The Market Framework Model organizes price behavior into structural states that appear across all markets. MFM is a structural model for serious traders who want to understand how the market behaves, not a buy/sell signal tool. This page summarizes the main empirical evidence supporting those structural layers.
MFM does not project future prices or make forward targets. It identifies structural states that increase or decrease probability, not guaranteed outcomes.
Some visualizations on this page represent the complete MFM model design, including layers that will be added to the TradingView build in upcoming releases (such as explicit Deviation overlays and HUD markers).
To provide full transparency, these sections outline how the backtests were performed, what the model does not claim to do, and in which situations MFM may become less reliable.
Backtest methodology
The backtests were performed using identical MFM settings across all assets. No asset-specific tuning, optimization or curve-fitting was applied.All assets were tested with the same configuration to avoid tuning, optimization or asset-specific bias. All results shown on this page reflect raw structural behavior rather than engineered parameters. The study uses multi-year historical datasets for SPX, NVDA, AMZN, Gold, BTC and XRP on the 4H timeframe. Transaction costs, slippage and execution noise were not simulated, as MFM is a structural framework, not a trading system. The purpose of the backtest is not to demonstrate performance. It is to evaluate whether the structural layers of MFM (Regime, Phase, Leadership, MPF and Deviation) behave consistently across different markets and volatility regimes.
Limitations
MFM is a structural model. It describes context, not outcomes, and there are environments where structure becomes unstable or less reliable:
- extreme macro events (CPI, FOMC, policy shocks)
- abnormal volatility expansion
- very low-liquidity markets
- unexpected cross-asset dislocations
During such conditions, Phase fields may rotate faster, Leadership may misalign, and MPF signals may be suppressed or unreliable. This behavior is normal: structural organization decays under stress. Deviation highlights these conditions, but cannot always anticipate abrupt instability.upt instability.
What MFM does not do
- MFM does not predict future prices.
- It does not identify tops or bottoms,
- It does not generate financial advice or guaranteed outcomes.
The framework does not rely on pattern recognition, fixed thresholds, oscillators or conventional TA signals. It organizes price behavior into structural states that can support decision-making, but it should never be used as the sole basis for trading or investment actions.
Users remain fully responsible for their own decisions, and MFM should never be used as the sole basis for trading or investment decisions.
1. Backtest Evidence – Market Framework Model (MFM)
Evidence-based structural behavior across multiple assets and volatility regimes.
The Market Framework Model is built on the idea that markets do not move randomly. They organize into repeatable structural states. The backtests examine whether these structural layers appear consistently across assets such as SPX, NVDA, Gold, BTC and XRP. The goal is not prediction, but to show whether MFM reflects real, observable market structure. This page summarizes the most important findings of the multi-asset backtest study.
Market Framework Model (MFM)
A multi-layer structural framework for market context
| Layer | Meaning |
|---|---|
| Regime | Macrotrend bias via HTF momentum ratio |
| Phase | Internal momentum rotation cycle (MRM) |
| Leadership | Cross-asset relative strength |
| MPF | Probabilistic pattern forecasting (pivot-anchored) |
| Deviation | AI-based anomaly & context Deviation layer |
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2. Phase Behavior Profiles
How market structure organizes into three distinct behavioral fields.
The MFM phase model identifies three structural fields that appear repeatedly across different assets and volatility regimes.
These fields describe how price behaves: independent of forecasting or trading direction.
The backtests confirm that Phase 1, Phase 2 and Phase 3 each express a characteristic behavioral signature that remains stable across instruments such as SPX, NVDA, Gold, BTC and XRP.
Phase 1: Volatile Field
Unstable movement and wide dispersion
Phase 1 reflects a state of instability.
Price tends to show:
- wide swings and sharp reactions
- elevated volatility
- weak directional control
- frequent micro-reversals
This field appears near exhaustion conditions or late-trend behavior.
It is the most unpredictable structural state.
Phase 2: Compression Field
Neutral structure with limited range
Phase 2 forms a neutral zone where momentum oscillates.
Price behavior becomes:
- narrowed in range
- hesitant and inconsistent
- low in directional bias
- structurally compressed
This field often precedes a transition away from the prior trend.
Phase 3: Drift Field
Organized stabilization and positive tendency
Phase 3 shows the most stable and constructive behavior.
Price typically displays:
- smoother, more organized movement
- reduced volatility
- early signs of rebuilding strength
- a positive drift after oversold conditions
This field frequently clusters around market lows.
Summary
Across all tested assets, the three phases emerge as consistent behavioral fields:
- Phase 1 concentrates volatility and structural instability.
- Phase 2 expresses compression and hesitation.
- Phase 3 reflects stabilization and constructive drift.
These fields describe the quality of market behavior (not its long-term direction) and form the behavioral foundation of the MFM phase model.
3. Frequency of Structural States
How often each MFM phase appears across real market conditions.
Markets do not remain in structured cycle zones at all times. The MFM backtests show that the majority of price action occurs in a neutral background state, while the three active phases appear with different frequencies depending on volatility and trend conditions. Across assets such as SPX, NVDA, Gold, BTC and XRP, the distribution of structural states remains remarkably consistent.
No Phase — Neutral Context (most common)
Most of the market’s movement does not occur in an active cycle zone.
During these periods:
- no exhaustion or recovery pressure is active
- directional signals are weak
- price can move but lacks structural definition
This is the natural baseline state of the market.
Phase 2 — Compression Field (most frequent active phase)
Among the defined phases, Phase 2 occurs most often.
It represents:
- structural hesitation
- narrowed ranges
- mixed or shifting control
- increased risk of breakout after compression
Phase 2 anchors most transitional periods in the market.
Phase 1 and Phase 3 — Low Frequency, High Meaning
Both Phase 1 and Phase 3 occur less frequently, but their meaning is substantial:
Phase 1 – Exhaustion / Volatile Field
- uncommon
- typically forms near late-trend conditions
- often precedes sharp reversals or instability
Phase 3 – Drift / Recovery Field
- also relatively rare
- clusters around market lows
- marks early rebuilding of stability
- often precedes upward drift
These two phases capture extreme structural conditions, which is why they appear less often but carry stronger contextual value.
Summary
Across all tested assets:
- No Phase is the dominant background state.
- Phase 2 is the most common active cycle state.
- Phase 1 and Phase 3 occur less frequently but define the most impactful structural turning points.
This distribution supports the idea that MFM phases capture meaningful, non-random structural behavior rather than noise-driven fluctuations.
4. Phase Evidence in Real Markets
How the three structural fields appear across real assets.
The MFM phase structure is not theoretical. It can be observed directly in real market data across different assets, timeframes and volatility regimes. The chart below shows Amazon (AMZN) over a full multi-month period.
Despite varying trend environments, the same structural patterns appear repeatedly:
- MPF signals activate only when the phase context allows it
- Phase 1 clusters near exhaustion conditions
- Phase 2 stabilizes hesitation periods
- Phase 3 forms early recovery zones
AMZN Example — Structural Phase Fields
Figure 4.1 – AMZN (1H) showing the MFM structural fields. Red zones (Phase 1) mark exhaustion and instability. Orange zones (Phase 2) show transitional compression. Green zones (Phase 3) cluster around structural lows. MPF ENTRY and EXIT signals appear only within their valid phase context.
What this demonstrates
Phase 1 — Exhaustion & Volatility
Appears near tops, during inefficient upward movement or late-cycle drive.
Price becomes unstable and prone to sharp reversals.
Phase 2 — Neutral Compression
Forms during sideways hesitation, where neither buyers nor sellers have control.
This phase acts as a transition zone in many assets.
Phase 3 — Recovery Drift
Appears near structural lows.
Price begins stabilizing, volatility contracts and upward drift becomes more probable.
These behaviors repeat across multiple markets, confirming that MFM phases reflect real, observable price structure, not curve-fitting or asset-specific anomalies.
Summary
The AMZN example illustrates a broader pattern seen across all backtested assets:
- Red (Phase 1) highlights exhaustion and instability
- Orange (Phase 2) captures compression and transition
- Green (Phase 3) marks stabilization and constructive drift
This consistency across unrelated markets supports the idea that MFM’s phase fields represent true structural behavior.
5. Forward Tendency of Structural Phases
What typically happens after each structural field.
Backtests across assets such as SPX, NVDA, Gold, BTC, XRP and AMZN reveal a clear pattern:
each MFM phase has a distinct forward tendency: It influences the quality of upcoming price behavior.
The model does not predict direction.
Instead, it identifies whether the next period is likely to be:
- unstable
- neutral
- or structurally constructive
This forward tendency is one of the most important empirical confirmations of the MFM framework.
Phase 1 – Volatile Field
Lowest forward stability
After Phase 1, markets tend to show:
- wide dispersion
- sharp reversals
- low signal reliability
- inconsistent follow-through
This phase concentrates instability, not opportunity.
It is the least predictable structural field and carries the lowest forward quality.
Forward tendency:
➡️ Low structural quality
➡️ High noise / low probability of clean continuation
Phase 2 – Compression Field
Neutral forward tendency
After Phase 2, price typically displays:
- sideways drift
- hesitation
- mixed signals
- limited directional edge
Phase 2 acts as a structural “pause”.
It is neither reliably positive nor negative — it simply compresses and resets.
Forward tendency:
➡️ Neutral
➡️ Limited directional advantage
Phase 3 – Drift Field
Highest forward stability
After Phase 3, markets most often show:
- upward drift
- smoother movement
- reduced volatility
- constructive follow-through
Phase 3 carries the strongest structural signature.
It marks early recovery and tends to produce the most stable price behavior in the following periods.
Forward tendency:
➡️ Highest structural quality
➡️ Clear positive drift bias
Summary
Across all tested assets:
- Volatile fields (Phase 1) show the least reliable forward behavior.
- Compression fields (Phase 2) behave as a structural pause, with neutral forward drift.
- Drift fields (Phase 3) show the highest probability of stable, constructive continuation.
These observations indicate that the MFM phases describe not only current structure, but also the expected quality of upcoming market movement.
6. Leadership Layer Evidence
How relative strength exposes structural Leaders and Laggers in the market.
- Outperforms the benchmark over the same period.
- Holds structure during volatility.
- Frequently aligns with Phase 3 recovery fields.
- Underperforms the benchmark over the same period.
- Breaks down earlier during corrections.
- Frequently aligns with Phase 1 exhaustion fields.
The MFM Leadership layer evaluates how an asset behaves relative to a benchmark using ratio-based RSI logic.
This reveals structural strength and weakness that price alone often hides. Across the backtests, Leadership proved to be a stable, non-predictive but highly informative structural layer that improves the interpretation of Regime, Phase and MPF.
What Leadership Measures
Leadership is not about direction.
It describes the quality of movement compared to the benchmark.
Leader
Structurally strong relative performance
- rises faster than the benchmark
- maintains structure during volatility
- often aligns with Phase 3 (rebuilding strength)
- confirms when a recovery is legitimate rather than fragile
Lagger
Structurally weak relative performance
- weaker than the benchmark
- breaks down earlier during corrections
- frequently aligns with Phase 1 (exhaustion or stress)
- exposes vulnerability even if price temporarily rises
What the Backtests Show
Backtests across SPX, NVDA, Gold, BTC, XRP and AMZN highlight several consistent patterns:
✔ 1. Leadership is not a predictor — it is a stability measure
It does not forecast direction.
Instead, it shows whether the market environment around an asset is strengthening or weakening.
✔ 2. Leader phases often align with Phase 3
During recovery fields (Phase 3), Leadership frequently shifts from Lagger → Leader.
This marks early structural improvement.
✔ 3. Lagger phases tend to appear before instability
Especially near Phase 1, the Lagger status often appears before heavy volatility or breakdown.
Not always — but more frequently than random.
✔ 4. Leadership is consistent across assets
While whipsaw-heavy environments can reduce clarity, the broader pattern repeats across assets and timeframes.
This confirms Leadership as a structural, not asset-specific, behavior layer.
✔ 5. Leadership improves other layers
MPF and Regime tend to behave more reliably when aligned with the Leadership layer:
- MPF BUY signals perform more consistently during Leader periods
- MPF SELL signals align more cleanly during Lagger periods
Leadership acts as a context amplifier, not a signal generator.
Leadership in Real Markets
Leadership transitions (Lagger → Leader, Leader → Lagger) appear at structurally important points:
- early in recoveries
- near exhaustion zones
- during regime shifts
- during cross-asset divergence phases
These transitions strengthen the interpretability of Phase and Regime.
(Optioneel: hier kun je een kleine Leader/Lagger visual plaatsen in dezelfde stijl als de Phase-cards.)
Summary
The Leadership layer adds a crucial dimension to MFM:
- It reveals structural strength and weakness relative to the benchmark.
- It does not predict direction, but shows the stability and quality of an asset’s movement.
- Leadership transitions frequently align with Phase 3 (strength) and Phase 1 (weakness).
- It increases the reliability of MPF and Regime by filtering weak setups from strong ones.
Leadership is therefore one of the key empirical validations of the MFM framework.
7. MPF Layer Evidence (Market Pattern Forecast)
How pivot-anchored pattern detection provides directional context within the MFM framework.
- Anchored to structural pivots (FUP/FDP).
- Requires Phase 3 or strengthening conditions.
- Invalid when Leadership = Lagger.
- Filters noise through Regime and Phase alignment.
- Anchored to pivot-based exhaustion patterns (FDN/EXIT).
- Requires Phase 1 or weakening conditions.
- Strengthened when Leadership = Lagger.
- Suppressed when Regime contradicts the structure.
The Market Pattern Forecast (MPF) is not a classic signal-generator. It does not attempt to predict exact turning points. Instead, MPF uses pivot-anchored structural patterns to identify:
- where short-term reversal pressure is increasing
- where buying or selling behavior becomes structurally probable
- when short-term signals align with the larger MFM context
MPF only activates when both structure and context are aligned.
How MPF Works
MPF uses a multi-step structural process:
- Pivot detection
– local highs/lows act as anchor points - Pattern confirmation
– FUP/FDN, micro-rotations, exhaustion points - Context filtering
– the signal is only valid when Regime + Phase + Leadership agree - Directional forecast
– BUY or SELL labels appear only in context-supported regions
This approach keeps MPF calm, selective and context-driven.
Why MPF Is Different
Traditional indicators trigger signals anywhere on the chart.
MPF does the opposite: it refuses to fire unless structural probability is present.
This is why, in the backtests:
- BUY signals cluster near Phase 3
- SELL signals cluster near Phase 1
- Very few signals appear in Phase 2
- False signals usually appear in conflicting regime/leadership environments
The result is not “more signals”.
The result is better-filtered structure.
MPF in Real Markets (AMZN Example)
Figure 7.1 – MPF BUY signals appear in recovery fields (Phase 3) while SELL signals align with exhaustion fields (Phase 1). MPF does not fire in neutral or conflicting context. This creates a stable, selective forecast pattern.
What the Backtests Show
Across several assets and timeframes:
✔ 1. MPF BUY signals cluster near Phase 3
Consistent across SPX, NVDA, BTC, XRP and AMZN.
BUY signals rarely appear outside recovery fields.
✔ 2. MPF SELL signals cluster near Phase 1
SELL signals concentrate around exhaustion and instability zones.
✔ 3. MPF ignores most noise
Whipsaws that would trigger classic indicators are filtered out by context alignment.
✔ 4. MPF improves when Leadership is aligned
Leader → higher quality BUY signals
Lagger → higher quality SELL signals
✔ 5. MPF is selective, not frequent
This is deliberate:
MPF is a probabilistic structural model, not a trading signal engine.
Summary
MPF adds a directional probability layer on top of Regime, Phase and Leadership:
- signals appear only when structural context is aligned
- BUY clusters around strengthening environments
- SELL clusters around weakening environments
- pivot anchoring keeps the model stable even in volatile markets
MPF does not predict the market: it identifies structurally favourable regions within the MFM framework.
9. Multi-Asset Consistency Evidence
The MFM structure appears independently across equities, crypto, metals and mixed volatility environments.
One of the strongest findings in the backtest study is that the structural layers of MFM behave consistently across fundamentally different markets. This demonstrates that MFM does not rely on price patterns unique to one asset class. It captures universal behavior driven by momentum, rotation and structural imbalance.
Across all tested assets — SPX, NVDA, AMZN, Gold, BTC and XRP — the same patterns appear:
- Phase 1 behaves as a volatile, unstable field
- Phase 2 forms a compression zone with limited directional drive
- Phase 3 produces recovery stability and forward drift
- Leadership transitions align with Phase structure
- MPF signals cluster where structure supports them
- Deviation appears when assets break their expected rhythm
This consistency is what makes MFM structurally reliable rather than asset-dependent.
Cross-Asset Behavior Highlights
✔ 1. Phase fields remain stable across assets
Whether in high-cap equities, metals or crypto, the three fields retain the same meaning:
- Phase 1 → volatility and drift instability
- Phase 2 → compression and hesitancy
- Phase 3 → stabilizing drift and recovery
✔ 2. Regime and Leadership behave predictably
Even in assets with extreme volatility (BTC) or heavy macro bias (Gold),
the higher-timeframe regime and relative strength ratio follow the same structural rhythm.
✔ 3. MPF clusters appear at the same structural points
BUY signals cluster around Phase 3 across all assets.
SELL signals cluster around Phase 1.
This pattern holds regardless of market type.
✔ 4. Deviation occurs in similar instability pockets
Irregular behavior (context conflicts) shows the same typical locations:
- late Phase 2
- failed recoveries
- extreme one-sided impulses
- or inconsistent leadership conditions
✔ 5. The model does not “curve-fit” to any single market
Because the layers derive from structural dynamics — not patterns —
the model behaves consistently even when price-action differs dramatically across assets.
Figure 9-1-4 – Across SPX (index), NVDA (tech), Gold (metals) and BTC (crypto), the same MFM structural layers appear.
Despite different volatility regimes, market types and behaviors, Phase, Regime, Leadership and MPF remain stable and consistent.
This confirms that MFM captures structural logic rather than asset-specific patterns. All assets were tested with identical settings.
No asset-specific tuning, no optimizations, no curve fitting.
Summary
Across six assets with completely different market characteristics:
- large cap tech
- broad index
- metals
- high-volatility crypto
- low-volume hybrid markets
…the MFM layers appear with remarkable consistency.
This indicates that the framework does not depend on:
- asset type
- volatility regime
- trading hours
- sector behavior
- price pattern features
MFM captures structural market logic, not surface-level patterns.
Note
The multi-asset study used identical MFM settings across all assets. No asset-specific tuning was applied. This further confirms the universal nature of the underlying structure.
8. Deviation Layer Evidence
How the Deviation layer identifies anomalies and structural inconsistencies in real markets.
- Volatility matches Phase structure.
- Momentum develops symmetrically.
- Leadership readings remain coherent.
- Regime and Phase move in alignment.
- Unexpected volatility expansion.
- Breakdown or breakout attempts that do not fit the phase.
- Momentum surges without structural support.
- Cross-asset divergence from benchmark rhythm.
The Deviation layer is designed to detect context-breaking behavior, moments where price action diverges from the expected structure defined by Regime, Phase and Leadership. Deviation is not a prediction tool. It is an AI-based anomaly and context-Deviation layer that highlights:
- unusual momentum bursts
- abnormal volatility spikes
- inconsistent structure relative to the active phase
- behavior that statistically does not belong in the current field
This layer acts as an early-warning system for instability.
Why Deviation Exists in MFM
Markets sometimes break their own rhythm.
When this happens:
- MPF becomes unreliable
- Phase structure degrades
- Leadership signals distort
- Regime readings temporarily lose clarity
Deviation identifies these moments and marks them explicitly.
Across the backtests, Deviation events cluster around:
- failed recoveries
- unexpected breakdown attempts
- late-trend irrational strength
- shakeouts inside Phase 2
- false tops and false bottoms
This makes the Deviation layer a risk-awareness tool, not a signal.
How the Deviation Layer Works
Deviation is triggered when the following conditions appear:
1. Structural mismatch
Price behaves in a way that conflicts with the current Phase or Regime.
2. Volatility irregularities
Sudden volatility expansions inside compression zones.
3. Momentum asymmetry
One-sided surges that break the expected rotational balance.
4. Cross-asset dissonance
When an asset diverges sharply from its benchmark behavior.
These Deviations often precede instability, even when price still looks calm.
Deviation in Real Markets (Example)
Figure 8-1 – Deviation highlights emerging instability that does not fit the active structure. These events frequently precede shakeouts, failed rallies or false breakouts.
What the Backtests Show
Across SPX, NVDA, AMZN, BTC and XRP:
✔ 1. Deviation appears in the build-up to instability
Often before the market visibly turns.
✔ 2. Deviation clusters in Phase 1 and late Phase 2
These are the most unstable regions structurally.
✔ 3. Deviation suppresses MPF
When deviation is active, BUY/SELL signals are filtered or weakened.
✔ 4. Deviations often mark “mispriced clarity”
The market pretends to be stable, but structure is breaking internally.
✔ 5. Deviations are asset-independent
The same patterns appear across crypto, equities and metals.
This makes Deviation a universal behavioral layer inside the MFM framework.
Summary
The Deviation layer strengthens MFM by identifying when:
- structure is breaking
- volatility becomes abnormal
- momentum diverges
- the market stops behaving according to its field
Deviation is not a prediction. It is a context alarm, a structural marker of instability.
When combined with Regime, Phase, Leadership and MPF, Deviation helps identify when the market is not trustworthy, even if price movement looks normal.
Implementation note
In the current TradingView version, Deviation is already used internally to filter MPF signals and to qualify structural context. A dedicated visual HUD / overlay for the Deviation layer will be added in a later release. The visuals on this page reflect the full model design, including upcoming features.
10. Why MFM works – The structural foundation
Markets do not move randomly. They follow structural rhythms shaped by momentum, volatility and rotational pressure. The Market Framework Model works because it is not built around patterns or signals. It is built around structural forces that appear across all markets, all timeframes and all volatility environments.
These forces organize price into recognizable fields:
- imbalance vs stability
- compression vs expansion
- strength vs weakness
- continuation vs exhaustion
MFM does not try to predict the market. It reads the structure that already exists.
The Four Mechanisms That Make MFM Work
1. Markets rotate, they do not drift randomly
Price moves through cycles of:
- expansion
- hesitation
- exhaustion
- recovery
These phases repeat because human behavior, liquidity flow and positioning repeat.
MFM models this rotation directly via Phase 1/2/3.
2. Momentum has direction and it has decay
Momentum is not constant.
It strengthens, weakens and compresses in recognizable patterns.
Regime captures the long-term direction of momentum.
Phase captures the short-term organization of momentum.
This combination describes the “state of motion” of the market.
3. Relative strength reveals hidden internal structure
Markets do not move as isolated assets.
They compete for capital, attention and liquidity.
This competition shows up as:
- leadership
- lagging
- structural divergence
- intermarket imbalance
MFM measures this via the ratio-based leadership layer,
which exposes internal strength or weakness that price alone cannot show.
4. Structural behavior influences probability
Price does not behave the same way in every state.
- Volatile fields → unstable, low predictability
- Compression fields → hesitation, limited edge
- Drift fields → stable forward tendency
- Phase 1 → exhaustion risk
- Phase 3 → recovery probability
- Deviation → instability
MPF builds on this by adding directional probability instead of prediction.
Signals fire only when structure supports them,
not when an indicator crosses a line.
Why MFM Avoids Classical TA Problems
Most TA fails because it:
- uses fixed levels
- chases patterns
- ignores volatility context
- ignores cross-asset strength
- treats every market the same
- triggers signals on every fluctuation
MFM avoids these problems by:
- using fields, not fixed thresholds
- modeling rotation, not pattern shapes
- considering leadership, not isolated price
- using phase logic, not crossover logic
- filtering through regime, not one-time signals
- suppressing noise via structure-first logic
- identifying instability via Deviation
This makes the model robust, selective and consistent.
The Core Principle
Markets behave differently depending on their structural state.
If you understand the state, you understand the behavior.
MFM works because it models the state, not the outcome.
Summary
MFM is effective because:
- it captures universal structural behavior
- it measures organization, not randomness
- it builds probability from context, not signals
- it adapts naturally across assets and volatility regimes
- it identifies instability instead of reacting to it
- it respects the cycles and rhythms that govern markets
This makes MFM a structural model — not just another indicator.
Below you will find a direct link to the complete backtest document
Disclaimer
The Market Framework Model (MFM) and all related materials are provided for educational and informational purposes only. Nothing in this publication, the indicator, or any associated charts should be interpreted as financial advice, investment recommendations, or trading signals. All examples, visualizations, and backtests are illustrative and based on historical data. They do not guarantee or imply any future performance. Financial markets involve risk, including the potential loss of capital, and users remain fully responsible for their own decisions. The author and Inratios© make no representations or warranties regarding the accuracy, completeness, or reliability of the information provided. MFM describes structural market context only and should not be used as the sole basis for trading or investment actions.
By using the MFM indicator or any related insights, you agree to these terms.
© 2025 Inratios. Market Framework Model (MFM) is protected via i-Depot (BOIP) – Ref. 155670. No financial advice.