We believe that transparency is paramount when dealing with sophisticated technology like AISHE. This FAQ addresses critical inquiries regarding security, performance, our business model, and technical aspects to help potential users make a well-informed decision.
Section 1: Security
Q: Why does the installation require disabling security features like antivirus scanners, power-saving functions, or screen savers? How is user data security ensured if these basic protections must be turned off?
A: This is a crucial point, and the reason is performance-related, not data-related. AISHE is a high-performance application that requires uninterrupted, real-time data flow and processing.
- Antivirus/Firewall: Real-time antivirus scanners can interfere with the constant, high-frequency data exchange (RTD/DDE) between AISHE and the MetaTrader platform. They might mistakenly flag this legitimate activity as suspicious, causing delays or connection interruptions that would be catastrophic for a live trading system. We recommend creating a specific exception for the AISHE application in your security software rather than disabling it entirely.
- Power-Saving/Screen Savers: These Windows functions can throttle CPU performance or suspend processes to save energy. For a system that analyzes the market 24/7, any interruption or performance reduction, even for a moment, could lead to missed opportunities or delayed risk management actions. Disabling these ensures AISHE has the dedicated resources it needs to operate optimally.
Data Security: AISHE's core security principle is that it does not collect or process any of your personal data. The system operates using an anonymous, hardware-bound ID. Therefore, the deactivation of these performance-hindering features does not expose any personal user data to us, because we never have it in the first place.
Q: Why is the specific date format (dd.MM.yyyy) and number format (dot as thousands separator, comma as decimal) mandatory?
A: This is a technical requirement for data consistency. The AISHE client processes numerical data from log files and potentially from the MetaTrader environment. Different regional settings can cause misinterpretations (e.g., is "1.234" one thousand two hundred thirty-four or one point two three four?). To ensure absolute data integrity and prevent catastrophic calculation errors in a live financial environment, AISHE requires a standardized format for all its inputs. This strict standardization is a safety feature, not a limitation.
Q: The documentation mentions, "Your distributor manages your cloud chain connections from a central location." How is the data exchanged between the local system and the "distributor" protected?
A: This phrase refers to the technical management of the license and the strategic data stream from our Main System (which you could consider the central "distributor" of intelligence) to your local client.
- Data Content: The data exchanged is not your personal or financial data. It consists of the anonymous AISHE-ID for license validation and the strategic, non-personal parameters and forecasts generated by our core AI.
- Data Protection: This connection is secured using industry-standard encryption protocols (like TLS/SSL) to ensure the integrity and confidentiality of the data stream. This prevents tampering with the strategic information your local AISHE client receives.
Section 2: Performance Verification
Q: Where can I see independent, verified performance data proving the system's claimed superiority?
A: As explained in our main FAQ, AISHE is a software tool, not an investment fund. We do not manage a central pool of money, so "company" performance cannot be audited in the traditional sense. Performance is generated on thousands of individual user accounts, which we do not have access to. We believe the only truly "independent" and "verified" performance data is what you observe yourself. That is the purpose of the trial period: to allow you to run AISHE in the live market, connected to your own demo or real account, and verify its performance in the current market environment for yourself.
Q: How is the "success rate of trading decisions" measured, and how can users verify it? Are there third-party audits of these metrics?
A: The "success rate" is an internal metric derived from our development simulations and ongoing monitoring of our core models. It is a probabilistic measure and not a guarantee of future results for any individual user. Users can verify their own instance's performance directly within their MetaTrader account history, which provides a complete, broker-verified log of every trade taken. This broker statement is the ultimate, unalterable record of performance. As we are not a financial entity managing funds, third-party audits of these software metrics are not applicable.
Q: Why is the trial period limited to 10 days (in the subscription contract) when marketing materials mention 14 days? Isn't this too short to evaluate performance?
A: Thank you for pointing out this discrepancy. This is an error in our documentation that will be corrected immediately. The official, legally binding trial period is 14 days. We apologize for this confusion. While 14 days may not cover all possible market conditions, it is a substantial period designed to demonstrate the system's core functionality, stability, and typical behavior, allowing users to make an informed decision about its value.
Section 3: Business Model
Q: Why is there "no refund policy" when the system cannot be fully evaluated during the trial?
A: Our business model is based on providing a substantial, fully-functional, no-obligation 14-day free trial. This allows every user to conduct their own due diligence and see the system's value firsthand before committing to a subscription. Because we offer this complete, upfront evaluation period, we do not offer refunds on subscription periods that have already begun. This is a standard practice for many software-as-a-service (SaaS) products that provide a comprehensive free trial.
Q: What specific services are covered by the monthly fee? How does this differ from a pure software license model?
A: The monthly fee covers much more than just a static software license. It is a subscription to a dynamic, evolving ecosystem. Your fee covers:
- License to Use: The right to use the autonomous AISHE client on one computer.
- Access to the Core AI: A continuous connection to our Main System, which provides the real-time strategic intelligence, market analysis, and adaptive forecasts that power your local client.
- Continuous Updates & Evolution: Ongoing updates, bug fixes, and performance enhancements for both the local client and the central Main System AI. You are subscribing to a system that is constantly learning and being improved.
- Technical Support: Access to second and third-level technical support provided by us to your distributor/reseller.
Section 4: Technical Aspects
Q: How can the statement that the system "learns through active trading" be reconciled with responsible risk management? How are losses during this "learning phase" avoided?
A: This is a critical distinction. The core, large-scale "learning" of the AI models is done by us in a simulated environment on petabytes of historical data before any model is deployed. The system does not "learn" the basics of trading at your expense. The "learning through active trading" refers to its real-time adaptation. The AI adapts its strategy based on its real-time analysis of the market's "hidden state." It learns which of its pre-trained strategies is most effective in the current market condition. Losses are managed through the robust, user-defined risk controls (max drawdown, risk per trade) and the AI's internal risk protocols, which are designed to reduce exposure in highly uncertain or unpredictable market phases.
Q: How does the "collective intelligence" approach work? What data is exchanged between users, and how is privacy maintained?
A: To be perfectly clear: No personal data, trading data, or strategies are ever exchanged between users. The term "collective intelligence" refers to an anonymized feedback loop within our own system. The performance data from thousands of anonymous AISHE-IDs, in aggregate, helps our Main System AI to learn and adapt its core models more effectively over time. For example, it can learn how a specific market event affects different currency pairs on a broad scale. This improves the strategic intelligence provided to all users without ever compromising the privacy of any single individual. Your trading activity remains completely private.
Q: How do you ensure the system doesn't make false decisions leading to significant losses, especially given studies showing AI traders averaging losses?
A: This is the central challenge we have dedicated over a decade to solving.
- Superior Model: Most "AI traders" cited in studies are simplistic, data-fitted models that fail when market conditions change. AISHE's unique "Knowledge Balance Sheet" model is designed to be more robust because it analyzes the underlying causes of market behavior, not just the price effects.
- No Guarantee Against Loss: No trading system in the world, human or AI, can be immune to losses. Trading always involves risk. The goal of AISHE is not to win every trade, but to achieve a positive expectancy over time by making statistically sound decisions based on its deep market analysis.
- User-Controlled Risk Management: The ultimate protection against significant financial loss is the robust risk management framework that you, the user, control. You set the absolute limits. AISHE is a powerful engine, but you are always the pilot with your hand on the main circuit breaker.
Section 6: Security Architecture & Installation Requirements
Q: Your installation requires exceptions in security software (antivirus/firewall) and disabling power-saving features. Professional trading platforms like MetaTrader do not. Why is AISHE different?
A: This is a fair and crucial question. The difference lies in AISHE's unique hybrid architecture and its function as an autonomous, real-time agent, not just a passive platform.
- Antivirus/Firewall Exception: MetaTrader is primarily a platform that receives price data and sends orders. AISHE, on the other hand, maintains a constant, high-frequency, bidirectional data stream not only with the MT4 terminal (via RTD/DDE for microsecond-level local data) but also with our encrypted Main System for strategic parameters. Heuristic-based security software, which is not designed for this type of constant, specialized communication, can misinterpret this legitimate high-volume traffic as anomalous, leading to packet inspection, throttling, or connection termination. This would not crash a passive platform, but for an autonomous agent making real-time decisions, such an interruption is a critical failure risk. Therefore, a specific application exception - not disabling the entire antivirus - is a necessary technical requirement for operational stability.
- Power Management: Similarly, Windows Power-Saving and sleep modes actively de-prioritize or suspend CPU and network resources for background applications. As the AISHE client is a persistent, 24/7 background process requiring consistent computational power for its analysis, any system-level resource throttling poses a direct risk to its operational integrity. Professional platforms are typically foreground applications actively used by a human, exempting them from these background process limitations.
Q: You state AISHE collects no personal data, but documentation mentions "Distributors provide data service functions for AISHE client systems" and "cloud chain connections." What data is actually being transmitted?
A: Let's be technically precise. There is a constant, encrypted data stream, but its content must be understood.
- Outbound Data (Local Client -> Main System): The only data sent from your local client to our system is the anonymous, hardware-bound AISHE-ID for license validation and anonymized system health telemetry (e.g., CPU load, connection status) for ensuring stability. No personal info, account numbers, balance, trade history, or IP addresses are transmitted.
- Inbound Data (Main System -> Local Client): Our Main System sends non-personal, strategic data packets to your client. These packets contain the real-time weightings of the "Knowledge Balance Sheet" factors (Human, Structural, Relational) and the resulting forecast with its calculated "half-life."
Section 7: Performance Verification & Transparency
Q: Your refusal to provide historical backtests or audited track records is a major red flag and contradicts industry standards. How do you justify this?
A: We understand this perspective, as it is valid for 99% of trading systems. Our approach deviates for a reason rooted in the nature of our adaptive AI.
- The Flaw of Backtesting for Adaptive AI: A backtest is a static simulation. It cannot replicate the core feature of AISHE: its ability to adapt its internal models based on evolving market regimes. Providing a backtest would be fundamentally misleading, as it would represent a static snapshot, not the living, adaptive system.
- The Problem with Aggregated Performance Data: While technically possible, publishing aggregated, anonymized performance data is also problematic. User results vary significantly based on their chosen broker (spreads, execution speed), their start date, and the specific risk parameters they set. Publishing an "average" would create an unrealistic performance expectation and would not be representative of any single user's potential experience. It would be a marketing metric, not a scientific one.
- Our Solution - The "Live Forward Test" Trial: We acknowledge the dilemma. Our solution is to shift the burden of proof from historical data (which we believe is misleading for our type of AI) to live, real-time verification. The 14-day free trial (we confirm 14 days is the correct, binding period and are correcting all documentation to reflect this) is designed as a live forward test. It allows a user to connect to a demo or small live account and generate their own, broker-verified track record under current market conditions. This, we argue, is a more honest and relevant form of due diligence than any historical report we could provide.
Q: 14 days is insufficient to evaluate performance across different market conditions.
A: We agree that 14 days will not cover every possible market scenario. The goal of the trial is not to provide a definitive long-term performance guarantee. Its purpose is to allow users to verify three critical aspects:
- System Stability and Functionality: Does the system install and run reliably as described?
- Operational Logic: Can the user observe the system making logical entries and exits based on its stated principles (e.g., becoming conservative in high volatility)?
- Short-Term Behavior: Does the system's behavior in the current market align with the user's risk tolerance and expectations?
It is a period for technical and operational due diligence, not for generating a statistically significant long-term track record.
Section 8: "Collective Intelligence" & Data Flows
Q: The explanation of "collective intelligence" is vague and seems to contradict the "no data sharing" claim, especially with mentions of "FL or CL groups." Please clarify precisely.
A: This is a critical area that requires precise language.
- "Collective Intelligence" is Internal to Our Main System: Let us be unequivocal. No data of any kind (personal, financial, or strategic) is ever exchanged between user clients. There is no peer-to-peer communication. "Collective Intelligence" refers to a process that happens exclusively within our secure data center. Our Main System AI analyzes the aggregated, completely anonymous performance data of its own strategic forecasts across thousands of anonymous IDs.
- How it works: The Main System sends out a forecast (e.g., Forecast #A123). It then receives anonymized feedback on how trades based on that forecast performed under various broker conditions. This feedback loop allows the Main System AI to learn which types of forecasts work better in which macro environments. It improves the central "brain," which in turn benefits all individual clients.
- "FL or CL Groups": This reference in internal documentation refers to advanced machine learning techniques like Federated Learning or Clustered Learning, which are methods for training AI models on decentralized data without exchanging the raw data itself. It describes the methodology our Main System uses to learn from the anonymized results, confirming our commitment to privacy. It does not imply that user data is shared.
Section 9: Functionality & Risk Management
Q: The "Knowledge Balance Sheet" model is presented without technical details or independent validation. How is this more than a marketing term?
A: The "Knowledge Balance Sheet" is the conceptual framework - the philosophy - that guides our feature engineering and model architecture. It is our proprietary methodology for classifying market dynamics.
- Technical Implementation: Technically, this translates into how we label and cluster vast amounts of market data for our neural network. Instead of just feeding the AI raw price data, we pre-process and label it according to features that represent our three factors (e.g., volatility spikes as "Human Factor," price reactions at key levels as "Structural Factor").
- Validation: While the model itself is proprietary, its validation is not theoretical but empirical: the real-time performance of the system. We have staked our entire project on the belief that this model provides a more robust and adaptive market view than standard quantitative approaches. The 14-day live trial is our invitation for users to begin their own empirical validation.
Q: The statement "you control the risk parameters" seems to contradict the marketing of an "autonomous" system.
A: This is a crucial distinction between strategic autonomy and risk autonomy.
- Strategic Autonomy: AISHE has full autonomy in its trading decisions - when to enter, when to exit, and what position size to take within the boundaries you set. You do not interfere with its real-time tactical logic.
- Risk Autonomy: You, the user, always retain ultimate control over the strategic risk. You are the fund manager who sets the overall risk mandate for your AI trader. You define the maximum capital at risk, the maximum drawdown you are willing to tolerate, and the markets it is allowed to trade. AISHE operates as your highly intelligent but obedient employee, who will never violate your core directives. This layered approach combines the power of AI with the safety of human oversight.