FAQ: Clarification on AISHE - Addressing All Concerns

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Security Concerns

Question: Why does the installation require disabling basic security features (antivirus scanners, power-saving functions, screen savers)?

Answer: The requirement to adjust certain security features is performance-related, not data-related:

 

1. Antivirus scanners:
   • AISHE works with RTD (Real-Time Data) and DDE (Dynamic Data Exchange) for data exchange with MetaTrader
   • Antivirus scanners can mistakenly flag these legitimate, high-frequency data streams as suspicious
   • Solution: Instead of completely disabling the antivirus, it's recommended to create specific exceptions for the AISHE application in your security software
   • This aligns with practices for professional trading systems that require high data integrity
2. Power-saving functions and screen savers:
   • These Windows functions throttle CPU performance or pause processes to save energy
   • For a 24/7 trading system, even brief interruptions can be critical
   • Important: It's not about disabling security features for security reasons, but ensuring AISHE receives the resources it needs
   • The AIMAN documentation confirms: "The monitoring page displays the state of AI deep learning mode, AI controlling mode, and neuronal state for each AISHE client" - this requires continuous resources
3. Data privacy:
   • AISHE does not collect or process personal data
   • The system works with an anonymous, hardware-bound ID
   • The statement "Your distributor manages your cloud chain connections from a central location" refers to license management and strategic data streams from the Main System to the local client
   • Exchanged data consists of anonymized, strategic parameters - no personal or financial data

 

Question: Why is the specific date format (dd.MM.yyyy) and number format (dot as thousands separator, comma as decimal) mandatory?

Answer: This is a technical requirement for data consistency, not an arbitrary standard:

 

1. Data integrity:
   • AISHE processes numerical data from log files and 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?)
   • The AIMAN documentation confirms: "The sharing of dynamic data exchange (DDE) and real-time data (RTD) in the AISHE application improves performance significantly"
2. Safety feature:
   • This strict standardization is a safety measure, not a technical limitation
   • Without a uniform format, catastrophic calculation errors could occur in a live trading environment
   • The Advanced FAQ explains: "This specific date format is crucial for accurate data exchange and processing within the AISHE system"

 

1. Compatibility:
   • The format is necessary for the proper functioning of the "V.Chain" (Value Chain)
   • The V.Chain enables interdependence between various templates and functions
   • As described in the Seneca White Paper (2013), the system is based on the "Knowledge Balance Sheet 2.0", which requires clear separation between knowledge market and financial market

 

Performance Verification

Question: Where can independent, verified performance data be viewed that proves the system's claimed superiority?

Answer: Performance verification works differently than with traditional systems:

 

1. System characteristics:
   • AISHE is a software tool, not an investment fund
   • We do not manage a central pool of money, so "company" performance cannot be audited traditionally
   • Performance is generated on thousands of individual user accounts, which we do not have access to
2. True verification:
   • The Advanced FAQ clarifies: "The purpose of the trial period is 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."
   • The only truly "independent" and "verified" performance data is what you observe yourself
   • The MetaTrader account history provides a complete, broker-verified record of all trades
3. Anonymized feedback loop:
   • The Advanced FAQ confirms: "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."
   • This is an anonymized feedback loop that improves the central Main System without disclosing individual data
   • The "V.Chain" enables collective intelligence without exchanging personal or trading data between users

 

Question: How is the "success rate of trading decisions" measured and how can users verify it?

Answer: The success rate is an internal metric that users can verify themselves:

 

1. Internal metric:
   • The "success rate" is a probabilistic measure derived from our development simulations and ongoing monitoring of our core models
   • It is not a guarantee of future results for an individual user
2. User verification:
   • You can verify your instance's performance directly in the MetaTrader account history
   • This broker record is the ultimate, immutable performance record
   • The AIMAN documentation confirms: "The monitoring page displays a historical view of the profit and loss (PL) from Sunday to Saturday" for each connected AISHE client
3. Collective intelligence:
   • The "Co-learn" section enables adjustment of dependency degree, reciprocity, and covariation
   • The Advanced FAQ explains: "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.'"
   • This is not a central database, but an anonymized feedback loop that improves the Main System

 

Question: Why is the trial period limited to 14 days? Isn't this too short to evaluate system performance under different market conditions?

Answer: The 14-day trial period is a carefully chosen compromise:

 

1. Timeframe:
   • The Advanced FAQ confirms: "The official, legally binding trial period is 14 days."
   • This is a substantial period designed to demonstrate core functionality, stability, and typical behavior
2. Market cycles:
   • 14 days typically covers multiple trading days across different market phases
   • The "Week" function in AIMAN enables analysis of volatility patterns across the week
   • The Advanced FAQ explains: "The Weekly Volatility Planning function in the AISHE system provides an overview of the weekly volatility planning function."
3. Real evaluation:
   • The Advanced FAQ emphasizes: "This allows every user to conduct their own due diligence and see the system's value firsthand before committing to a subscription."
   • The trial period enables you to test the system in the current market situation, connected to your own demo or real money account
   • The "Rec" (Trading Record Status) function in AIMAN enables analysis of trading results for selected instruments

 

Business Model

Question: Why is there no refund policy when the system cannot be fully evaluated during the trial?

Answer: The business model is based on a comprehensive trial period before commitment:

 

1. Trial period as due diligence:
   • The Advanced FAQ explains: "We offer this complete, upfront evaluation period, we do not offer refunds on subscription periods that have already begun."
   • The 14-day trial period is a fully functional, obligation-free evaluation phase
   • This is standard practice for many software-as-a-service products that offer a comprehensive free trial
2. No hidden costs:
   • The Advanced FAQ clarifies: "The monthly fee covers much more than just a static software license."
   • Your fee covers:
     • License to use the autonomous AISHE client on one computer
     • Access to the Core AI: Continuous connection to the Main System for strategic intelligence
     • Continuous updates and evolution of the system
     • Technical support at second and third level
3. Transparency:
   • The Advanced FAQ emphasizes: "We believe the only truly 'independent' and 'verified' performance data is what you observe yourself."
   • The trial period allows you to verify the system's actual functionality before financial commitment

 

Question: What specific services are covered by the monthly fee?

Answer: The monthly fee covers a dynamic ecosystem, not just a static software license:

 

1. License to use:
   • The right to use the autonomous AISHE client on one computer
   • The Advanced FAQ explains: "The monthly fee covers the license to use the autonomous AISHE client on one computer."
2. Access to Core AI:
   • Continuous connection to the Main System that provides strategic intelligence
   • Real-time market analysis and adaptive forecasts that power your local client
   • The AIMAN documentation confirms: "Your distributor manages your cloud chain connections from a central location."
3. Continuous updates:
   • Ongoing updates, bug fixes, and performance improvements for client and Main System
   • The Advanced FAQ explains: "You are subscribing to a system that is constantly learning and being improved."
4. Technical support:
   • Access to technical support at second and third level
   • The Advanced FAQ confirms: "Technical Support: Access to second and third-level technical support provided by us to your distributor/reseller."

 

Technical Aspects

Question: How can the statement that the system "learns through active trading" be reconciled with responsible risk management?

Answer: The learning mechanisms are clearly defined and safely implemented:

 

1. Pre-trained model:
   • The Advanced FAQ clarifies: "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 fundamentals of trading at your expense
2. Real-time adaptation:
   • The Advanced FAQ explains: "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 situation
3. Risk management:
   • Losses are managed through robust, user-defined risk controls (max. drawdown, risk per trade)
   • The Advanced FAQ emphasizes: "The ultimate protection against significant financial loss is the robust risk management framework that YOU, the user, control."
   • The AIMAN documentation confirms: "The monitoring page displays the balance, equity, used margin, and free margin of each AISHE client."

 

Question: How does the system ensure it doesn't make false trading decisions?

Answer: Multiple layers of protection ensure reliability:

 

1. Superior model:
   • The Advanced FAQ explains: "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."
   • The system is based on the Seneca White Paper (2013), which describes a cybernetic system with adaptive control mechanisms
2. No loss guarantee:
   • The Advanced FAQ clarifies: "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 expectation over time
3. User-controlled risk management:
   • The Advanced FAQ emphasizes: "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

 

Question: How exactly does the "collective intelligence" approach work?

Answer: Collective intelligence is a precise technical concept:

 

1. No data exchange between users:
   • The Advanced FAQ is unequivocal: "No personal data, trading data, or strategies are ever exchanged between users."
   • The AIMAN documentation confirms: "The monitoring page displays the state of collaborative learning of artificial intelligence as on or off for each AISHE client."
2. Anonymized feedback loop:
   • The Advanced FAQ explains: "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 various currency pairs broadly
3. V.Chain architecture:
   • The AIMAN documentation describes the "Value Chain Index structure" as "key component of the AISHE system that allows for interdependence among various templates and functions."
   • The "Co-learn" section includes:
     • Control - the situation
     • Degree/level of dependency
     • Reciprocity of dependency
     • Covariation of interest
     • Basis of dependency
     • Temporal structure
     • Availability of information

 

Question: Why are the installation requirements so complex?

Answer: The complexity is system-inherent and necessary:

 

1. Technical necessity:
   • The AIMAN documentation confirms: "The sharing of dynamic data exchange (DDE) and real-time data (RTD) in the AISHE application improves performance significantly."
   • The "MT" section in the Assistant checks whether the required DDE and RTD are available
2. V.Chain complexity:
   • The "V.Chain" (Value Chain) is a key component that enables interdependence between various templates and functions
   • As described in the Seneca White Paper (2013), the system is based on the "Knowledge Balance Sheet 2.0", which requires a complex architecture
3. Autonomy as core principle:
   • The Advanced FAQ explains: "AISHE is an autonomous system; it learns through active trading in conditions that operate through neural networks."
   • The "NSPE" (Neuronal Parameter Parameter Estimation) environment offers advanced neural network modeling capabilities
   • The AIMAN documentation confirms: "The NSPE environment in the AISHE system provides advanced neural network modeling capabilities."

 

Summary

The concerns initially raised were based on an incomplete understanding of AISHE's technical architecture and functionality. After thorough examination of the documentation and historical development (Seneca White Paper 2013, Knowledge Balance Sheet 2.0), it's clear that:

 

1. Security: The system configuration requirements are performance-related, not data-related. Data integrity requires uniform formats and uninterrupted resources.
2. Performance verification: The 14-day trial period enables genuine, individual verification by the user in the current market situation, connected to their own account.
3. Complexity: The system's complexity is inherent and necessary, based on decades of scientific work (Knowledge Balance Sheet 2.0).
4. Business model: The subscription model covers a dynamic ecosystem, not just a static software license, and is justified by the comprehensive trial period before commitment.
5. Technical aspects: The learning mechanisms are clearly defined, with pre-trained models and real-time adaptation, not "learning at the user's expense."

 

AISHE is not a simple tool but a highly complex, autonomous trading system built on solid theoretical foundations. Like any complex system, it requires some initial investment of time, but the potential benefits justify this investment for serious traders.