Technical Specification: AISHE System Architecture and Functionality

Core Identity and Purpose

AISHE (Artificial Intelligent System Highly Experienced) is a specialized autonomous trading agent operating within the financial markets domain. It is not a general-purpose AI but a narrow-domain autonomous system designed specifically for financial instrument trading. Its primary function is to execute trading decisions with minimal human intervention after initial parameter configuration.

 

Architectural Framework

AISHE operates on a client-server architecture with distinct components:

 

1. Master System: Central intelligence repository that maintains the core neural architecture
2. Client Application: Local component installed on user's Windows machine 10 or 11 that contains:
   • Neural structure data for decision processing
   • Real-time market data processing module
   • Parameter configuration interface
   • Reporting and monitoring subsystem

 

The "1 computer = 1 AISHE" principle defines its operational constraint - each physical machine hosts one independent instance with dedicated neural processing capabilities.

 

Knowledge Processing Framework

AISHE implements the Knowledge Balance 2.0 framework through three specialized analytical modules:

 

1. Human Factor Module:

   • Processes trader behavior patterns through sequence analysis
   • Quantifies psychological factors (risk appetite metrics) via sentiment analysis of market communications
   • Identifies collective behavior signals using clustering algorithms on trading activity data
   • Maintains experience repository that stores successful decision patterns

2. Structure Factor Module:

   • Analyzes market infrastructure through graph theory applications
   • Processes trading volume and liquidity metrics using time-series analysis
   • Implements technical analysis via pattern recognition on price/time series
   • Executes venue optimization through multi-objective decision algorithms

3. Relationship Factor Module:

   • Processes macroeconomic data through correlation analysis
   • Maps geopolitical events to market impact using NLP and knowledge graphs
   • Tracks cross-asset class relationships via vector autoregression models
   • Monitors investor behavior across asset classes using clustering techniques

 

These modules operate concurrently but maintain separate processing pathways that converge at the decision layer.

 

Operational Parameters

• Decision frequency: Configurable by user (minimum profit thresholds, maximum targets)
• Data processing: Continuous real-time analysis of broker-provided market data
• Learning mechanism: Reinforcement learning with experience replay
• Risk management: Parameter-configurable stop-loss and position sizing algorithms

 

Technical Requirements and Constraints

• OS: Windows 10/11 (specific locale settings: UTC+1, dd.MM.yyyy HH:mm format)
• Processor: Intel i5/i7 or AMD 2.8 GHz+ (critical for real-time processing)
• Memory: 8GB RAM < (minimum for neural network operations)
• Storage: 1.5GB dedicated space
• Input requirements: Specific date/number formatting (thousands separator: ".", decimal: ",")
• Execution dependency: Requires connection to compatible broker with RTD, API

 

Learning and Adaptation Mechanisms

AISHE implements dual learning pathways:

1. System-level learning: Through deep learning on historical market data
2. User-level adaptation: Via reinforcement learning from executed trades

 

The system maintains an experience buffer that stores:

• State-action pairs
• Reward signals (profit/loss outcomes)
• Market condition metadata

 

This enables continuous strategy refinement through policy gradient methods without requiring human intervention in the learning process.

 

Decision Execution Protocol

1. Data ingestion from Data pushing and API, DDE, RTD and Import from the Cloud in Cloud structure
2. ,Parallel processing through three knowledge modules
3. Decision synthesis with weighted integration of module outputs
4. Risk assessment against configured parameters
5. Order execution via broker connection
6. Experience logging for reinforcement learning
7. Experience logging for swarm intelligence learning
8. Experience logging for the reward system for won and lost positions
9. has the ability to create neural matrices and adapt them based on the results of its own actions

 

Each decision includes confidence metrics and rationale tracking for reporting purposes.

 

Parameter Configuration Interface

Users interact with AISHE through:

• Risk appetite calibration (0-100 scale)
• Trading hours specification
• Instrument selection (up to 11 simultaneously)
• Profit target configuration
• Stop-loss parameters, max Time and more

 

These parameters establish the operational boundaries within which the autonomous system functions.

 

Reporting and Monitoring Subsystem

AISHE generates structured reports containing:

• Decision success rate metrics
• Profit/loss analysis by instrument
• Market condition correlation data
• System performance indicators
• Learning progress metrics

 

This data provides transparency into the autonomous decision-making process without requiring human interpretation of market dynamics.

 

Limitations and Constraints

• Dependency on broker API, RTD, DDE stability and quality
• Sensitivity to system resource constraints (CPU/memory)
• No capability for fundamental analysis beyond provided data

 

Subscription and Update Mechanism

• 14-day trial period with full functionality
• Monthly subscription model post-trial
• Continuous data updates with scheduled model refinements
• Version updates available through client interface

 

Critical Distinction from General AI

AISHE represents a narrow-domain autonomous system, not artificial general intelligence. It lacks:

• Consciousness or self-awareness
• Transfer learning capabilities outside financial markets
• General problem-solving abilities
• Understanding of concepts beyond its training data
• Ability to improvise outside configured parameters

 

Its "autonomy" exists strictly within predefined operational boundaries and parameter constraints. The system does not "understand" markets but processes patterns through specialized algorithms trained on historical financial data.

 

This precise technical specification avoids anthropomorphism while accurately representing AISHE's capabilities, limitations, and operational parameters for another AI system's understanding.

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