# 2. What is OracleX? - Predicting the Future of DEXs

#### **2.1 Price Prediction AI Engine**

OracleX's "Price Prediction AI Engine" is its most important component, forming its core and the source of its advanced predictive capabilities. This engine combines multiple state-of-the-art machine learning algorithms with vast amounts of on-chain and off-chain data to achieve unprecedented levels of price prediction accuracy.

First, the diversity and comprehensiveness of the data sources are one of the strengths of this engine. The on-chain data collected directly from the blockchain includes each token's historical price movements, trading volume, detailed order book information, and liquidity information of each DEX. These data are collected in real-time via smart contracts and are constantly kept up-to-date. On the other hand, off-chain data is collected from a wide range of sources, including cryptocurrency-related news articles, social media (Twitter, Reddit, etc.) posts, major economic indicators, and even the opinions of experts and influencers. These data are analyzed using Natural Language Processing (NLP) techniques and utilized to understand market sentiment and trends.

Secondly, the adoption of multiple machine learning algorithms greatly contributes to the improvement of prediction accuracy. Specifically, multiple advanced deep learning algorithms are used in combination, including Long Short-Term Memory (LSTM) networks, which excel at analyzing time-series data, and Transformer models, which are adept at recognizing complex patterns. Furthermore, rather than using these algorithms alone, a method called ensemble learning is employed to integrate the prediction results of each model. Ensemble learning is a cutting-edge approach in machine learning that combines predictions from multiple different models to achieve more robust and accurate predictions than relying on a single model.

This engine not only predicts future prices but also provides detailed analysis based on historical data. Users can analyze specific tokens from various angles, including past price movements, changes in prediction accuracy, and changes in market sentiment. This allows users to understand the factors behind the prediction results and gain deeper insights.

Furthermore, this "Price Prediction AI Engine" is developed on the G.A.M.E framework. This enables continuous data collection and feedback loops, allowing it to continue learning and growing over time. Each time new data is added, the models are retrained, and prediction accuracy improves. User feedback and evaluations of prediction results are also used to improve the models. In this way, OracleX's "Price Prediction AI Engine" is a constantly evolving, self-learning prediction system.

**2.2 Token Economics (ORX)**

The OracleX ecosystem is designed around its native token, "ORLX".

The ORLX token serves as the cornerstone of value exchange and incentive design within the ecosystem, playing a vital role in supporting its sustainable growth and development.

ORLX has three primary functions:

1. **Governance Voting:** ORLX token holders gain the right to participate in important decision-making regarding the future of OracleX. This includes updates to prediction models, API specification changes, and token economics adjustments. Voting power is granted according to the amount of tokens held, and through a decentralized decision-making process, the community can determine the direction of the project in a community-driven manner.
2. **Access to Prediction Data:** ORLX tokens are required to access the highly accurate price prediction data provided by OracleX. API users can obtain prediction data by paying ORLX tokens according to their usage. This mechanism ensures that the ORLX token has real value and creates demand.
3. **Rewards for Data Providers:** OracleX rewards data providers who contribute to improving prediction accuracy with ORLX tokens. By incentivizing the provision of high-quality data, the overall data quality of the ecosystem improves, ultimately leading to improved prediction accuracy.

The total supply of ORLX tokens is fixed at 1 billion, following the standard of the Virtuals Protocol.

Initially, tokens will be distributed to the team, foundation, ecosystem development fund, strategic partners, and public sale participants.

Furthermore, the ORLX token implements a buyback and burn mechanism.

This mechanism uses a portion of the API usage fees to periodically buy back ORLX tokens from the market and burn (destroy) them, reducing the circulating supply and increasing the scarcity of the token.

This deflationary mechanism is expected to contribute to the long-term value appreciation of the ORLX token.

Additionally, ORLX token holders can stake their tokens to contribute to the security and stability of the network and earn rewards in return.

Staking rewards are paid from a portion of the rewards for data providers and validators, increasing the incentive to hold ORLX tokens and encouraging active participation in the ecosystem.

Thus, the ORLX token is the source of value in the OracleX ecosystem, and it is designed with a sophisticated incentive structure to support its healthy growth.

**2.3 Evaluation and Improvement of Prediction Accuracy**

In OracleX, to ensure the reliability of the provided price prediction information, we are continuously and thoroughly committed to the evaluation and improvement of prediction accuracy.

First, all prediction results are rigorously verified against actual data.

Specifically, we track the actual price movements of the predicted tokens, and quantitatively evaluate the difference (error) between the predicted price and the actual price.

This evaluation process prioritizes transparency.

Key evaluation metrics, such as the error rate, are made public through a dashboard and API, allowing users to constantly monitor them and ensuring accessibility for everyone.

Furthermore, we are committed to not relying on a single metric for evaluating prediction accuracy, but rather using multiple evaluation metrics for a multifaceted approach.

For example, we evaluate not only Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), but also the accuracy of the prediction direction (up/down).

This allows users to understand the accuracy of the predictions from various aspects, and to appropriately decide how much to trust the predictions based on their own judgment.

Moreover, OracleX has established a mechanism to actively utilize feedback from the community to improve prediction accuracy.

Users can directly deliver their opinions and suggestions for improvement on prediction results to the development team through a dedicated forum or channel.

This feedback is directly used to improve the model and add new data sources, thereby enhancing the prediction engine.

Additionally, we are introducing an incentive program that rewards users who make significant contributions to prediction accuracy with ORLX tokens.

This creates a user-participatory, continuous improvement cycle.

Furthermore, regular audits by experts are also planned.

By receiving objective evaluations and advice from third-party organizations, we aim to identify potential issues in the prediction model and further improve its accuracy.

In this way, OracleX is fully committed to the continuous improvement of prediction accuracy through a highly transparent evaluation process, close collaboration with the community, and audits by experts.
