What is OBA?
Creating Unparalleled Investment Solutions
The OBA token is the core value carrier of the OBA project, supporting the platform's operations, governance, and
ecosystem incentive mechanisms. As an ERC-20 token based on Ethereum, OBA offers high compatibility and security,
enabling wide application across various decentralized scenarios. It plays a key role in supporting the development
and promotion of Orbis IA 6.0 technologies and a decentralized financial education ecosystem.
The OBA token is not only an internal payment medium within the platform but also a driving force for innovation and
community governance. Through blockchain technology, the OBA token ensures a high level of transparency and
immutability in transactions, providing users with a stable and secure asset management and value transfer
experience.
About OBA
AI Matrix Trading System Design
The core of the OBA project is its AI Matrix Trading System, which aims to enhance the quality and speed of trading decisions through high levels of automation and intelligence. The system is composed of several modules, including data collection, data processing, decision support, and execution strategies, ensuring that users can make informed investment choices swiftly in complex market environments.
Valmont Business School
Valmont Business School, founded in 2018 with the support of Fidelity Investments, is OBA's primary strategic partner. Fidelity Investments, one of the world's largest asset managers with over $4 trillion under management, invested in the school to develop AI+finance.
The school specializes in research and development of the Orbis IA 6.0 system, currently in the final testing phase before commercialization. The team led by Lioran Vesse follows the philosophy: "Making investment the simplest thing in the world" and "Creating love for investment".
Machine Learning
Through supervised and unsupervised learning, the system can identify complex patterns in market data and predict future trends based on historical data. For example, regression analysis and decision tree models can be used for price forecasting, while cluster analysis can identify market segmentation.
Deep Learning
Utilizing neural network models (such as Convolutional Neural Networks and Recurrent Neural Networks) for data processing and analysis, deep learning is capable of handling unstructured data such as images and texts to identify potential trading signals. These models can capture subtle changes in market trends, thereby improving forecasting accuracy.
Natural Language Processing (NLP)
NLP technology is used to analyze social media, news reports, and financial commentary to extract sentiment and topics. This enables the system to quickly assess market sentiment and generate trading suggestions.
Time Series Analysis
In the OBA AI Matrix Trading System, time series analysis can be used to predict future trends of financial assets such as currency exchange rates and stock prices. By applying models like Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), or Seasonal ARIMA (SARIMA), the system can capture dynamic changes in financial markets and develop trading strategies accordingly.
Reinforcement Learning
In the decision support module, reinforcement learning algorithms can optimize trading strategies by simulating trading environments and maximizing long-term returns. The algorithm continuously adjusts strategies and adapts to different market conditions through trial and error.
Data Fusion and Multi-Source Data Analysis
The OBA system can integrate data from various sources, such as exchange data, social media information, and economic indicators. Through data fusion technologies, the system provides more comprehensive and accurate market analysis.
