SatoshiMacro Model (SMM)

Tier 1 (Valuation) and Tier 2 (Miner) are live. Tiers 3 (Sentiment), 4 (Rotation), and 5 (Macro) ship in subsequent updates and will further refine the composite.

For decades, the analytical frameworks institutions used to read crypto cycles sat behind paywalls, Bloomberg terminals, and prop-desk-only access. Retail got the leftovers: surface-level metrics, equal-weighted composite scores, and a steady stream of paid courses pitching what professional desks already had for free.

I traded allocated institutional capital at a Sydney proprietary trading firm. I saw the asymmetry from the inside. Professional desks were not running 9-indicator equal-weight composites. They were running weighted confluence models calibrated to historical signal quality, layering on-chain valuation with derivatives positioning, sentiment extremes, rotation flows, miner stress, and macro context. The retail tools never caught up because the gap was the business model.

The SatoshiMacro Model is what that gap actually looks like, published free. 48 signals across 6 weighted tiers - cycle-position valuation, mass psychology, miner economics, sentiment & positioning, institutional rotation flow, and macro context - calibrated against three completed Bitcoin cycles, with every input value, normalized score, tier weight, and historical reading disclosed transparently on this page. No paywall. No signup. No funnel into a course at the end. This is the tool. It costs nothing because the institutional playbook should not cost anything to use.

For years, institutional traders had access to information retail never saw. That asymmetry is finally breaking. SatoshiMacro exists to hand you the analytical playbook, and the SatoshiMacro Model is the first piece of it. Govind Satoshi · Former Institutional Trader · Founder, SatoshiMacro

More on the background, methodology, and why this is published free on the About page.

The SatoshiMacro Model is a composite cycle confluence model built from up to 48 on-chain, derivatives, sentiment, mass-psychology, and macro signals. Each signal is computed at every historical date back to January 2013, normalized to its own historical distribution (0-100 percentile rank or calibrated mapping), and aggregated into six tier scores. The six tier scores are then weight-summed to produce the final SMM score. Every constituent signal also lives as a standalone tool in the Bitcoin & Crypto Charts Dashboard if you want to drill into any individual signal.

Why a confluence model?

No single indicator calls cycle tops reliably. The 2017 top was best called by Pi Cycle. The 2021 first peak was best called by MVRV. The 2018 bottom was best called by Puell. A model that averages multiple signals captures the strengths of each while smoothing out their individual failure modes. CBBI pioneered this approach with 9 indicators; SMM extends it to 29 with proper tier weighting.

Tier structure

Indicators are grouped into six tiers based on what they measure, then weighted by how predictive each tier's signal class has been across the 2013, 2017, and 2021 cycle inflections:

• Tier 0: Cycle Timing & Mass Psychology (30%). The highest-weight tier because the 4-year halving cycle is Bitcoin's single most reliable cyclical pattern. Plus mass-psychology signals: drawdown from all-time high, days since ATH, and a rolling profitable-days proxy. The 4-year cycle component is a piecewise function calibrated to historical peaks (~488-549 days post-halving) and troughs (~790-924 days post-halving) across all three completed cycles.
• Tier 1: Valuation & Cycle Position (25%). The gold-standard cycle-timing metrics. MVRV Z-Score, Pi Cycle Top, Mayer Multiple, 2-Year MA Multiplier, 200-Week MA distance, Power Law deviation, Golden Ratio Multiplier, Rainbow Chart position, Bitcoin Risk Metric. Each has called a prior cycle top within 60 days when registering extreme readings.
• Tier 2: Sentiment & Positioning (20%). Crowd sentiment runs hot before price tops. Derivatives crowding amplifies. Fear & Greed, Google Trends (Bitcoin / buy Bitcoin / Bitcoin tax), Funding rate aggregate, Open Interest normalized, Coinbase Premium, Deribit Put/Call ratio, Futures basis 3M. Implementation in Phase 1.C.
• Tier 3: Rotation & Institutional Flow (10%). The "everyone's in" signal. BTC Dominance, Altcoin Season Index, Spot ETF cumulative holdings, ETF daily flows, MicroStrategy / Strategy BTC accumulation rate, Corporate Treasury growth. Implementation in Phase 1.D.
• Tier 4: Miner & Production Stress (10%). Miner economics distort near cycle extremes. Puell Multiple, Hash Ribbons state, Miner Fee Share %, Stock-to-Flow deviation.
• Tier 5: Macro Headwinds/Tailwinds (5%). Cross-asset macro context. USD Index, M2 growth YoY, Yield curve inversion, VIX level. Implementation in Phase 1.E.

Normalization

Each indicator is normalized to its own historical percentile rank (0-100). This means a Mayer Multiple of 1.5 doesn't feed in as "1.5". It feeds in as "the 78th percentile of all historical Mayer Multiple readings," making it directly comparable to a Fear & Greed value of 65 (the 78th percentile of its own distribution). This is the only mathematically sound way to combine indicators that live on different scales.

Calibration curve

The raw weighted composite of 48 signals naturally caps around 65-78 at historical cycle tops because diversifying signals (sentiment, macro, rotation) don't all peak at the same moment as cycle-position valuation - the weighted average gets diluted by signals that haven't fired yet. CBBI doesn't have this dilution because it uses only 9 indicators, all highly cycle-correlated. SMM keeps the 48-signal diversity (more robust against false signals and single-source outages) and applies a calibration curve on the final composite to stretch the upper half of the 0-100 scale.

The calibration is piecewise:

• Raw 0-40 (identity): bottoms pass through unchanged. 2018-12 raw 30 stays at 30 (Accumulation), 2022-11 raw 24 stays at 24 (Accumulation).
• Raw 40-60 (slope 1.5): gentle mid-cycle stretch. Raw 50 maps to 55, raw 60 maps to 70.
• Raw 60-70 (slope 2.0): steeper late-cycle stretch. Raw 65 maps to 80, raw 70 maps to 90.
• Raw 70-80 (slope 1.0): top-zone capping. Raw 75 maps to 95, raw 80 caps to 100.
• Raw 80+ (clamp): stays at 100.

Calibrated readings at historical cycle tops (48-signal version): 2013-12 (98.8), 2017-12 (100), 2021-04 (94.7), 2021-11 (85.1). All four register in the Cycle Top zone (85-100), matching the visual benchmark CBBI sets at major tops. The raw pre-calibration score is also emitted as raw_smm in the output JSON so consumers can compare or recalibrate if needed.

Zone interpretation

• 0-15 Deep Value. Historical cycle-bottom zone. Past readings here: 2022-11 (SMM 15.0).
• 15-30 Accumulation. Favourable risk/reward. Below-trend valuation.
• 30-50 Neutral. Mid-cycle. No strong directional edge.
• 50-70 Caution. Late-mid cycle. Begin de-risking on strength.
• 70-85 Distribution. Historical late-cycle zone. Trim aggressively. Past readings here typically occur in the 6-12 months bracketing major cycle tops.
• 85-100 Cycle Top. Historical top zone. Maximum caution. Calibrated readings (48-signal version): 2013-12 (SMM 98.8), 2017-12 (SMM 100), 2021-04 (SMM 94.7), 2021-11 (SMM 85.1). All four major BTC cycle tops register in this zone after the May 2026 calibration curve was applied.

Caveats and limitations

• SMM starts at January 2013, not Bitcoin's genesis. Pre-2013 Bitcoin was in raw price discovery: thin liquidity, single-venue trading (Mt. Gox), and a market cap measured in tens of millions of USD. Cycle-position metrics computed against that period produce unreliable readings because the "historical distribution" itself is too small and too dominated by a single venue's order book to be meaningful. Same reason the Mayer Multiple and most other cycle indicators are conventionally backtested from 2013 onward, not from the 2009 genesis block. Treat any chart visualisations that extend earlier than January 2013 as price-history context only; the SMM line begins at the first reading where the underlying distribution is broad enough to support meaningful percentile ranks.
• Percentile ranks use expanding-window history. Each historical date's score is computed only against indicator values that existed up to that date - no lookahead bias. Early-period readings reflect what the model would actually have shown at the time, given only the data available then.
• MVRV Z-Score uses CoinMetrics realised-cap data. The standard Awe & Wonder formula Z = (MarketCap - RealizedCap) / stddev(MarketCap) is computed against the CoinMetrics community-api daily series (free, on-chain-derived, the same realised-cap value referenced by Glassnode and LookIntoBitcoin). The stddev is expanding-window so historical readings remain lookahead-free. If the CoinMetrics fetch ever fails on a build, the model gracefully falls back to a 4-year MA proxy and the indicator name on this page is suffixed with "(4Y MA proxy)" so you can tell the difference.
• Indicator coverage varies by date. Earlier periods (pre-2018) have less data from sentiment, derivatives, and macro tiers, so the composite there uses re-normalized weights across only available tiers. This is one reason the 2015-01 bottom reads higher (31.4 Neutral) than later cycle bottoms (15-19 Deep Value/Accumulation).
• This is not financial advice. The SMM is a research tool for cycle position context. It does not predict prices. Cycle indicators have failed before and will fail again. Use as one input among many.

Why this beats CBBI

CBBI uses 9 indicators with equal weight. SMM uses 48 signals across 6 tiers with weights calibrated to each tier's historical signal quality. Equal weighting treats a noisy Google Trends value the same as a precision-instrument like Pi Cycle Top, which dilutes the precision instruments. SMM's tier-weighted approach plus the calibration curve lets high-quality signals dominate at extremes while lower-quality signals still contribute context across the cycle. The 5x larger panel also makes SMM more robust to single-source data outages - any one feed going dark only marginally affects the composite.

How to use it

The SMM is a position-sizing input, not a buy/sell trigger. Sample uses:

• SMM in Deep Value zone (0-15): consider increasing crypto allocation toward your maximum.
• SMM in Distribution zone (70-85): trim positions, raise cash, take profits on strength.
• SMM in Cycle Top zone (85-100): maximum caution. Historical readings in this zone preceded multi-year drawdowns.
• SMM in Neutral zone (30-50): no signal. Manage based on your base strategy.