, low-current-earnings businesses where the discount rate directly compresses multiples. Two rate signals work together - DGS10 for the discount rate itself, and T10Y2Y for curve regime (steepening = growth optimism = SaaS tailwind). HYG vs LQD isolates the high-yield vs investment-grade spread rotation cleanly without duration contamination. DTWEXBGS captures USD tightening that hits international SaaS revenue translation, while VIX and NFCI capture equity risk-off and broad financial conditions. When subscription software is outperforming hardware tech, enterprise spending is healthy, and rates are not crushing valuations, SCSI rises.

HBGI is a risk-appetite thermometer for high-beta growth. The heaviest weights sit on pure risk-on equity expressions: ARKK (12%) for innovation-premium speculation, IWM (10%) for small-cap breadth, MSTR (8%) for high-volatility narrative proxies, and SQ (7%) for fintech. HYG (13%) and VIX (13%) - both negative polarity - are the dominant risk-sentiment signals because high-beta growth is the first to collapse when liquidity tightens or volatility spikes. The 2-year yield (10%) captures short-rate pressure on growth valuations, consumer credit (TOTALSL, 10%) tracks the marginal dollar of speculative leverage, and gold (7%) and the dollar (5%) signal conditions hostile to speculation. This index is intentionally the most risk-sensitive in the framework - it measures the froth that evaporates first when conditions tighten.

CBRI measures the cybersecurity premium - the extent to which security spending is behaving as non-discretionary and security equities are outperforming broader software. Cyber is structurally different from generic SaaS: it can hold up or even outperform during risk events because breaches, regulation, and threat environments force spend even when budgets tighten. A rising CBRI implies security platforms are taking budget share, consolidating spend, and being rewarded by the market as must-have infrastructure; a falling CBRI implies budget pressure is reaching security, or macro tightening is overwhelming the defensive narrative. The primary anchor is CIBR vs IGV, which isolates cyber versus software specifically (not just tech broadly), while HACK vs XLK provides a second cross-check against all of tech. This means CBRI only rises when security stocks are genuinely leading - not just surfing a tech rally. Four individual cyber leaders (Palo Alto Networks, CrowdStrike, Fortinet, Zscaler) are benchmarked against the CIBR basket to capture platform consolidation and category-winner dynamics without turning the index into a single-stock bet. Rate sensitivity is deliberately lighter than SCSI (10% vs 18%) because cybersecurity's perceived non-discretionary nature partially insulates it from discount-rate compression. VIX and STLFSI4 capture two different stress channels - equity panic and funding stress - that can ultimately constrain even must-have security budgets. When security budgets are expanding, threat-driven demand is strong, and macro conditions are not actively hostile, CBRI rises.

SPSI measures factor momentum, market breadth, and conditions favoring event-driven strategies. The momentum factor ETF (MTUM, 15%) and Russell 2000 (IWM, 12%) form the equity core - momentum health plus small-cap breadth. The most distinctive component is the sector dispersion signal (15%), computed from 11 SPDR sector ETFs, which measures cross-sector divergence. HYG (13%) captures the credit backdrop, the yield curve (T10Y2Y, 10%) signals growth expectations, and jobless claims (10%) and retail sales (10%) provide recent economic momentum. VIX (10%) gauges the volatility regime. The high dispersion weight is intentional - when sectors are diverging rather than moving in lockstep, momentum and event-driven strategies are being rewarded. This index essentially answers: 'Is the market rewarding stock-picking and tactical rotation right now?'

ETSI measures momentum inside the energy-transition complex. ICLN (12%) anchors as the broad clean energy ETF, with Enphase (8%), First Solar (7%), TAN (8%), and LIT (7%) covering solar and battery supply chains. Oil (10%) carries positive polarity - a defining feature - because expensive fossil fuels improve the relative economics of renewables. The 10-year yield (12%) matters because renewable projects are financed with long-duration debt, and LQD (10%) captures project-finance credit conditions. Construction permits (8%) and industrial production (8%) confirm the physical buildout pipeline. VIX (10%) rounds out risk sentiment. The weight structure intentionally separates 'transition fundamentals improving' from 'duration rally' - transition equities are extremely discount-rate sensitive, so the rate and credit controls prevent ETSI from becoming a generic long-duration tech proxy.

RACI captures the industrial automation and robotics investment cycle - the reshoring, labor substitution, and factory-of-the-future theme. The structure is built to isolate automation as a sub-theme rather than broad industrial beta. Two robotics ETFs (ROBO and BOTZ) are benchmarked against industrials (XLI) as dual ratio anchors that explicitly measure automation outperformance inside the industrial complex - so RACI only rises when automation is winning, not when all industrials rally. The single-name sleeve captures different automation vectors: Rockwell Automation for factory automation, Teradyne bridging semiconductor test equipment and collaborative robots, and Intuitive Surgical diversifying into healthcare automation. Fanuc (FANUY), the world's largest industrial robot manufacturer, provides a Japanese supply-chain signal at canary weight (with a documented fallback if OTC ADR staleness persists). The most distinctive design choice is MANEMP at negative polarity with a 45-day half-life: falling manufacturing employment is bullish for RACI because it signals labor substitution - the exact dynamic that drives automation adoption. This is the opposite of broad industrial indices where employment growth is bullish, and the longer half-life accounts for monthly data cadence. The macro sleeve is deliberately causal: DGS10 penalizes capex financing cost, INDPRO captures manufacturing demand, DTWEXBGS captures USD headwinds for globally sourced automation supply chains, and DCOILWTICO carries positive polarity because rising energy costs increase the ROI of automation (human-intensive processes become relatively more expensive). VIX penalizes risk-off regimes where discretionary capex budgets get delayed. When automation stocks outperform industrials, manufacturing employment is falling, energy costs are rising, and industrial production is expanding, RACI signals that the automation capex cycle is accelerating.

INMI measures broad industrial cycle traction. XLI (15%) anchors as the diversified industrials ETF, flanked by Caterpillar (CAT, 8%) and Deere (DE, 7%) as canonical cycle bellwethers, plus COPX (8%) for copper miners - an early-cycle, globally sensitive input that tends to lead industrial earnings revisions. The design philosophy weights the economic cycle heavily because machinery demand is deeply cyclical: industrial production (12%) and the inventory-sales ratio (ISRATIO, 10%) matter as much as equity prices here. The rate complex - 10-year yields (10%) and the 2s-10s spread (10%) - captures financing conditions and growth expectations. Oil (10%) and the dollar (5%) are kept as context filters that prevent the index from confusing late-cycle inflation pressure with healthy industrial momentum.

SPDI tracks defense procurement demand and geopolitical intensity. ITA (15%) provides broad defense-sector exposure, with Raytheon (RTX, 10%) and L3Harris (LHX, 8%) as prime contractors - procurement cycles show up in these names first. Oil (13%) is weighted high because geopolitics and energy shocks tend to coincide with defense repricing; this positive polarity is intentional. Employment (12%) and industrial production (10%) proxy fiscal capacity and the defense manufacturing base, while the T10YFF spread (10%) and NFCI (12%) capture the policy and financial conditions that determine budget feasibility. The weight design ensures SPDI rises when procurement demand is genuinely accelerating, not just when defense stocks get a risk-off bid under worsening conditions.