Risk Disclaimer

Stackorithm is an AI-powered behavioral detection and risk-scoring analytics platform designed exclusively for trading businesses. The Service analyzes trade data to produce probabilistic behavioral outputs, including confidence scores and risk flags, for use in the Client's internal risk management and operational processes. The Service does not constitute financial advice, investment advice, trading recommendations, or any form of regulated financial service.

Nothing produced by the Service should be interpreted as a recommendation to buy, sell, or hold any financial instrument, or as guidance on the suitability of any trading strategy for any individual or entity. The outputs of the Service are analytical data points intended to assist trading businesses in their own internal assessments. They are not directives, determinations, or professional opinions on financial matters.

Stackorithm LLC is not a licensed financial advisor, investment manager, broker-dealer, or regulated financial institution. The platform operates as a data analytics service provider to trading businesses. Clients who require financial, legal, or regulatory advice should engage appropriately qualified professionals in their respective jurisdictions.

The distinction between analytical outputs and financial advice is fundamental to the nature of the Service. Clients must not represent Stackorithm's outputs to their own clients, regulators, or counterparties as constituting financial advice, regulatory determinations, or independent professional assessments.

All outputs generated by the Stackorithm platform, including behavioral confidence scores, risk flags, anomaly indicators, and pattern detection results, are probabilistic in nature. They represent statistical assessments of behavioral patterns derived from submitted trade data and are subject to inherent uncertainty, model limitations, and the quality of input data.

A high confidence score does not constitute proof, confirmation, or a guarantee that a particular behavior has occurred or will occur. Conversely, a low confidence score does not guarantee the absence of the behavior in question. Probabilistic outputs reflect the likelihood of certain patterns based on the model's training and the data provided; they are not binary determinations of fact.

The Service produces confidence scores on a continuous scale rather than binary classifications. A confidence score of 0.85 for a particular behavior type indicates the model's probabilistic assessment based on pattern matching against known behavioral signatures, not a factual determination that the behavior occurred with 85% certainty. Clients must interpret these scores within the context of their own risk tolerance and operational requirements.

Clients should treat analytical outputs as one input among several in their decision-making processes. Outputs should be interpreted by qualified personnel with appropriate domain expertise, and should not be acted upon mechanically or without the application of human judgment and contextual knowledge.

The Stackorithm platform employs machine learning models that are subject to inherent limitations common to all AI and ML systems. Clients must understand these limitations when interpreting analytical outputs and making decisions based thereon.

Training Data Constraints: The models underlying the Service are trained on historical trade data representing patterns observed in past trading activity. Training data, while extensive, cannot encompass all possible trading behaviors, market conditions, or strategic approaches that may exist or emerge. The models may not accurately characterize behaviors that differ substantially from patterns present in the training data. Stackorithm does not warrant that its training data is representative of all trading behaviors the Client may encounter.

Novel Pattern Blindness: Machine learning models are inherently limited in their ability to detect novel or previously unseen patterns. Traders who employ strategies, techniques, or behavioral patterns not adequately represented in the model's training data may not be detected or may be incorrectly classified. New or evolving trading strategies, market manipulation techniques, or behavioral adaptations may fall outside the model's detection capabilities until such patterns are incorporated into future model updates.

False Positive and False Negative Rates: All behavioral detection systems produce both false positives (incorrectly flagging behavior that did not occur or was benign) and false negatives (failing to detect behavior that did occur). Stackorithm does not warrant any specific false positive or false negative rate, and actual rates may vary based on data quality, trading context, behavior type, and other factors. Clients must account for both error types in their operational processes and should not assume that flagged behavior necessarily occurred or that unflagged accounts are free of concerning behavior.

Model Drift and Updates: Machine learning model performance may degrade over time as market conditions, trading patterns, and strategic behaviors evolve. Stackorithm periodically updates its models to address drift and improve performance, but cannot guarantee that model updates will be implemented before performance degradation affects the Client's use case. Model updates may also change detection characteristics, potentially affecting historical comparability of outputs. Clients will be notified of material model updates through standard communication channels.

Behavioral Type Limitations: The Service detects specific behavioral patterns including, but not limited to, gambling behavior, martingale strategies, high-frequency trading patterns, news trading, copy trading, and hedging. The Service does not guarantee detection of all instances of these behaviors, nor does it warrant that its behavioral categories are exhaustive. Trading behaviors not within the Service's detection scope will not be flagged regardless of their risk implications.

Any model performance metrics, detection statistics, or accuracy indicators presented by Stackorithm, whether in marketing materials, documentation, or platform interfaces, are based on backtesting against historical data sets and do not constitute guarantees of live performance.

Backtested results are inherently subject to limitations including, but not limited to: survivorship bias in training data, look-ahead bias in feature engineering, overfitting to historical patterns, and the absence of real-time data quality issues that affect live operations. Historical performance of behavioral detection models is not indicative of future results.

Live detection performance may differ materially from backtested results due to factors including: differences between Client data and training data characteristics, evolving trader behaviors that adapt to detection systems, market regime changes, data quality variations, and latency or timing differences in live data submission.

Clients should conduct their own validation of the Service's performance against their specific data and use cases. Stackorithm recommends a calibration period during which Clients compare platform outputs against their own manual assessments before relying on the Service for consequential decisions.

Stackorithm does not warrant or guarantee that the Service will detect all instances of any particular trading behavior, pattern, or strategy. The absence of a risk flag or a low confidence score does not constitute certification that an account, trader, or trading pattern is free of concerning behavior.

Sophisticated traders may employ techniques specifically designed to evade detection systems, including behavioral obfuscation, pattern fragmentation across accounts or time periods, and exploitation of detection system limitations. The Service cannot guarantee detection of deliberately evasive behavior.

The Service analyzes data submitted by the Client and cannot detect behaviors that are not reflected in the submitted data. Off-platform activity, activity conducted through different accounts or entities, or trading conducted during periods not covered by submitted data will not be analyzed or detected.

Clients remain responsible for implementing comprehensive risk management and compliance programs that do not rely solely on automated detection systems. The Service is intended to augment, not replace, human oversight, investigation capabilities, and professional judgment in identifying and addressing trading risks.

The Stackorithm platform does not generate, provide, or imply any recommendations regarding trading activity, position sizing, risk exposure, or trading strategy for any individual trader or account. The Service analyzes historical and submitted trade data to identify behavioral patterns; it does not advise on future trading actions.

Outputs such as risk scores and behavioral flags are descriptive and analytical. They characterize patterns in submitted data. They are not prescriptive and should not be used as the basis for advising individual traders on how to trade, what instruments to trade, or how to manage their trading accounts from a performance perspective.

Clients must not use Stackorithm's outputs to provide trading advice or signals to their own clients or traders. Any such use would be outside the intended scope of the Service and would be the sole responsibility of the Client. Stackorithm expressly disclaims any liability arising from the use of its outputs in a manner that constitutes the provision of financial or trading advice to third parties.

The Service is designed to support the operational and risk management functions of trading businesses, not to influence the trading decisions of individual market participants.

All decisions made in connection with the outputs of the Stackorithm platform, including but not limited to trader payout decisions, account restrictions, risk management actions, compliance determinations, and any other operational or enforcement measures, are made exclusively by the Client. Stackorithm does not participate in, direct, approve, or bear responsibility for any such decisions.

Clients are solely responsible for establishing and applying appropriate internal policies, procedures, and governance frameworks for the use of analytical outputs. This includes determining the thresholds, criteria, and processes by which outputs are reviewed, escalated, and acted upon. Stackorithm's outputs are inputs to the Client's decision-making process. They do not substitute for that process.

Clients must ensure that their use of analytical outputs complies with all applicable laws and regulations, including those governing fair treatment of traders, anti-discrimination requirements, financial services regulation, and data protection law. The fact that a decision is informed by an algorithmic output does not relieve the Client of its legal and ethical obligations to the individuals and entities affected by that decision.

Stackorithm strongly encourages Clients to implement human review processes for consequential decisions, particularly those that may significantly affect individual traders. Automated or semi-automated decision-making based on probabilistic outputs carries inherent risks that Clients must manage through appropriate oversight and governance.

The quality and accuracy of Stackorithm's analytical outputs are directly dependent on the quality, completeness, and integrity of the trade data submitted by the Client. Stackorithm processes the data it receives and cannot independently verify the accuracy, completeness, or authenticity of submitted data.

Clients are responsible for ensuring that the data they submit to the platform is accurate, complete, and representative of the trading activity they wish to analyze. Submission of incomplete, erroneous, manipulated, or unrepresentative data may result in analytical outputs that do not accurately reflect the underlying trading behavior. Stackorithm accepts no liability for outputs generated from data that does not accurately represent the trading activity in question.

Data quality issues that may affect output accuracy include, but are not limited to: missing or incomplete trade records, incorrect timestamps or timezone handling, data normalization inconsistencies, submission of data from multiple incompatible systems without appropriate harmonization, and gaps in historical data that affect behavioral baseline calculations.

Clients should implement data quality controls and validation processes before submitting data to the platform. Where data quality issues are identified, Clients should interpret outputs with appropriate caution and consider resubmitting corrected data.

Stackorithm maintains service level commitments relating to platform availability and uptime. These commitments, including any stated uptime percentage such as 99.5% or higher, apply exclusively to the technical availability of the platform infrastructure and API endpoints. Service level commitments do not apply to, and should not be interpreted as guarantees regarding, detection accuracy, model performance, or analytical output quality.

Platform availability means that the Service's systems are operational and capable of receiving data submissions and returning analytical outputs. It does not mean that those outputs will achieve any particular level of accuracy, precision, recall, or fitness for purpose. A platform may be fully available while producing outputs that are less accurate than desired due to model limitations, data quality issues, or other factors unrelated to infrastructure uptime.

Planned maintenance windows, during which the platform may be unavailable, will be communicated to Clients in advance where practicable. Unplanned outages may occur and will be addressed in accordance with Stackorithm's incident response procedures. Service credits or remedies for availability failures, if any, are governed by the applicable service agreement and do not extend to detection accuracy concerns.

Clients should not conflate platform availability with detection reliability. High availability of the platform does not imply high accuracy of outputs, and Clients must maintain appropriate alternative processes for periods when the platform is unavailable or when outputs require additional validation.

Stackorithm's use of Client data for machine learning model training and improvement is conducted on an opt-in basis only. Client data will not be used to train, improve, or develop machine learning models unless the Client has expressly consented to such use through a separate written agreement or through opt-in mechanisms provided within the platform.

Where Clients opt in to data usage for model training, Stackorithm implements appropriate anonymization, aggregation, and security measures to protect Client confidentiality. However, Clients should understand that contributing data to model training may influence the detection characteristics that apply to all users of the Service.

Clients who do not opt in to training data usage will receive the same level of service and access to analytical features as those who do opt in. Opt-in status does not affect platform functionality, and Clients may change their opt-in preferences at any time by contacting [email protected].

To the fullest extent permitted by applicable law, Stackorithm LLC shall not be liable for any losses, damages, claims, or expenses, whether direct, indirect, incidental, consequential, or otherwise, arising from or related to: (a) decisions made by Clients based on analytical outputs from the Service; (b) the accuracy, completeness, or fitness for purpose of any analytical output; (c) actions taken against individual traders by Clients in reliance on the Service's outputs; (d) regulatory, legal, or compliance consequences arising from the Client's use of the Service; (e) any failure of the Service to detect, flag, or score any particular behavior or pattern; (f) false positive outputs that incorrectly flag benign behavior; or (g) false negative outputs that fail to detect concerning behavior.

The probabilistic nature of the Service's outputs means that false positives and false negatives are inherent and unavoidable characteristics of the system. Stackorithm does not warrant any specific level of detection accuracy, precision, recall, or F1 score, and shall not be liable for consequences arising from such errors regardless of their frequency or impact.

Clients assume full responsibility for the consequences of their decisions and actions taken in connection with the Service. This includes any financial losses, regulatory sanctions, legal claims, or reputational harm arising from decisions informed by the Service's outputs. The Service is a tool to support, not replace, the Client's own expertise, judgment, and governance processes.

In no event shall Stackorithm's total aggregate liability exceed the fees paid by the Client to Stackorithm during the twelve (12) months preceding the event giving rise to the claim.

Clients are solely responsible for ensuring that their use of the Stackorithm platform and its outputs complies with all applicable laws, regulations, and regulatory guidance in their jurisdiction and in any jurisdiction in which they operate. This includes, without limitation, financial services regulations, anti-money laundering requirements, data protection and privacy laws, consumer protection regulations, and any sector-specific rules applicable to proprietary trading firms, forex brokerages, or CRM platform operators.

Stackorithm does not represent that the Service is designed to satisfy any specific regulatory requirement or that its outputs constitute compliance with any regulatory standard. The Service is an analytical tool; regulatory compliance is the Client's responsibility. Clients should engage qualified legal and compliance professionals to assess how the Service may be used within their regulatory framework.

The regulatory landscape for AI-powered analytics in financial services is evolving. Clients should monitor developments in applicable regulation, including rules relating to algorithmic decision-making, explainability requirements, and the use of AI in financial services, and assess the implications for their use of the Service on an ongoing basis.

Stackorithm cooperates with lawful regulatory inquiries and may be required to disclose information in response to regulatory requests. Stackorithm will endeavor to notify Clients of regulatory requests affecting their data to the extent permitted by applicable law.

The Client agrees to indemnify, defend, and hold harmless Stackorithm LLC, its officers, directors, employees, agents, and affiliates from and against any and all claims, damages, losses, liabilities, costs, and expenses (including reasonable legal fees) arising out of or related to: (a) the Client's use of the Service; (b) decisions made by the Client based on the Service's outputs; (c) the Client's breach of any terms of service, data processing agreement, or other agreement with Stackorithm; (d) the Client's violation of any applicable law or regulation; (e) any claim by a third party, including traders, arising from the Client's use of the Service's outputs; or (f) the Client's misrepresentation of the Service's outputs to third parties.

This indemnification obligation shall survive the termination of the Client's subscription to the Service and shall apply regardless of whether the claim arises from the Client's negligence, willful misconduct, or strict liability.

Stackorithm shall promptly notify the Client of any claim subject to indemnification and shall cooperate with the Client's defense of such claim. The Client shall not settle any claim in a manner that admits liability on behalf of Stackorithm or imposes obligations on Stackorithm without Stackorithm's prior written consent.

This Risk Disclaimer and any disputes arising from the use of the Stackorithm platform shall be governed by and construed in accordance with the laws of England and Wales, without regard to conflict of law principles.

Any legal action or proceeding arising out of or relating to this Risk Disclaimer or the Service shall be brought exclusively in the courts of England and Wales, and each party irrevocably submits to the exclusive jurisdiction of such courts.

For legal inquiries relating to this Risk Disclaimer or the Service, Clients may contact Stackorithm LLC at [email protected].