AISHE Questions Catalog: Professional Edition
Technical Questions
1. Goal and scope: What specific trading problems or opportunities does the AI project aim to address?
AISHE addresses the fundamental challenge of market unpredictability through a specialized autonomous trading framework. Unlike conventional AI applications, AISHE operates as a weak AI system exclusively dedicated to financial market analysis and execution. Its unique value lies in processing three critical dimensions simultaneously: human behavioral patterns, structural market conditions, and inter-asset relationships. This tripartite analytical approach enables the system to identify subtle market signals that traditional quantitative models miss, particularly those arising from collective trader psychology and emerging market inefficiencies. By focusing exclusively on financial markets rather than attempting general intelligence, AISHE achieves precision in identifying short-term trading opportunities while maintaining appropriate risk parameters.
2. Data sources: What types of data are used and how is data quality ensured?
AISHE processes data across three specialized categories that form its analytical foundation:
- Human factor data: Trader behavior patterns, risk appetite indicators, and collective decision-making trends derived from market participation metrics
- Structural data: Exchange infrastructure metrics, liquidity measurements, technical chart patterns, and execution quality analytics
- Relationship data: Cross-asset correlations, macroeconomic indicator relationships, and market regime transitions
Data quality is maintained through a multi-layer verification system that continuously validates information against known market behaviors. Unlike centralized AI systems, AISHE operates in a decentralized manner where each user's instance validates data through local processing, with anomalous data points automatically flagged and quarantined while maintaining operational continuity. The system's design ensures that data integrity is preserved without requiring external validation services.
3. Backtesting and Validation: How has the AI been tested in the past and what were the results?
Traditional backtesting methodologies prove inadequate for AISHE due to its fundamentally different operational paradigm. As a continuously learning autonomous system, AISHE operates on the principle that "the past is not static but serves as a basis for adapting to future conditions." Each user's AISHE instance develops unique decision-making patterns based on individual market interactions and hardware capabilities. Rather than relying on historical simulation, AISHE validates performance through real-time adaptation metrics and state transition analysis. The system tracks its ability to recognize and respond to specific market states (represented by 18-digit state vectors) and measures improvement through successful navigation of increasingly complex market conditions over time.
4. Risk management: How does AI deal with risks, including market volatility, liquidity and model degradation over time?
AISHE implements a sophisticated risk management framework that dynamically adapts to changing market conditions while accounting for the user's specific hardware limitations. The system continuously monitors volatility indicators and automatically adjusts position sizing based on both market conditions and computational capacity. For users with less powerful hardware, AISHE implements conservative risk parameters that align with their system's processing capabilities. The framework includes:
- Hardware-aware risk scaling that adjusts to individual computational capacity
- Real-time liquidity analysis to ensure executable trade sizes
- State transition monitoring to detect potential model degradation
- Automatic strategy simplification when processing constraints are detected
- User-defined risk boundaries that maintain alignment with individual risk tolerance
This approach ensures that risk management remains effective regardless of the user's specific hardware configuration.
5. Performance metrics: What metrics are used to evaluate AI trading performance?
AISHE employs a personalized performance evaluation framework that recognizes the inherent variability between user instances. Rather than relying on universal benchmarks, the system focuses on individual trajectory improvement through metrics including:
- State recognition accuracy (measuring the system's ability to identify relevant market conditions)
- Decision latency (time between state recognition and action, hardware-dependent)
- Adaptive learning rate (tracking improvement in decision quality over time)
- Hardware utilization efficiency (optimizing performance within specific computational constraints)
- Risk-adjusted trajectory (measuring progress toward user-defined financial goals)
This personalized approach acknowledges that performance must be evaluated within the context of each user's unique hardware configuration and market interaction patterns.
6. Integration: How will AI be integrated into existing trading systems and workflows?
AISHE functions as a client-based autonomous agent that integrates with existing trading infrastructure through standardized protocols. The system establishes secure connections with supported brokers and exchanges using DDE (Dynamic Data Exchange) and RTD (Real-Time Data) protocols, enabling seamless data flow and order execution. Each user's AISHE instance operates independently on their local machine, processing market data and generating trading decisions without requiring cloud infrastructure. This decentralized architecture ensures that trading decisions remain responsive to real-time market conditions while respecting the user's specific hardware capabilities.
7. Regulatory Compliance: How does the project ensure compliance with financial regulations and data protection laws?
AISHE maintains regulatory compliance through architectural design rather than active monitoring. By operating exclusively as a client-side application that interfaces with regulated brokers and exchanges, AISHE delegates compliance responsibilities to established financial institutions. The system itself does not collect or transmit personal data, nor does it store sensitive information beyond what is necessary for local operation. This approach ensures that all trading activities adhere to the regulatory frameworks of the user's chosen broker while maintaining GDPR compliance through minimal data processing requirements.
8. Transparency and explainability: How transparent is the AI decision-making process?
AISHE provides multi-layer transparency through its state-based decision framework. For each trading decision, the system identifies:
- The specific market state that triggered the decision (represented by the 18-digit state vector)
- The dominant factor (human, structural, or relationship) driving the decision
- The confidence level based on historical state transitions
- The hardware-dependent processing time that influenced decision parameters
This state-based explanation framework allows users to understand the system's reasoning without requiring technical expertise in machine learning. The transparency model acknowledges that complete explainability is constrained by hardware capabilities, with more powerful systems providing more detailed decision rationales.
9. Scalability: Is the AI solution scalable and can it handle increasing data volume and trading complexity?
AISHE's scalability model differs fundamentally from centralized AI systems. Rather than scaling through additional server capacity, AISHE scales through distributed processing across user instances. Each user's hardware configuration determines their individual capacity to process market data and execute trading decisions. The system automatically adjusts complexity based on available computational resources, with more powerful hardware enabling:
- Recognition of more complex market states
- Faster state transition analysis
- More nuanced risk parameter adjustments
- Greater integration of relationship factors
This distributed scalability model creates a network effect where individual improvements contribute to collective market understanding without requiring centralized infrastructure.
10. Security: What measures are in place to protect against cybersecurity threats?
Security is implemented through AISHE's decentralized architecture. As a client-side application that operates exclusively through established broker connections, the system minimizes attack surfaces by:
- Processing all data locally without cloud transmission
- Maintaining minimal data retention requirements
- Using broker-provided encryption for all market data
- Implementing hardware-specific security protocols
- Avoiding external API dependencies that could introduce vulnerabilities
This security model recognizes that individual user hardware configurations represent the primary security boundary, with system integrity maintained through local processing rather than centralized protection mechanisms.
11. Maintenance and updates: How often is the AI model updated or retrained?
AISHE operates on a continuous learning paradigm where model updates occur through organic adaptation rather than scheduled retraining. Each user's instance evolves based on:
- Individual market interactions
- Hardware-specific processing constraints
- State recognition improvements
- Peer learning from other AISHE instances (optional)
The system incorporates swarm intelligence principles where users can opt to share anonymized state transition data, allowing collective improvement while maintaining individual customization. Major framework updates (such as the current version 5.526) are released periodically to enhance core functionality, but the autonomous learning process continues uninterrupted between updates.
12. Ethical considerations: Are there ethical concerns with the system?
AISHE addresses ethical considerations through its specialized design and decentralized operation:
- Market impact limitations through hardware-constrained execution speed
- No attempt to manipulate markets beyond individual capacity
- Transparent state-based decision framework
- Hardware-dependent risk parameters that prevent over-leveraging
- User-controlled participation in swarm learning networks
The system's focus on specialized financial analysis rather than general intelligence inherently limits potential ethical concerns, with the primary consideration being appropriate risk management within each user's specific hardware and financial constraints.
13. Team expertise: What experience and background does the development team have?
The AISHE development team combines deep expertise in financial markets, specialized AI systems, and high-performance computing:
- Prof. DI. Günter Koch: Pioneer in intellectual capital accounting and data modeling
- Prof. Dr. Hans Günter Lindner: Expert in business intelligence and analytical frameworks
- Sedat Özcelik: Implementation specialist with extensive experience in financial technology
This multidisciplinary team has dedicated 16 years to developing the Knowledge Balance 2.0 framework that underpins AISHE, focusing exclusively on creating a specialized autonomous trading system rather than pursuing general artificial intelligence capabilities.
14. Expected ROI: What is the realistic performance expectation?
ROI expectations for AISHE must be understood within the context of individual hardware capabilities and market conditions. The system's performance varies based on:
- User's specific hardware configuration (processor speed being critical)
- Individual risk parameters and trading goals
- Market volatility and opportunity density
- Duration of system adaptation to user-specific patterns
Rather than promising fixed returns, AISHE focuses on demonstrable trajectory improvement, with users typically observing performance enhancement as their system adapts to both market conditions and their specific hardware constraints. The system's value lies in consistent, hardware-appropriate decision-making rather than absolute return metrics.
Business Questions
15. Founding and headquarters
AISHE originated from 16 years of dedicated research into specialized autonomous trading systems. Developed under Intellectual Capital Concepts GmbH in Troisdorf, Germany, the project represents a focused effort to create a truly autonomous trading solution rather than a general-purpose AI. The headquarters remains in Germany, with operational entities in Spain (Seneca AG 24 SL and Seneca AG Trading Limited Company) established for operational efficiency.
16. Capital and shareholders
The AISHE project has been entirely self-funded through private investment over 15 years, with approximately 12 million euros invested in high-performance hardware infrastructure. There are no external investors or loans, ensuring complete independence in development direction. The capital structure reflects a commitment to sustainable growth through operational excellence rather than external financing.
17. Project financing
AISHE maintains a self-sustaining business model through subscription revenue from the AISHE client software. This approach ensures continued development while preserving independence. The focus remains on organic growth through user satisfaction rather than external funding that might compromise the system's specialized focus.
18. Vision and strategy
The vision centers on creating the definitive autonomous trading system that operates within the constraints of real-world hardware while delivering consistent, specialized market analysis. Rather than pursuing artificial general intelligence, AISHE focuses on perfecting its narrow domain expertise through continuous adaptation and user-specific customization. The strategy emphasizes hardware-aware optimization and decentralized learning to create a robust ecosystem of specialized trading agents.
19. Business model
Value creation
AISHE creates value by transforming complex market dynamics into actionable insights tailored to individual hardware capabilities. The three-pillar framework (Human, Structure, Relationship) provides a comprehensive market perspective impossible to achieve manually, while respecting each user's computational constraints.
Revenue model
AISHE operates on a subscription basis with a 10-day trial period, providing sustainable revenue while ensuring users can verify the system's value within their specific hardware environment.
Competitive advantage
The true differentiator is AISHE's recognition that autonomous trading must adapt to individual hardware constraints rather than requiring uniform high-end infrastructure. This creates a more accessible and realistic approach to autonomous trading that acknowledges the practical limitations of real-world deployment.
Future direction
Future development focuses on enhancing hardware-aware optimization, improving state recognition accuracy, and expanding the swarm learning capabilities while maintaining the core principle of specialized, autonomous trading.
This catalog presents AISHE not as a generic AI solution but as a specialized autonomous trading system designed for practical, hardware-conscious deployment. By acknowledging the constraints of real-world implementation while delivering sophisticated market analysis, AISHE represents a realistic approach to autonomous trading that prioritizes sustainable performance over theoretical capabilities.
References
- Company questionnaire: Details