Chart Interconnection Map

This document maps every chart on the RJALPHA Analysis Dashboard into logical "buckets" that show how they feed into, depend on, and validate each other. The purpose is to help the platform AI (and users) understand that these charts are not isolated, they form a connected analytical pipeline from sovereign debt issuance through to crypto price action and trading signals.

How to Read This Document

Each bucket represents a logical domain of the analytical pipeline. Within each bucket, charts are listed in causal order, the thing that happens first is listed first. Between buckets, arrows show how one domain feeds into another. The overall flow moves from macro fundamentals (government debt, central bank policy) through the financial plumbing (credit transmission, liquidity) and into market signals (price models, trading strategies).

Master Flow Summary

BUCKET 1: Sovereign Debt & Refinancing
  Treasury Maturity Tracker → Treasury Auction Demand → Collateral Multiplier
        ↓                           ↓                         ↓
BUCKET 2: Credit Transmission & Liquidity Pipeline
  Transmission Tracker → Credit Liquidity Index → Global Liquidity Index
        ↓                       ↓                        ↓
BUCKET 3: Dollar & Global Liquidity Context          BUCKET 4: Valuation Models
  GLI vs DXY                                    BTC Fair Value / Gold Fair Value
        ↓                                               ↓
BUCKET 5: Macro Regime & Risk Environment        BUCKET 6: Market Structure
  Macro Regime / Truflation / Carry Trade    Market State / Market Regime / BTC Cycle
        ↓                                               ↓
BUCKET 7: Asset Selection & Rotation             BUCKET 8: Trading Signals
  Asset Dominance / Normalized Price /           VAMS Mid TF / HODL Index
  Capital Allocation / Leverage Risk / Alt League

BUCKET 1: Sovereign Debt & Refinancing Pipeline

What this bucket answers: "How much government debt needs to be refinanced, how easily is it being absorbed, and what capacity does the banking system have to use it as collateral?"

This is the origin point of the entire liquidity story. Everything starts here, when the US Treasury issues or refinances debt, the ripple effects flow through every other chart on the dashboard.

Charts in This Bucket

1.1 Treasury Maturity Tracker

Method: treasury_maturity_tracker

What it does: Tracks the maturity schedule of all outstanding US Treasury securities (Bills, Notes, Bonds) across the next 12–24 months. It shows when debt comes due and must be refinanced, identifies peak refinancing months, and calculates total outstanding amounts by security type.

Key outputs: Monthly maturity schedule broken down by Bills/Notes/Bonds, total outstanding ($30.54T as of dashboard), bills outstanding ($6.91T), next 12-month refinancing volume ($10.38T), peak month and amount, forecast horizon (24 months), historical Z-scores vs 60-month averages.

Why it matters upstream: The maturity schedule is the "demand generator" for the entire pipeline. When $2.19T in bills mature in a single month (Apr 2026 per the dashboard), the Treasury must refinance that debt. Whether it is rolled into short-dated bills or longer-dated notes/bonds determines how that new debt interacts with the rest of the system. Short-dated bills are absorbed primarily by money market funds and used as repo collateral, this directly feeds Bucket 2 (credit transmission). Longer-dated issuance competes with bank lending and affects the yield curve.

Connects forward to:

Treasury Auction Demand (1.2), the maturity schedule determines auction volumes; more maturities = more auctions = more stress potential
Transmission Tracker (2.1), bills outstanding is a primary input; the bills-to-lending velocity is calculated from this data
Macro Regime (5.1), refinancing pressure influences QE/QT probability and Treasury QE regime signals

1.2 Treasury Auction Demand

Method: treasury_auction

What it does: Analyzes how well the market is absorbing newly issued Treasury debt by tracking auction metrics: bid-to-cover ratios, bidder composition (dealers, foreign/indirect, direct), and stress signals. It identifies when auctions show strain, low foreign demand, dealer stuffing, weak bid-to-cover.

Key outputs: Bid-to-cover ratios by security type (Bills, Notes, Bonds), stress rate (e.g., 1/33 = 3.0% stressed), Fed regime context (QE vs QT), dealer trend, foreign demand trend, bid-to-cover trend, stress alerts with red diamond markers.

Why it matters in the chain: This is the "absorption check." The Treasury Maturity Tracker tells you how much debt needs to roll over; the Auction Demand chart tells you how easily the market is digesting it. Key signals:

Low foreign demand (47.0% on dashboard) means domestic institutions must absorb more, potentially crowding out private lending.
Rising dealer trend means primary dealers are being "stuffed" with inventory they do not want, they will use it as repo collateral, which feeds the transmission mechanism.
Strengthening bid-to-cover means demand is healthy and the system can absorb the issuance.

Connects forward to:

Collateral Multiplier (1.3), auction stress directly affects bond market volatility (MOVE index), which determines collateral multiplier
Transmission Tracker (2.1), auction outcomes determine what ends up on bank balance sheets as collateral
Credit Liquidity Index (2.2), the spreads and yields that result from auction dynamics are direct CLI inputs
Carry Trade Monitor (5.3), foreign demand trends are tied to carry trade dynamics (if Japanese investors pull back, both foreign auction demand and carry trade risk shift)

1.3 Collateral Multiplier vs MOVE

Method: collateral_multiplier_vs_move

What it does: Calculates the effective collateral multiplier, how many times a dollar of Treasury collateral can be reused (rehypothecated) in the repo market. It derives this from the MOVE Index (bond market volatility). Low MOVE = calm bond markets = banks trust each other's collateral = higher multiplier. High MOVE = volatility = haircuts increase = multiplier contracts.

Key outputs: Current MOVE index value, 14-day moving average, collateral multiplier (range 0.5x to 3.0x). The formula is exponential: multiplier = 2.0 × exp(-0.5 × normalized_MOVE) + 0.5.

Why it matters in the chain: The collateral multiplier is the amplifier or dampener of the entire credit transmission system. When the multiplier is high (say 1.9x as shown on dashboard), each dollar of Treasury collateral supports nearly $2 of lending. When it contracts (bond market stress, rising MOVE), collateral velocity drops and credit tightens, even if the Fed has not changed policy.

This is why the MOVE index is sometimes called the "most important chart no one watches." A spike in MOVE can:

Force banks to post more collateral for the same positions
Reduce the velocity of money through the repo system
Tighten financial conditions faster than rate hikes

Connects forward to:

Transmission Tracker (2.1), the collateral multiplier directly affects how efficiently credit flows from Stage 2 (repo) to Stage 3 (lending)
Credit Liquidity Index (2.2), bond volatility affects credit spreads, which are CLI components
GLI (2.3), the multiplier effect amplifies or dampens the liquidity signal

Connects back to:

Treasury Auction Demand (1.2), stressed auctions create MOVE spikes which compress the multiplier

Bucket 1 Internal Flow

Treasury Maturity Tracker
  "How much debt needs refinancing?"
        ↓ (maturity schedule drives auction volumes)
Treasury Auction Demand
  "How well is the market absorbing new debt?"
        ↓ (auction stress → bond volatility)
Collateral Multiplier vs MOVE
  "How effectively can that debt be used as collateral?"
        ↓ (feeds into credit transmission)

BUCKET 2: Credit Transmission & Liquidity Pipeline

What this bucket answers: "Is the liquidity created by government debt issuance actually flowing through the banking system into the real economy, and what does total system liquidity look like?"

This bucket tracks the journey of liquidity from the Fed/Treasury through the banking plumbing and into a composite score. The Transmission Tracker watches the plumbing in detail; the CLI and GLI aggregate the result into single indices that correlate with crypto prices.

Charts in This Bucket

2.1 Transmission Tracker (Transmission Lead)

Method: transmission_tracker

What it does: Monitors the 5-stage credit transmission pipeline from sovereign debt issuance through to real economy impact. It tracks whether liquidity is actually flowing from the government to banks to borrowers, or if it is getting stuck somewhere.

The 5 Stages:

Stage 1, Issuance: Is the Treasury issuing new bills? (Bills outstanding growth above 5% = score 100)
Stage 2, Buying: Are buyers absorbing them? (RRP draining + MMF growing = healthy)
Stage 3, Repo: Is the repo market functioning? (SOFR stability = plumbing works)
Stage 4, Lending: Are banks converting reserves to loans? (C&I loan growth above 3% = score 100)
Stage 5, Impact: Is it showing up in money supply? (M2 growth above 5% = full transmission)

Key outputs: Transmission velocity (C&I loan change / bills change), loan-to-deposit ratio, regime detection (STRONG/MODERATE/WEAK/DYSFUNCTIONAL/BROKEN), stage scores, binding constraints (DEMAND/CAPACITY/CREDIT_QUALITY/FUNDING/MMF_COMPETITION), leading indicators, recovery timeline estimates, BTC correlation metrics.

The "lead" in "Transmission Lead": The chart calculates a lead indicator by normalizing and inverting the bills-to-lending ratio, then shifting it forward by approximately 13 weeks. This creates a leading signal for BTC price, when credit transmission improves, BTC tends to follow with a lag.

Why it matters in the chain: This is the diagnostic engine of the entire system. When other charts show liquidity expanding (GLI rising) but BTC is not responding, the Transmission Tracker explains why, maybe credit is getting stuck at Stage 4 (banks are not lending despite having reserves) or the binding constraint is MMF competition (money is flowing to money market funds instead of bank deposits).

Connects to everything:

FROM Bucket 1: Treasury maturities, auction outcomes, and collateral multiplier all feed the Stage 1–3 scores
TO CLI (2.2): Transmission velocity and credit conditions are reflected in CLI components (loans, deposits, spreads)
TO GLI (2.3): Effective credit transmission shows up as GLI expansion via bank credit and M2 growth
TO BTC price: Direct BTC correlation metrics with forward-looking velocity

Data dependencies: 26 FRED series covering: Treasury bills, TGA, MMF assets, bank Treasury holdings, RRP, commercial paper, SOFR, Fed repo, C&I loans, total bank credit, real estate loans, consumer credit, deposits, H.8 borrowing, delinquencies, loan demand survey, lending standards, M2, M2 velocity, GDP, Fed funds, 3M yield, 10Y yield, HY spread, TED spread, VIX, BTC price.

2.2 Credit Liquidity Index (CLI)

Method: credit_liquidity_index

What it does: Aggregates 11 credit-specific liquidity indicators into a single weighted index with a 6-day lead on BTC price. While the Transmission Tracker diagnoses the plumbing, the CLI distills the output of that plumbing into one number.

Components (with static weights):

Treasury yields and term spreads
Bank deposits and lending volumes
Credit spreads (high yield, investment grade)
Money market conditions
Interbank lending indicators

Each component is standardized (z-scored) against a rolling baseline, then weighted. The result captures whether credit conditions are easing or tightening relative to recent history. Rate-of-change metrics (1d, 8d, 30d) as Z-scores show acceleration, ±1 is normal, ±2 is significant, ±3 is extreme.

Why it matters in the chain: The CLI is the "credit-specific" companion to the broader GLI. While GLI captures total system liquidity (including Fed balance sheet, RRP), the CLI focuses specifically on credit market conditions, how easy is it for borrowers to access capital? This distinction matters because:

GLI can expand (Fed adding reserves) while CLI contracts (banks tightening lending standards)
The divergence between GLI and CLI signals where in the transmission chain the bottleneck is
CLI's 6-day lead on BTC makes it a shorter-term actionable signal compared to GLI's 7-day lead

Connects forward to:

GLI (2.3), some CLI components (bank credit, spreads) also feed into GLI; divergence between CLI and GLI is analytically significant
Market Regime (6.2), credit conditions influence bull/bear probability
BTC Fair Value (4.1), CLI expansion supports higher fair value via the liquidity channel

Connects back to:

Transmission Tracker (2.1), the CLI reflects the outcome of credit transmission; if transmission is BROKEN, CLI contracts
Collateral Multiplier (1.3), collateral velocity amplifies credit conditions measured by CLI
Treasury Auction (1.2), auction stress widens spreads, lowering CLI

2.3 Global Liquidity Index (GLI)

Method: gli

What it does: The broadest measure of system liquidity on the dashboard. Aggregates 15 macro liquidity indicators into a single weighted index with a 7-day lead on BTC. This captures everything from central bank balance sheets to credit conditions to money supply dynamics.

Components (15 FRED series, weighted):

Fed balance sheet (positive weight, expansion = more liquidity)
Reverse repo facility (negative weight, high RRP = liquidity locked away)
Treasury yields (various tenors)
Credit spreads (HY, IG)
Bank credit and lending
M2 money supply
Interbank rates

Key outputs: GLI_weighted (the composite index), GLI_ROC_1d/8d/30d (rate-of-change Z-scores), plus crypto prices (BTC, ETH, SOL, BNB, XMR).

Why it matters as the anchor: GLI is the central metric of the entire dashboard. It is the single most important indicator because:

It is the broadest measure, captures Fed policy, credit conditions, and money supply simultaneously
It feeds directly into the BTC Fair Value model (Bucket 4)
Its relationship with DXY (Bucket 3) confirms or contradicts the dollar regime
Multiple other charts reference GLI as a dependency (BTC GLI Fair Value, GLI vs DXY, Market Regime)

The 7-day lead means GLI moves before BTC, making it predictive rather than reactive.

Connects forward to:

BTC GLI Fair Value (4.1), GLI is the primary input for the fair value regression model
GLI vs DXY (3.1), correlation analysis to confirm/contradict dollar trends
Market Regime (6.2), GLI momentum is one of four bull/bear probability inputs

Connects back to:

CLI (2.2), credit-specific components contribute to GLI; CLI is a "zoom in" on the credit portion of GLI
Transmission Tracker (2.1), effective credit transmission = GLI expansion via bank credit and M2
Collateral Multiplier (1.3), higher collateral velocity amplifies GLI through money multiplier effects

Bucket 2 Internal Flow

Transmission Tracker
  "Is liquidity flowing through the 5 stages?"
        ↓ (credit conditions, lending, deposits, spreads)
Credit Liquidity Index (CLI)
  "What's the composite credit environment? (6-day lead)"
        ↓ (credit components feed into broader liquidity)
Global Liquidity Index (GLI)
  "What's the total system liquidity? (7-day lead)"
        ↓ (feeds valuation, regime, and dollar analysis)

How Buckets 1 & 2 Connect: The Full Pipeline

Treasury Maturity Tracker → auction volumes → Treasury Auction Demand
        ↓                                              ↓
  Bills outstanding ────→ Transmission Tracker ←── auction stress → MOVE → Collateral Multiplier
        ↓                        ↓                                           ↓
  Bills/lending velocity    Stage scores           Collateral velocity amplifies credit
        ↓                        ↓                                           ↓
  Leading indicator ───→ CLI (credit-specific) ──────────────────→ GLI (total system)
        ↓                        ↓                                      ↓
  BTC correlation         6-day BTC lead                         7-day BTC lead

BUCKET 3: Dollar & Global Liquidity Context

What this bucket answers: "Is the dollar environment supporting or fighting the liquidity expansion?"

Charts in This Bucket

3.1 GLI vs DXY

Method: gli_vs_dxy

What it does: Plots the Global Liquidity Index against the US Dollar Index (DXY) and calculates their rolling 30-day correlation. The relationship is typically inverse, when global liquidity expands, the dollar weakens, and vice versa.

Key outputs: GLI value, DXY value, 30-day rolling correlation, 7-day momentum for both, correlation status (strong_positive/positive/neutral/negative/strong_negative).

Why it matters: The GLI-DXY relationship is a confirmation or divergence signal:

Normal state (negative correlation): GLI up + DXY down = liquidity expansion confirmed by dollar weakness. This is the "everything rally" environment where BTC, gold, and risk assets all benefit.
Divergence (positive correlation): GLI up + DXY up = something unusual is happening. Possible causes: dollar-denominated liquidity expanding faster than global liquidity, flight to safety with simultaneous monetary expansion, or technical dislocations.
Double negative: GLI down + DXY up = worst environment for risk assets. Liquidity contracting + dollar strengthening = double headwind.

Connects to:

FROM GLI (2.3), direct dependency; requires GLI results
TO Capital Allocation (7.3), dollar strength affects BTC vs gold vs SPX relative strength
TO Carry Trade (5.3), DXY strength/weakness is linked to USD/JPY carry dynamics
TO Macro Regime (5.1), dollar environment contextualizes regime probabilities

BUCKET 4: Valuation Models

What this bucket answers: "What should BTC be worth right now, based on liquidity and gold?"

These are the dashboard's two independent valuation frameworks. They approach BTC fair value from different angles, one uses liquidity (GLI), the other uses gold as a leading indicator. When they agree, conviction is high.

Charts in This Bucket

4.1 BTC Fair Value Model (GLI-Based)

Method: btc_gli_fair_value

What it does: Uses the Global Liquidity Index to derive a "fair value" for BTC with standard deviation bands. It normalizes GLI to the BTC price scale using z-scores, then calculates rolling deviation and confidence bands.

Key outputs: Fair value price, current deviation from fair value (%), ±1σ bands (60-day lookback), ±2σ bands, rolling correlation between GLI and BTC.

How it connects to the pipeline: This is where the entire liquidity pipeline (Buckets 1–2) translates into an actionable price target:

Treasury refinancing → credit transmission → CLI → GLI → Fair Value
If BTC is trading below the lower 1σ band, the model says BTC is undervalued relative to system liquidity
If BTC is above the upper 1σ band, liquidity does not justify the price, it is running on momentum/speculation

Dashboard reading (from screenshots): BTC price was around $88K–$95K range, with fair value bands between approximately $80K–$140K (1σ range approximately $98,708–$133,919). BTC trading below fair value = undervalued relative to liquidity.

Connects back to:

GLI (2.3), direct dependency; GLI is the input
CLI (2.2), CLI expansion supports GLI expansion, which raises fair value
Transmission Tracker (2.1), working transmission = higher GLI = higher fair value

4.2 Gold-Based BTC Fair Value

Method: gold_fair_value

What it does: Uses gold prices as a 60-day leading indicator for BTC. Runs a linear regression (BTC = slope × gold + intercept) trained on historical data, then projects BTC's "gold-implied" fair value 60 days forward using current gold prices.

Key outputs: Current predicted BTC price, premium/discount percentage (negative = BTC undervalued vs gold), 60-day forward projection, confidence interval (±1.96 × residual std), R² score, correlation, percentile rankings.

Dashboard reading: Projected BTC (60d) = $116,313. 1-SD range = $98,708–$133,919. 95% confidence interval = $81,807–$150,820.

Why gold leads BTC: Gold and BTC both respond to monetary debasement expectations, but gold moves first because:

Gold is a deeper, more institutional market, it prices in macro shifts faster
Gold has lower volatility, so smart money rotates there first
BTC follows 60–65 days later as the narrative catches up and retail/institutional crypto allocators respond

Connects to:

Gold vs BTC (4.3), sister chart; Gold vs BTC shows the ratio, Gold Fair Value shows the regression model
Capital Allocation Tracker (7.3), gold relative strength vs BTC is tracked there; when gold leads, it confirms the 60-day lead thesis
GLI (2.3), both gold and GLI respond to monetary conditions; agreement between gold-implied and GLI-implied fair value is a strong confirmation signal

4.3 Gold vs BTC (Gold-Based BTC Fair Value Chart's Companion)

Method: gold_vs_btc

What it does: Tracks the gold/BTC ratio with a configurable lead (65 days default), showing gold prices shifted forward to visualize the leading relationship. Calculates rolling correlation (30-day window) and ratio percentiles.

Key outputs: BTC price, gold price, gold/BTC ratio, average/max/min ratio, ratio percentile, rolling correlation, projected future BTC based on current gold.

Why this exists alongside Gold Fair Value: Gold vs BTC shows the relationship dynamics (ratio, correlation, divergences) while Gold Fair Value shows the regression model (predicted price, confidence intervals). Together they answer: "Is the gold-BTC relationship currently reliable (Gold vs BTC correlation), and if so, what does it predict (Gold Fair Value)?"

Bucket 4 Internal Flow

GLI (from Bucket 2)                    Gold Price
        ↓                                  ↓
BTC GLI Fair Value                  Gold vs BTC (ratio/correlation)
  "What should BTC be                      ↓
   worth per liquidity?"            Gold Fair Value (regression model)
        ↓                            "What should BTC be worth
        ↓                             per gold's 60-day lead?"
        ↓                                  ↓
        └───────── CONVERGENCE ────────────┘
              When both models agree,
              conviction is highest

BUCKET 5: Macro Regime & Risk Environment

What this bucket answers: "What is the macro environment doing, and what risks could derail the liquidity thesis?"

This bucket provides the context layer. Buckets 1–4 track the liquidity pipeline and derive fair values. Bucket 5 checks whether the macro environment supports or threatens that pipeline. A positive CLI/GLI setup can be invalidated by a carry trade unwind or a sudden shift to QT.

Charts in This Bucket

5.1 Macro Regime Probabilities

Method: macro_regime

What it does: Calculates the probability of 7 different macro regimes occurring within 3 months, using 40 FRED economic indicators across 5 categories. The regimes are: QE, QT, Rate Hike, Rate Cut, Treasury QE, Recession, Market Stress.

Key outputs: Per-regime probability (0–100%), 30-day change, 90-day change, trend direction (rising/falling/neutral).

Dashboard reading:

Market Stress: 98.7% (↑36.9% over 90d), dominant signal
Rate Cut: 79.6%
QE: 66.5%
Recession: 55.6% (↑4.8% over 90d)
Treasury QE: 44.4% (↓4.8% over 90d)
QT: 33.5%
Rate Hike: 20.4%

Logic details:

Competitive pairs normalize (QE vs QT, Rate Hike vs Rate Cut, Treasury QE vs Recession)
Logical constraints: QE dominance (above 50%) suppresses rate hike probability; Treasury QE (above 55%) dampens recession probability
Each indicator: 67% or more alignment in direction required, graduated scoring based on 30d % change vs threshold

Why it matters in the chain: Macro Regime is the scenario planner. It tells you:

If QE probability is rising, expect GLI expansion → higher BTC fair value
If Market Stress is dominant (as now), expect volatility → lower collateral multiplier → CLI/GLI headwinds
If Rate Cut probability is high, the yield curve will steepen → this affects CLI components and carry trade dynamics
If Recession probability rises, lending contracts → Transmission Tracker Stage 4 weakens

Connects to:

Transmission Tracker (2.1), rate cuts → easier lending standards → better transmission
CLI/GLI (2.2/2.3), QE/QT regime directly determines whether liquidity indices expand or contract
Carry Trade Monitor (5.3), rate differentials are core to both
Treasury Auction (1.2), QE/QT determines Fed as buyer/seller at auctions
HODL Index (6.3), macro regime feeds into risk-off signals

5.2 Truflation (Macro Regime Probabilities, Inflation Component)

Method: truflation

What it does: A composite inflation gauge that tracks 21 CPI/PPI/wage/commodity FRED series to produce two proprietary gauges plus all standard inflation measures. This is the inflation context for the macro regime.

Key outputs:

Official measures: CPI YoY (2.13%), Core CPI (2.18%), PCE YoY (2.12%)
Trueflation composite: 1.58% (below official CPI)
3M change: -0.64%
Fed 2% target differential: -0.42% (below target)
Two composite gauges:
- Policy Anchor Gauge: 65% core PCE + 20% rent + 7.5% energy + 5% food + 2.5% median CPI
- Trueflation Gauge: 40% rent + 15% food + 10% each (gas, medical, services, wages, energy)

Why it matters: Inflation is the gatekeeper for Fed policy. Everything in the liquidity pipeline (Buckets 1–2) is influenced by what the Fed does, and what the Fed does depends on inflation:

Trueflation below target (as now at -0.42% differential) = green light for rate cuts = supports Macro Regime's 79.6% Rate Cut probability
If Trueflation were rising above target, it would raise Rate Hike probability and suppress QE probability
The gap between Trueflation (1.58%) and official CPI (2.13%) suggests real-time inflation is lower than lagging official figures, the Fed may be behind in cutting

Connects to:

Macro Regime (5.1), inflation directly determines Rate Hike vs Rate Cut probabilities
CLI (2.2), lower inflation → lower rates → tighter spreads → higher CLI
Carry Trade (5.3), inflation differentials between US and Japan affect carry dynamics

5.3 Carry Trade Monitor (USD/JPY Carry Trade Risk)

Method: carry_trade_monitor

What it does: Monitors the risk of a USD/JPY carry trade unwind, one of the most destabilizing events for global markets. Scores 6 risk components (0–100 scale) covering rate differentials, Fed liquidity, JPY strength, and volatility.

6 Scoring Components:

Differential level (0–30 pts): 10Y spread US vs Japan
Differential ROC (0–25 pts): Rate of change of the spread
Fed liquidity (0–20 pts): QT-related indicators
Fed policy (0–15 pts): Rate change direction
USDJPY movement (0–40 pts): JPY strengthening with intervention thresholds
VIX (0–30 pts): Volatility spike detection

Risk levels: EXTREME (80+), HIGH (60–79), MODERATE (40–59), LOW-MODERATE (20–39), LOW (under 20)

Dashboard reading: Risk score = 57, Level = MODERATE, Rate diff = 1.89%

Why it matters as a risk overlay: The carry trade is the hidden landmine in global markets. Trillions of dollars are borrowed in low-rate JPY and invested in higher-yielding assets (including US Treasuries and risk assets). If the trade unwinds:

JPY strengthens violently
Leveraged positions are liquidated
MOVE index spikes → Collateral Multiplier collapses
Treasury auction demand drops (foreign buyers flee)
VIX spikes → HODL Index triggers EXIT
All liquidity metrics (CLI, GLI) get disrupted

Connects to:

Collateral Multiplier (1.3), carry unwind → MOVE spike → multiplier collapse
Treasury Auction (1.2), carry trade unwinding reduces foreign auction demand
GLI vs DXY (3.1), carry dynamics affect dollar strength
HODL Index (6.3), VIX component in carry trade feeds HODL's crash detector
Market State (6.1), carry unwind creates "volatile trending" states

Bucket 5 Internal Flow

Truflation
  "What's real inflation doing?"
        ↓ (inflation determines Fed policy path)
Macro Regime Probabilities
  "What regime are we in / heading toward?"
        ↓ (regime context for all other buckets)
        ↓
Carry Trade Monitor
  "Is the JPY carry trade at risk of unwinding?"
  (risk overlay that can override all positive signals)

BUCKET 6: Market Structure & Cycle Position

What this bucket answers: "Where are we in the market cycle, and what is the current character of price action?"

This bucket synthesizes the outputs of the upstream buckets into market characterization and cycle positioning. It is where the macro/liquidity analysis meets price action reality.

Charts in This Bucket

6.1 Market State

Method: market_state

What it does: Classifies the current BTC market into one of four quadrants based on two dimensions: volatility (high/low) and trend strength (trending/ranging).

Calculation:

Volatility: Annualized standard deviation of daily returns (30-day rolling)
Trend Strength (Efficiency Ratio): |price change over period| / sum of |daily changes|. 0 = pure ranging, 1 = pure trending.
Thresholds: Each dimension compared to its rolling mean × 1.2

Four states: Volatile Trending, Volatile Ranging, Calm Trending, Calm Ranging

Dashboard reading: Current = Calm Ranging (Volatility 46.3%, Trend Strength 5.3%)

Why it matters: Market State tells you how to trade, not what direction:

Calm Ranging = mean-reversion strategies work, breakout strategies fail
Volatile Trending = momentum strategies work, mean-reversion gets killed
Calm Trending = best for systematic accumulation (DCA)
Volatile Ranging = worst environment, high chop

Connects to:

VAMS Mid TF (8.1), VAMS adapts its signals based on volatility regime
Market Regime (6.2), state provides the "how" while regime provides the "where"
HODL Index (6.3), volatility feeds into VIX crash detector component
Leverage Risk Gauge (7.4), volatility scoring uses similar upside/downside decomposition

6.2 Market Regime

Method: market_regime

What it does: Classifies the market as Bull, Bear, or Neutral using technical indicators enhanced with GLI momentum. Calculates probability distribution across regimes.

4 Signal Components (25% each):

RSI: above 60 = bull, below 40 = bear
MACD: Above signal + positive = bull, below signal + negative = bear
SMA Structure: SMA_20

Quick Navigation

How to Read This Document

Master Flow Summary

BUCKET 1: Sovereign Debt & Refinancing Pipeline

Charts in This Bucket

1.1 Treasury Maturity Tracker

1.2 Treasury Auction Demand

1.3 Collateral Multiplier vs MOVE

Bucket 1 Internal Flow

BUCKET 2: Credit Transmission & Liquidity Pipeline

Charts in This Bucket

2.1 Transmission Tracker (Transmission Lead)

2.2 Credit Liquidity Index (CLI)

2.3 Global Liquidity Index (GLI)

Bucket 2 Internal Flow

How Buckets 1 & 2 Connect: The Full Pipeline

BUCKET 3: Dollar & Global Liquidity Context

Charts in This Bucket

3.1 GLI vs DXY

BUCKET 4: Valuation Models

Charts in This Bucket

4.1 BTC Fair Value Model (GLI-Based)

4.2 Gold-Based BTC Fair Value

4.3 Gold vs BTC (Gold-Based BTC Fair Value Chart's Companion)

Bucket 4 Internal Flow

BUCKET 5: Macro Regime & Risk Environment

Charts in This Bucket

5.1 Macro Regime Probabilities

5.2 Truflation (Macro Regime Probabilities, Inflation Component)

5.3 Carry Trade Monitor (USD/JPY Carry Trade Risk)

Bucket 5 Internal Flow

BUCKET 6: Market Structure & Cycle Position

Charts in This Bucket

6.1 Market State

6.2 Market Regime