Grasp of the Thoughts
How It Truly Works
There’s a elementary distinction between constructing a buying and selling robotic and constructing a choice engine.
Most Knowledgeable Advisors are procedural. They look ahead to a situation — an indicator cross, a threshold breach, a sample completion — and so they execute a predefined response. They’re deterministic. They react to guidelines.
Magister Mentis was not designed as a rule executor. It was designed as a probabilistic inference system.
As a substitute of asking, “Did RSI cross 30?”, it asks:
Given the present multidimensional state of the market, what’s the chance distribution of future directional bias?
All the things that follows stems from that single query.
The Market as a Classification Downside
At its core, Magister Mentis treats every closed candle as a characteristic vector. The mannequin doesn’t try to forecast worth magnitude. It performs classification.
For every resolution cycle, it outputs three values:
The impartial class is just not ornamental. It’s vital. It acts as a structural uncertainty absorber. Markets should not binary techniques, and forcing them into up/down logic usually results in overtrading in ambiguous zones.
The system trades solely when one directional class meaningfully dominates the distribution.
The 17-Dimensional Characteristic Area
Each inference begins with a 17-feature vector derived from the final closed candle.
These options should not uncooked values. Nearly all are volatility-normalized to cut back regime bias. This was a deliberate design resolution throughout mannequin coaching to mitigate overfitting to particular volatility environments.
The characteristic teams embrace:
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Candle construction asymmetry (wick imbalance)
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EMA directional relationship (50 vs 200), normalized by ATR
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EMA displacement from worth
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RSI
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ADX
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ATR
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Greenback index delta (actual image if out there, inverse proxy in any other case)
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Quantity delta normalized
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Structural stability rating derived from Singular Worth Decomposition
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Quick-term normalized returns (1, 3, 6 bars)
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Normalized candle vary
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Cyclical encodings of hour and weekday utilizing sine/cosine transforms
The SVD-based stability metric deserves particular point out. Quite than relying solely on momentum or oscillation, the system analyzes current candle construction coherence. A steady singular worth distribution signifies structural consistency; instability signifies regime fragmentation.
This characteristic engineering layer is the place most robustness is created. The mannequin is barely pretty much as good because the house it observes.
Regime Detection: The Macro Filter
Earlier than the system decides course, it decides context.
A secondary mannequin evaluates whether or not the present market is trending or ranging. It outputs a chance:
P(pattern)
This isn’t used straight as a buying and selling sign. As a substitute, it determines routing.
To forestall flip-flopping between regimes on marginal chance shifts, hysteresis thresholds are utilized:
This ensures structural stability in mannequin choice.
Specialist Routing
As soon as regime is set, inference could also be routed to a specialist:
Every mannequin has its personal calibration fixed. This structure permits specialization with out fragmenting execution logic.
AUTO mode routes dynamically. Handbook override is feasible.
Likelihood Calibration
Uncooked mannequin outputs are hardly ever well-calibrated. Machine studying classifiers are usually overconfident.
Magister Mentis applies temperature scaling:
Every mind can use a special temperature fixed decided throughout validation.
This improves threshold reliability and prevents systematic bias from overconfident outputs.
This calibration step is likely one of the causes the EA behaves extra persistently throughout datasets.
Entry Logic: Dominance and Thresholds
A commerce is just not triggered just because a chance is excessive.
Two circumstances have to be happy:
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The directional chance should exceed its confidence threshold.
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It should dominate competing courses by at the least a configurable margin.
Formally:
Purchase requires:
Promote requires the symmetric situation.
This eliminates ambiguous distributions similar to:
P(Up)=0.62, P(Down)=0.59, P(Impartial)=0.05
Which could look robust however are structurally unstable.
Moreover, just one motion is permitted per candle. This prevents intra-bar churn and overreaction.
Danger as a Perform of Confidence
Place sizing can function in two modes:
In risk-based mode, lot dimension is derived from:
The scaler will increase place dimension reasonably if chance exceeds the brink considerably. It’s bounded and capped by:
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Dealer quantity limits
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Free margin constraints
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A tough most lot cap
There isn’t any martingale logic. There isn’t any geometric publicity improve. Loss doesn’t set off dimension enlargement.
Adaptive Cease Logic
Stops and targets could also be ATR-based:
SL = ATR × multiplier
TP = ATR × multiplier
This ensures volatility-adjusted threat management, notably necessary for devices similar to XAUUSD the place static stops turn out to be structurally inconsistent throughout regimes.
Commerce Administration Layer
Unbiased of entry logic, the system can apply:
These mechanisms function after commerce entry and are decoupled from Sensible Exit (RSI exhaustion).
Institutional Shields
Two exhausting safety techniques exist:
If both is breached:
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All positions are closed
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The EA locks till reset
Comfortable filters embrace:
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Unfold filter
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Latency filter
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Buying and selling hour window
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Elective Friday shut
These filters forestall structurally poor execution environments from degrading long-term efficiency.
Coaching and Overfitting Management
The mannequin pipeline contains:
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Multi-year knowledge
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Separate coaching, validation, and holdout take a look at units
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Optuna-based hyperparameter optimization
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Likelihood calibration on validation
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Characteristic normalization
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Regime specialization
The target was not most backtest revenue. The target was steady chance conduct throughout unseen knowledge.
Overfitting was addressed by way of:
No grid logic was launched to artificially clean fairness curves.
Execution Mannequin
All inference is executed regionally inside MT5 through ONNX runtime.
There may be:
The EA operates autonomously as soon as hooked up.
Beta Tester Program
Magister Mentis is presently in staged analysis.
For the primary two analysis phases, a structured beta testing window can be out there.
On roughly the twenty sixth day of every month, one designated “Beta Testing Day” can be introduced.
For that day solely:
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The worth can be lowered by 50%.
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Entry is granted in change for structured suggestions.
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Beta members are anticipated to:
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Ahead take a look at responsibly.
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Present written suggestions.
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Depart an trustworthy assessment reflecting actual expertise.
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Participation is proscribed and manually dealt with.
merchants might ship a non-public message on that day to use for the beta window.
This method permits real-world suggestions with out compromising long-term product positioning.
Magister Mentis is just not designed to commerce continuously.
It’s designed to judge, filter, and act solely when chance alignment and structural affirmation intersect.
It waits.
Then it executes.