Aishe system client - quick reference

Welcome to the AISHE System Client Documentation page! 

Here you will find all the information you need to customize and set up your AISHE system. From configuring and adjusting the neural network to utilizing deep learning, cooperative learning, and swarm intelligence, we've got you covered. Our comprehensive documentation will guide you through every step, ensuring that you get the most out of your AISHE system. 

Explore our resources and start unleashing the power of AI today!

 

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Setup

This is where you can make default or desired customizations, which is referred to as the "interdependence chain and adjustment structure". It includes two components: "Day setting", which applies only to the current day and is not a global setting, and a combination of "Adjust Daily Trading Requirements".

For now, it is recommended to leave everything unchanged. However, for computers with country-specific settings, you must check whether the table contains incorrect settings. You can change them here and enter them in the configuration file or have them changed.

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Config

This guide explains how to configure the AISHE system to suit your specific needs and requirements. You can adjust the system's settings and preferences, including the default trading parameters, neural networks, and machine learning algorithms.

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Timing

The Timing function is used to set trading instrument activity times in the AISHE system. This function allows you to specify the time interval for trading, set the trading hours for each trading instrument, and adjust the time zone.

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MSetup

This guide explains how to set up the AISHE client system for multiple Matrix Neuronal sessions, point structures, various AI settings and methods, and users. It covers all the necessary steps for configuring the system to meet your needs.

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Highway

The AISHE Highway is a tool that allows you to monitor and drive the AI system. It provides real-time access to the current status and results of the system and enables you to communicate with other AI systems. You can use the highway to manage the interactive management of results, news reports, and other relevant data.

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Orders

The Orders function explains how the AISHE fulfills orders and sees the results and chooses what to publish. It covers the steps for creating, placing, and monitoring orders in the system.

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Week 

The Weekly Volatility Planning function in the AISHE system provides an overview of the weekly volatility planning function. It explains how to set the volatility parameters for each trading instrument and how to create a volatility forecast for the upcoming week.

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Plan

This guide explains how to create and run plans with sessions and how to add additional neural data filter integrations using the AISHE system. It covers all the necessary steps for creating and implementing a successful trading plan.

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NSPE

The Neuronal Parameter Parameter Estimation (NSPE) environment in the AISHE system provides advanced neural network modeling capabilities. This function allows you to create 4D Variational, Advanced Kalman Filters, UKF, EnKF, PDE, SNC, and other models.

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NPS

The Neural Parameter State (NPS) Estimation provides an overview of the neural parameter state estimation function. This function allows you to create short-term, medium-term, and long-term forecasts using cloud forecasting.

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Rec

The AISHE Trading Record Status (Intervals) function provides an overview of the time recommendation system and sessions. This function allows you to analyze the trading results for selected instruments and make informed decisions about your trading activities.

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RecA

The Record Analysis system provides a detailed explanation of the record analysis system. This system allows you to analyze and select from trading sessions. You can import, load, time, export, and transfer data using the AISHE system.

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MMB

The AISHE Mind Map Buys function provides a guide to using the AISHE Mind Map Buys. This function allows you to create mind maps of buying strategies and implement them in the system.

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MMS

The AISHE Mind Map Sells function provides an overview of the AISHE Mind Map Sells function. This function allows you to create mind maps of selling strategies and implement them in the system.

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StateA

The State Analyzer function in the AISHE system provides variable analysis of daily results and creates a strategic plan for daily actions for the week. This function allows you to analyze the results of your trading activities and adjust your strategies accordingly.

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TRADE

This function provides an explanation of AISHE trade instrument status from brokers and the Chain Base and Chain Clients.

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VC

The Value Chain Index structure is a key component of the AISHE system that allows for interdependence among various templates and functions. It provides a comprehensive overview of the virtual functions and control settings for both local and external connections. With the VC, users can easily monitor and manage the value chain process to ensure optimal performance and efficiency. The VC also allows for customization and fine-tuning of the AISHE system to suit specific user needs and preferences.

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Statement

Explanation of the State Analyzer, Reversal Features for MMB, MMS for Modified Neuronal Extended Inputs.

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BadL

Learn how to import a bad list of results from external AISHE clients with experience results and filter functions. This feature allows you to improve the performance of the AISHE system by learning from past mistakes.

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DailyP

A guide to using the Daily Planner, including setting target points in templates and evaluating the best possible setting suggestions. The Daily Planner helps you stay organized and on track with your trading goals

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AISP

Explanation of daily strategic planning that facilitates the success of the artificial intelligence service provider function. This feature helps you optimize your daily trading strategy by using the collective intelligence of the AISHE system.

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History

An overview of the AISHE history log feature. This feature allows you to review past trading activity and performance, helping you to refine your trading strategy for better results.

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TUTORIAL

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Please follow the links provided for detailed descriptions of individual items.

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AISHE System and Cutting-Edge Technics

1. The AISHE system
2. Applied machine learning methods of the AISHE system
   1. self Supervised Learning (SSL)
   2. Unsupervised Learning (UL)
   3. Reinforcement Learning (RL)
   4. Transfer Learning (TL)
   5. Active Learning (AL)
   6. Online Learning (OL)
3. Learning Approaches from the AISHE system
   1. Federated Learning (FL)
   2. Cooperative Learning (CoL)
   3. Reinforcement Learning with Expert Demonstrations (RLfED)
4. Below are some of the neural networks provided by the AISHE system
   1. Neural Network (NN)
   2. Deep Learning (DL)
   3. Convolutional Neural Network (CNN)
   4. Recurrent Neural Network (RNN)
   5. Long Short-Term Memory (LSTM)
   6. Restricted Boltzmann Machine (RBM)
   7. Generative Adversarial Networks (GANs)
5. AI in Finance from the AISHE system
   1. Autonomous Trading (AU)
   2. Chart Indicators (CI)
6. AI Classifications
   1. Weak AI (WAI)
   2. Strong AI (SAI)
7. Swarm Intelligence from the AISHE system
   1. Swarm Intelligence
   2. Collective Learning
   3. Collective Intelligence
8. The AISHE system client
9. The sharing of dynamic data exchange (DDE) and real-time data (RTD) in the AISHE application improves performance significantly.
10. DDE functions in AISHE:
11. RTD features in AISHE:
12. ActiveX technology
13. Important