Glossary: the terms BlockPhi, an investment analytics firm, uses in its Global Liquidity and Bitcoin research

Global Liquidity and macro measures#

BIS global liquidity indicators#

The BIS global liquidity indicators are the Bank for International Settlements' official measure of global liquidity, tracking credit to non-bank borrowers through cross-border bank loans and international debt securities. They are a credit aggregate, explicitly not a money-supply measure and not a central bank balance sheet measure, and they are published quarterly. BlockPhi does not use them as its series.

Source: Bank for International Settlements, global liquidity indicators, https://www.bis.org/statistics/dataportal/gli.htm

BlockPhi Global Liquidity Index (BlockPhi's usage)#

The BlockPhi Global Liquidity Index is BlockPhi's proprietary composite of central bank balance sheets, private sector credit, cross-border capital flows and short-term credit spreads, used as the macro input to its Bitcoin valuation and regime work. It is BlockPhi's own construction rather than an industry standard, and readings taken on it are not directly comparable with global M2, with the BIS global liquidity indicators, or with CrossBorder Capital's index.

DXY#

DXY is the US Dollar Index, which measures the dollar against a basket of major currencies. It matters to liquidity work because the dollar sets the price of cross-border funding: a stronger dollar tightens global financial conditions and a weaker dollar loosens them. BlockPhi charts it inverted against Bitcoin, with both series expressed as z-scores.

Global Liquidity#

Source: Bank for International Settlements, global liquidity indicators, https://www.bis.org/statistics/dataportal/gli.htm

Global M2#

Global M2 is the sum of national broad money supplies, usually converted into US dollars, and it is the most common liquidity proxy in crypto charts. Country coverage differs between publishers, so two charts both labelled global M2 can differ materially, and the dollar conversion means part of the series measures the dollar itself.

Liquidity pipeline (BlockPhi's usage)#

The liquidity pipeline is BlockPhi's term for liquidity that has already been observed and is still feeding through to price over the coming weeks. Because a measured change in liquidity precedes the associated move in price, part of the near-term path is set by data already in hand rather than by a forecast of new data.

Net liquidity#

Net liquidity is a United States measure, normally the Federal Reserve's balance sheet less the Treasury General Account and reverse repo balances, describing the dollar reserves actually available to markets. It is daily and freely published, which is why it is widely used, but it is domestic rather than global, so it answers a narrower question.

Regime state (BlockPhi's usage)#

A regime state is BlockPhi's classification of the liquidity environment in a given week, using three labels: Withdrawal, read as tightening; Normal Injection, read as neutral; and Massive Injection, read as expansion. The label describes measured conditions on BlockPhi's own series, and it is a description rather than an instruction to anyone.

Transmission lag (BlockPhi's usage)#

The transmission lag is the interval between a change in a liquidity series and the associated response in Bitcoin's price. BlockPhi estimates it with a cross-correlation scan run on rate-of-change series, and re-estimates it rather than treating it as fixed, because published research consistently finds that the optimal lag moves over time.

Model outputs and valuation#

As-of date#

An as-of date is the date on which the inputs behind a figure were last observed, which is not the same as the date a page was published. BlockPhi frameworks and monitor readings carry one so a reader can judge how current a number is. A figure with no as-of date cannot be assessed for staleness.

Fair value band#

A fair value band is the range drawn around a model's central fair-value estimate, marked at standard-deviation intervals so a current price can be read as stretched or depressed relative to what the model supports. BlockPhi derives its band from a fit against its Global Liquidity Index. A band is a model estimate. Past results do not indicate future results.

Global Liquidity × Bitcoin Monitor (BlockPhi's usage)#

The Global Liquidity × Bitcoin Monitor is BlockPhi's member-facing dashboard, reading the Global Liquidity level and its rate of change, the liquidity already feeding through to price, model fair value against the current Bitcoin price, and a forward distribution conditioned on observed liquidity. Every reading is a model estimate carrying its own as-of date. Past results do not indicate future results.

Model estimate#

A model estimate is a number a model produces from stated inputs and assumptions on a stated date, as distinct from a measurement or a promise. It should be read together with its as-of date, which tells a reader how old the input data is, and it can be revised when the inputs are revised. Past results do not indicate future results.

Quantile fan#

A quantile fan is a forecast display showing a distribution rather than a single path: bands running from a low to a high percentile of historical outcomes, conditioned on a stated input. BlockPhi conditions its fan on the recent change in its Global Liquidity Index. A fan is a model estimate of a range. Past results do not indicate future results.

Statistical method#

ARIMAX#

ARIMAX is a time-series regression that models a variable from its own past values, its own past errors, and one or more external inputs. Modelling an asset's own momentum first matters because any explanatory power left over then sits with the external input rather than with the asset's inertia. BlockPhi uses it to isolate the liquidity term.

Cross-correlation#

Cross-correlation measures how strongly two series move together when one is shifted forward or backward in time. Scanning a range of shifts and taking the strongest reading identifies the lag at which one series best lines up with the other. It measures association rather than cause, and the strongest lag can change as the sample changes.

Drawdown#

Drawdown is the decline from a strategy's or an asset's highest recorded value to its subsequent lowest point, expressed as a percentage, and maximum drawdown is the largest such decline over a stated period. It describes the depth of a losing stretch rather than how long the stretch lasted.

Granger causality#

Granger causality is a statistical test of whether the past values of one series improve forecasts of another beyond what that second series' own history already provides. It establishes predictive precedence rather than causation, and it is directional: a test can find that liquidity Granger-causes Bitcoin while the reverse test finds nothing at all.

Rate of change#

Rate of change is the percentage difference in a series over a fixed window, for example six weeks. Comparing rates of change rather than levels removes the shared upward trend in two growing series, which otherwise produces a high correlation that says very little. Correlations measured on rates of change are usually much lower, and more honest.

Z-score#

A z-score expresses a value as the number of standard deviations it sits from its own mean, which puts series measured in different units onto a common scale. In BlockPhi's charts, a Bitcoin deviation and a liquidity series are compared as z-scores, with bands drawn at two standard deviations to mark unusual readings.

Bitcoin and on-chain measures#

MVRV#

MVRV, or market value to realised value, is the ratio of Bitcoin's market capitalisation to its realised capitalisation: the price the market pays against the aggregate price at which existing coins last moved. The MVRV Z-score standardises that gap. It is a widely used on-chain valuation gauge and is not proprietary to any firm.

Source: Glassnode, MVRV Z-score, https://studio.glassnode.com/charts/market.MvrvZScore?a=BTC

NVT#

NVT, the network value to transactions ratio, divides Bitcoin's market capitalisation by the value settled on its network over a period, as a rough analogue of a price-to-earnings ratio. Introduced by Willy Woo in 2017, it is an industry-standard on-chain metric rather than a BlockPhi construction.

On-chain cost basis#

On-chain cost basis is the price at which a coin, or a defined group of coins, last changed hands on the blockchain, aggregated across the whole supply or across a cohort of it. Because Bitcoin's ledger is public, this can be measured directly rather than surveyed, which is what separates on-chain valuation measures from sentiment measures.

Realised price#

Realised price is Bitcoin's realised capitalisation divided by its circulating supply: the average price at which the existing coins last moved on-chain. It is read as an aggregate cost basis for the network, which is why the current price relative to it is treated as a valuation reference rather than as a target.

BlockPhi research surfaces#

Allocation book (BlockPhi's usage)#

An allocation book is an analyst's stated portfolio: each holding, its asset class and its weight, set beside the other analysts' books so a reader can see where views agree and where they diverge. Weights are self-reported by the publishing analyst and change without notice. A book is a disclosure of positioning, not a recommendation.

Decision matrix (BlockPhi's usage)#

A decision matrix is the framework type that scores several weighted inputs as bullish, neutral or bearish and rolls them into a single weighted signal balance. It is BlockPhi's default framework form, used for house models including the Bitcoin Valuation Model and the Altseason Investment Decision Matrix. The balance describes a lean, not an action.

Forward-verified equity curve (BlockPhi's usage)#

A forward-verified equity curve is a record of a system's results accrued from its publication date onward, rather than a backtest fitted to history once the outcome is already known. It removes hindsight from the rule design, but it is not an audit: figures on BlockPhi member surfaces are self-reported unless marked Verified. Past results do not indicate future results.

Framework (BlockPhi's usage)#

A framework, in BlockPhi's usage, is a weighted table of the inputs behind a view: each criterion, its weight, where it reads today, and why it matters, rolling up to a signal balance. A framework carries an as-of date and explains a position rather than instructing one.

Positioning views (BlockPhi's usage)#

Positioning views is the framework type that records an overweight, neutral or underweight stance per asset, with a conviction level and a one-line rationale, netting to an overall stance. It describes how one analyst is positioned relative to a stated benchmark. It is a record of that analyst's stance, not guidance for a reader.

Risk limits (BlockPhi's usage)#

Risk limits is the framework type in which an analyst publishes their own rules, such as drawdown caps, per-position risk and exposure ceilings, with the current reading against each rule and the action a breach forces on them. It describes the publishing analyst's own discipline rather than a rule set for the reader.

Scenario outlook (BlockPhi's usage)#

A scenario outlook is the framework type that sets out bull, base and bear paths with probabilities totalling one hundred, a key assumption for each path, and the condition that would invalidate it. Stating the invalidation is the point, because it makes the view falsifiable. Probabilities are model estimates on a stated date.

System (BlockPhi's usage)#

A system, in BlockPhi's usage, is a rule-based strategy an analyst runs and reports, with a published allocation and an equity curve tracked from publication onward. Full-access members can follow a system and receive an email when its published allocation changes. A system is a record of what one analyst does, not an instruction.

Trade journal (BlockPhi's usage)#

A trade journal is the running ledger an analyst keeps of open and closed positions, each entry carrying the thesis that opened it, the levels behind it, and how it ended, losing positions included. Publishing the closed losers alongside the winners is what makes a record readable as evidence rather than as marketing.

Trigger checklist (BlockPhi's usage)#

A trigger checklist is the framework type that lists indicators, each with a defined trigger level, and reports how many have fired. It answers how close conditions sit to a stated threshold rather than what anyone should do about it. Cycle-top dashboards are the common form of it.