The AISHE system
The AISHE system is a cloud-based platform designed for real-time financial trading, powered by advanced artificial intelligence and machine learning techniques. Its blockchain network ensures secure and efficient exchange of data between clients. The system comprises two main components: the AISHE system client and the AISHE system itself.
The client is a downloadable software application that connects to the AISHE system and receives real-time data on financial market trends, news, and other relevant data. It utilizes a range of machine learning and AI techniques, such as neural networks, deep learning, and reinforcement learning, to analyze market data and execute trades in real-time. Users can customize it to their specific trading preferences and risk tolerance.
The central hub for data exchange and coordination between clients is the AISHE system itself, located in the AISHE data center. It supplies neural structures and relevant data streams to individual client systems so that each client can act independently. The system provides users with the opportunity to train their system-client for free using demo money, allowing for experience and development of trading strategies without risking real capital.
The AISHE System Client is an autonomous AI-powered system accessible to anyone with a computer, regardless of their financial or trading background. It is a powerful tool for potentially earning money in the financial markets. The system is cloud-based and can be customized to meet different strategies and preferences, making it easy to use and adaptable. By utilizing the latest AI technologies, the AISHE System Client allows users to confidently enter the world of financial opportunities. Best of all, it's completely free with no obligations for 30 days. Try it out and discover how it can help you achieve your financial goals.
Applied machine learning methods of the AISHE system
The AISHE system provides access to its applied machine learning methods for users to train and utilize their own AISHE system clients in real-time. Users can personalize their own AISHE system clients to suit their specific goals and optimize their performance in the financial market. The following applications are available: self-supervised learning (SSL), unsupervised learning (UL), reinforcement learning (RL), transfer learning (TL), active learning (AL), and online learning (OL).
self Supervised Learning (SSL)
This is a type of machine learning that trains the algorithm on a labeled dataset. The goal is to learn a mapping between the input and output variables by finding a function that can accurately predict the output given the input. The AISHE system uses SSL for a variety of financial forecasting tasks, such as B. Forex, Indices, Commodity, Stock, and Crypto Currency Price Prediction.
Unsupervised Learning (UL)
This is a type of machine learning where the algorithm is trained on an unlabeled data set. The aim is to find states and relationships within the data without prior knowledge of the data structure. The AISHE system uses UL to identify market trends and anomalies in real-time financial quotes.
Reinforcement Learning (RL)
This is a type of machine learning where the algorithm learns through trial and error by interacting with an environment. The goal is to learn the best possible action in a given situation to maximize a reward signal. The AISHE system uses RL for algorithmic trading, where the system learns the best trading strategies based on the feedbacks and corrections from the Connected AISHE system-client's.
Transfer Learning (TL)
This is a technique where a model that has been trained for a task is reused as a starting point for a new, related task. The AISHE system uses TL to improve the accuracy and speed of financial forecasts by using pre-trained models of traded experiences for related tasks.
Active Learning (AL)
This is a type of machine learning where the algorithm can actively query a user or other information source to get labeled data. The goal is to minimize the amount of tagged data required to achieve a desired level of performance. The AISHE system uses AL to minimize the need for labeled data in financial forecasting tasks.
Online Learning (OL)
This is a type of machine learning that continuously updates the model as new data becomes available. The goal is to adapt to changing data distributions and ensure the model remains accurate over time. The AISHE system uses OL to ensure its real-time financial forecasts are always up to date with market information.
Learning Approaches from the AISHE system
The AISHE system provides users with various learning approaches to train and use their own AISHE system clients in real financial market conditions. It is important to note that only trading instruments approved by the central AISHE system and for which the neural structures are available can be used. You can easily check the availability of an instrument by entering it in the AISHE system client. If the returned value is "0.0", it means that the instrument is not available. Therefore, it is necessary to check with your bank, broker, or the AISHE System Support Team to confirm and adjust the instruments before using them.
Users can personalize their clients to fit their specific goals and optimize their performance in the financial market. The following learning approaches are available:
Federated Learning (FL)
This is a machine learning approach that enables multiple parties to train a shared model using their local data, without sharing the data itself. Each party trains a model on its own data, and then shares only the model updates with a central server. The central server aggregates the model updates to generate a new global model, which is then sent back to each party to use for further training.
Cooperative Learning (CoL)
This is an approach where multiple learners collaborate with each other to learn a common task. Each learner has access to a different subset of data, and they share information with each other to improve their individual learning outcomes. This approach can be used to improve the overall performance of a machine learning system by leveraging the strengths of each individual learner.
Reinforcement Learning with Expert Demonstrations (RLfED)
This approach combines the strengths of reinforcement learning (RL) and supervised learning. In RL, an agent learns through trial-and-error interactions with its environment, while in supervised learning, the agent is provided with labeled data. In RLfED, an expert provides the agent with demonstrations of how to perform a task, and the agent uses these demonstrations to guide its own learning through RL. This approach can be used to improve the speed and efficiency of RL-based systems by reducing the amount of trial-and-error needed to learn.
Below are some of the neural networks provided by the AISHE system
The AISHE system provides users with different neural networks to train and use their own AISHE system clients under real financial market conditions. It is important to note that only trading instruments approved by the central AISHE system and for which the neural structures are available can be used. You can easily check the availability of an instrument by entering it in the AISHE system client. If the value returned is "0.0", it means that the instrument is not available. Therefore, it is necessary to confirm and adjust the instruments with your bank, broker, or the AISHE System Support Team before using them.
Neural Network (NN)
type of machine learning algorithm that are designed to simulate the behavior of the human brain. NN are composed of layers of interconnected nodes that process and transmit information, similar to the way neurons in the brain work. The connections between these nodes are weighted, allowing the network to learn from data by adjusting these weights to better predict an output based on a given input.
Deep Learning (DL)
A type of machine learning algorithm that are designed to simulate the behavior of the human brain. NN are composed of layers of interconnected nodes that process and transmit information, similar to the way neurons in the brain work. The connections between these nodes are weighted, allowing the network to learn from data by adjusting these weights to better predict an output based on a given input.NN can be used for a wide variety of tasks, including forecasting and time-series prediction for orders on the financial market. They are particularly useful for tasks that involve pattern recognition, such as stock price prediction or anomaly detection in financial data. NN can also be used for image and speech recognition, natural language processing, and many other applications.In the context of financial market prediction, NN can be trained to identify patterns and trends in historical data, which can then be used to make predictions about future market behavior. For example, a NN might be trained to predict the price of a particular stock based on factors such as its historical price, trading volume, and economic indicators. This can help traders make more informed decisions about when to buy or sell a particular security.
Convolutional Neural Network (CNN)
A convolutional neural network is a type of neural network that is particularly well-suited for image recognition tasks. It uses a process called convolution to extract features from input images, and then applies pooling operations to reduce the dimensionality of the feature maps. In financial market applications, CNNs are often used for state classification tasks, such as predicting whether a stock price will go up or down.The AISHE system uses a modified version of CNNs that applies Kalman filters to the input state short, medium, and long-term forecasts in levels 1 to 10 in the AISHE system clients. This allows the network to learn hierarchical features at different levels of abstraction, making it more effective at identifying patterns in financial data. The output of the network is a probability distribution over possible outcomes, which can be used to make trading decisions based on the predicted likelihood of different outcomes.
Recurrent Neural Network (RNN)
In the context of the AISHE system and client, the Recurrent Neural Network (RNN) is a powerful tool that allows users to analyze and predict financial market data in real-time. The RNNs in the AISHE system client are specifically designed to process sequences of data, such as time series of daily orders, and use loops to allow information to persist from one time step to the next. This means that the RNNs can capture the temporal dependencies and patterns in the data, making them well-suited for forecasting future trends and market movements.In the AISHE system client, users can train their own RNN models on historical financial data, and use these models to make predictions about future market conditions. The RNN models can be customized to fit the user's specific needs, such as the desired forecasting horizon, the level of granularity of the data, and the type of financial instruments being analyzed.The RNN models in the AISHE system client can also be used in conjunction with other neural network models, such as Convolutional Neural Networks (CNNs) or Long Short-Term Memory Networks (LSTMs), to create more powerful predictive models that can capture both temporal and spatial patterns in the financial data. Overall, the RNNs in the AISHE system client provide a powerful tool for analyzing and predicting financial market data, allowing users to make informed decisions about their investments and trading strategies.
Long Short-Term Memory (LSTM)
A type of recurrent neural network (RNN) that is designed to handle the problem of vanishing gradients in traditional RNNs. LSTMs are particularly well suited for modeling sequence data with long-term dependencies, such as natural language processing or time-series analysis. The main difference between an LSTM and a traditional RNN is that an LSTM has a more complex structure, including a cell state that can selectively forget or remember information based on gating mechanisms.The memory cell in an LSTM is the component that enables the network to store information for longer periods of time. The memory cell has three gating mechanisms: the forget gate, the input gate, and the output gate. The forget gate determines which information in the cell state should be discarded, while the input gate decides which new information should be added to the cell state. Finally, the output gate determines which information from the cell state should be outputted to the next layer or to the output of the network.In the context of the AISHE system and client, LSTMs can be used for a variety of tasks, including time-series analysis and forecasting in financial markets. By storing information for longer periods of time, LSTMs can learn to identify long-term trends and patterns in the data, and make predictions based on those patterns. The AISHE system provides users with pre-trained LSTM models that can be customized and fine-tuned for specific tasks, such as predicting stock prices or currency exchange rates.
Restricted Boltzmann Machine (RBM)
A type of generative model used for unsupervised learning, which is a type of machine learning that doesn't require labeled data. RBMs learn to represent the underlying probability distribution of the input data, which makes them useful for tasks such as dimensionality reduction and feature learning.In RBMs, the visible and hidden units are connected by weights, and the network is trained to learn the weights that best represent the input data. The weights are adjusted using a technique called contrastive divergence, which iteratively updates the weights to minimize the difference between the model's distribution and the input data's distribution.RBM has been widely used for a variety of applications, such as image recognition, speech recognition, and recommendation systems. In the context of the AISHE system, RBM can be used to learn patterns and trends in financial data and help with the statement of the day.
Generative Adversarial Networks (GANs)
A type of generative model that can be used in the AISHE system for tasks such as data augmentation and data penetration between clients. GANs consist of two neural networks: a generator network and a discriminator network. The generator network learns to generate new data samples that are similar to the training data, while the discriminator network learns to distinguish between real and generated data. The functions for implementing GANs can be found in the AIMAN management tool within the AISHE system.
AI in Finance from the AISHE system
Autonomous Trading (AU)
The AISHE system client includes an autonomous trading system that uses AI-based algorithms to analyze market data and make trading decisions in real-time. The system uses machine learning algorithms and deep neural networks to automate trading decisions, allowing traders to create custom trading models that can make decisions based on market trends and other factors without the need for human intervention.Traders using the AISHE system client have a high level of customization and control over their trading strategies. They can set their own parameters and risk levels, and the system automatically adjusts to changing market conditions. The autonomous trading system can also be started manually using action buttons, giving traders more flexibility and control.
Chart Indicators (CI)
The AISHE system client does not integrate chart indicators directly into its platform. However, traders can use their own chart indicators to analyze market data and identify potential trading opportunities. The client's AI-based algorithms can provide directions or trends, as well as alerts and notifications based on its own insights, helping traders stay informed and react quickly to market changes.Some common chart indicators that traders may use include moving averages, MACD, RSI, and Bollinger Bands, among others. These tools help traders spot patterns and trends in market data and can be useful in making informed trading decisions. However, it is important to note that the AISHE system client does not provide direct access to chart indicators, so traders must use external tools to incorporate them into their trading strategies.
AI Classifications
Weak AI (WAI)
Also known as narrow AI, this type of AI is designed to perform a specific task or solve a particular problem. Weak AI systems are not capable of generalizing their knowledge to other domains, and they require significant human supervision to function properly. Examples of WAI include voice assistants like Siri or Alexa, chatbots, and recommendation engines.
Strong AI (SAI)
Also known as artificial general intelligence (AGI), this type of AI aims to develop machines that can perform any intellectual task that a human being can. Strong AI systems would be able to understand and reason about the world, learn from experience, and make decisions on their own. While SAI is still a long way off, some researchers believe that it is achievable in the future.
Swarm Intelligence from the AISHE system
The AISHE System provides users with different Swarm Intelligence tools to train and use their own AISHE system clients in real financial market conditions. It is important to note that only trading instruments approved by the central AISHE system are supported.
Below are some of the neural networks provided by the AISHE System and AISHE system clients:
Swarm Intelligence
Swarm Intelligence refers to the collective behavior exhibited by decentralized and self-organized systems, typically inspired by the social behavior of animals or insects. In the AISHE system clients, Swarm Intelligence is utilized in the development of algorithms that simulate the collective behavior of groups of AISHE system clients to solve complex problems. The Swarm Intelligence approach is especially useful for tasks that cannot be solved by a single AISHE system client or traditional computing algorithms.
Collective Learning
Collective Learning refers to the process by which a group of AISHE system clients learn together to improve their individual and collective performance. In the AISHE system clients, Collective Learning is achieved through the use of Swarm Intelligence algorithms, which allow AISHE system clients to share information and learn from one another. This approach has been particularly useful in the development of financial trading strategies, where a group of AISHE system clients work together to make trading decisions based on market conditions and past performance.
Collective Intelligence
Collective Intelligence refers to the ability of a group of AISHE system clients to solve problems that are beyond the capabilities of any individual AISHE system client. In the AISHE system, Collective Intelligence is achieved through the use of Swarm Intelligence algorithms, which allow AISHE system clients to share information and work together to solve complex problems. This approach has been particularly useful in the development of predictive models for financial trading, where a group of AISHE system clients work together to analyze market data and make trading decisions based on their collective intelligence.
The AISHE system client
The AISHE system client is a software application that provides users with access to the cloud-based real-time financial trading platform, AISHE system. The client is compatible with Windows 10/11 operating systems and requires Microsoft Office Excel 2016/2019.
Using machine learning and AI techniques such as supervised learning, unsupervised learning, reinforcement learning, transfer learning, active learning, and online learning, the AISHE system client enables users to analyze financial data and make trading decisions.
The client's key feature is its ability to be individually trained by users, allowing them to create customized models tailored to their specific trading strategies and goals. The client also provides users with real-time market data and supports DDE and RTD for real-time trading.
To use the AISHE system client, users must download the software from the AISHE website and install it on their Windows 10/11 operating system. Additionally, they require a trading environment from their bank or broker, such as Meta Trader 4, which supports DDE and RTD. The AISHE system client can connect to different trading platforms for trading and execute trades.
The client is free to download and comes with demo money, allowing users to practice trading without risking real funds. Once the client is installed, users can connect it to the AISHE system and start training their models using the available machine learning and AI techniques.
The sharing of dynamic data exchange (DDE) and real-time data (RTD) in the AISHE application improves performance significantly.
DDE is a legacy protocol that allows AISHE to communicate and exchange data with other applications. DDE is asynchronous, which means AISHE has to wait for data sent by another application. However, it can be useful when data does not need to be updated in real time.
RTD, on the other hand, allows AISHE to access real-time data from another application. RTD operates synchronously, allowing AISHE to receive and display data in real time. This is necessary because data needs to be updated in real time.
So, sharing DDE and RTD in an AISHE application can take advantage of both protocols. For example, the application that uses DDE to provide historical data to AISHE can use the RTD function to send real-time data to AISHE. This allows AISHE to access historical data while processing and displaying real-time data.
An example of the joint use of DDE and RTD in the AISHE application is the display of stock prices. The AISHE uses DDE to send historical price data while at the same time using RTD to send real-time prices to AISHE. This allows the AISHE client to display historical rate data while updating real-time rates.
It is important to note that using DDE and RTD together has some complexities and requires careful planning. For example, DDE and RTD servers must be configured to communicate with the AISHE application. In addition, the AISHE application must be configured to correctly process the data from both protocols.
Overall, the DDE and RTD is a powerful combination to leverage an AISHE that can process both historical and real-time data. However, correct implementation requires careful planning and configuration of all components involved.
DDE functions in AISHE:
- The DDE function in AISHE is used to receive data from other applications that support the DDE protocol.
- The syntax for the DDE function is "=DDE(Server, Topic, Item)".
- Server: The name of the DDE server to communicate with.
- Topic: The topic that defines the type of data being accessed.
- Item: The name of the item or data being accessed.
- The DDE function is a volatile function, meaning it is recalculated each time a change in AISHE occurs.
Dynamic Data Exchange (DDE) is a method that allows applications to communicate with each other by exchanging data directly. In AISHE, DDE allows other applications to read or write data from an AISHE protocol.
DDE is normally activated via the Windows clipboard. When an application connects to another application, it opens a DDE channel to exchange data. The two applications can then send and receive messages over the DDE channel to exchange data.
In order to use DDE in AISHE, you need a so-called DDE formula. A DDE formula always begins with an exclamation mark (!) followed by the application you want to communicate with, followed by a keyword that defines the type of action you want to perform, and finally the parameters used for the action required are.
Here is an example of a DDE formula in AISHE, which takes the price of EURUSD "1.06541" into the AISHE system from the metatrader and inserts it into a cell:
=PROTOCOL|APPLICATION!COMMAND|PARAMETER
The components of the DDE formula are as follows:
- PROTOCOL: The protocol used for communication. For DDE, this is usually "DDE".
- APPLICATION: The name of the application you want to communicate with. In this case it would be "HIGHWAY".
- COMMAND: The keyword that defines the action you want to perform. In this case it would be "InsertPrice".
- PARAMETERS: The parameters required for the action. In this case, that would be the number "1.06541".
If you enter this formula in a cell and update the cell, the number "1.06541" is inserted into AISHE.
RTD features in AISHE:
- The RTD function in AISHE is used to access real-time data provided by another application.
- The syntax for the RTD function is "=RTD(Server, Topic1, Topic2, ...)".
- Server: The name of the RTD server providing the data.
- Topic1, Topic2, ...: The topics or data being accessed. These can be any number of topics or dates.
- The RTD function is a non-volatile function, meaning it is only recalculated when the data being accessed changes.
Real-Time Data (RTD) is a method that allows AISHE to access real-time data from another program or application. Unlike DDE, which works asynchronously, RTD works synchronously, allowing AISHE to receive and display data in real time.
RTD is normally activated by using a special function in AISHE, the RTD function. The RTD function has three required parameters:
ProgID: The program identifier (ProgID) of the application or program providing the data.Server: The server name or IP address of the computer running the program providing the data.Topic: A unique identifier for the type of data being served.
Once the RTD function is configured, AISHE periodically calls the function to retrieve the data. When new data is available, the RTD function returns it to AISHE, and AISHE updates the cell with the new data.
Here is an example using the RTD function in AISHE:
=RTD("ProgID","Server","Topic")
The components of the RTD function are as follows:
ProgID: The ProgID of the application or program providing the data. The ProgID identifies the program and gives AISHE the ability to access it. Examples of ProgIDs are "AISHE.Application" for another AISHE instance or "MSWinsock.Winsock.1" for a Winsock control.Server: The name of the computer running the program that provides the data. This can be the local computer name or the name of a remote computer.Topic: A unique identifier for the type of data being served. The Topic parameter is set by the application and defines what type of data is served.
It is important to note that RTD is only updated when AISHE is running and the RTD function is active in the workbook. If AISHE is not active or closed, no data will be updated.
RTD is a powerful feature that allows AISHE to access and display real-time data. However, it requires a configured application that provides data and a proper implementation of the RTD function in AISHE.
That the use of DDE and RTD functions has some complex aspects and requires careful planning. For example, DDE and RTD servers must be configured to communicate with the AISHE application. Also, the AISHE application needs to be configured to correctly process the data from both protocols.
ActiveX technology
The AISHE Client Application is designed to handle incoming data and requests in real-time, providing users with a powerful tool for data analysis and processing. To achieve this functionality, the application utilizes a variety of technologies, including DDE, RTD, and ActiveX controls.
ActiveX technology plays a crucial role in the AISHE application by allowing for seamless communication and integration with other applications and programming languages. This cooperative intelligence enables the AISHE application to interact with external data sources and leverage their capabilities to enhance the application's functionality.
For example, the AISHE application may use ActiveX controls to interact with external databases or web services, allowing users to access a wealth of data that would otherwise be unavailable. ActiveX controls can also be used to add interactivity to the application's user interface, making it more intuitive and user-friendly.
By leveraging the power of ActiveX technology, the AISHE application can take advantage of the strengths of other applications and programming languages to improve its own performance and capabilities. The result is a powerful tool for data analysis and processing that can provide users with valuable insights and actionable information.
The use of ActiveX technology in the AISHE application is a critical component of its cooperative intelligence, enabling seamless communication and integration with other applications and programming languages.
Important
The AISHE client-application is a robust AI software application that utilizes a variety of technologies to handle incoming data and requests in real-time. Specifically, the application uses DDE, RTD, and ActiveX controls to achieve this functionality.
- DDE is an important part of the application as it enables communication with external applications that support the DDE protocol. When the application receives data from an external source, it can process the data in real time. The application can also send data to external applications via DDE.
- The RTD function is also an integral part of the AISHE client application. This feature allows the application to get real-time data from external sources such as stock tickers. When the data changes, the RTD function updates the data in real time. This data can be processed, allowing the application to perform real-time calculations and processing.
- ActiveX controls are used extensively in the AISHE client application to add functionality and interactivity to the user interface. The ActiveX control interacts in real time with the AISHE central system to process data. In addition, ActiveX controls are used to interact with external applications as well.
The AISHE client-application is designed to handle incoming data and requests in real-time, making it a powerful tool for real-time calculations and processing. Examples of how the application processes data in real-time using VBA code, and how it uses DDE, RTD, and ActiveX controls to interact with external data sources and applications can be provided. Overall, the combination of DDE, RTD, and ActiveX controls enables the AISHE client-application to deliver real-time functionality that is essential in a variety of industries and use cases.