How the AI works in DyneDollar Stablecoin?

2 min readNov 30, 2023

The core strength of DyneDollar’s AI lies in its sophisticated algorithm, which is designed to process vast amounts of data and make real-time decisions. We source data from multiple decentralized oracles and market aggregators, ensuring a comprehensive and tamper-proof view of the market. This data encompasses not only the price and volume of DyneDollar but also broader market sentiments and macroeconomic indicators. The AI processes this data to forecast potential market shifts and preemptively adjust the token’s monetary policy. As for communication, the AI system interfaces with the DyneDollar smart contract through a secure and efficient off-chain to on-chain bridge. This ensures that the AI’s decisions are seamlessly translated into on-chain actions, be it rebasing or any other adjustment. The combination of our advanced AI algorithm, reliable data sources, and efficient communication bridge positions DyneDollar as a next-generation stablecoin, capable of adapting to market dynamics like no other.

The following sections specify various components of the DyneDollar ecosystem regarding the AI system:

Data Gathering:

Price and Volume Data:

Sources: Decentralized oracles (e.g., Chainlink), cryptocurrency exchanges’ APIs, data aggregators (e.g., CoinGecko, CoinMarketCap).
Information: Real-time and historical prices, trading volumes, liquidity, etc.

Market Sentiment:

Sources: Social media platforms, news articles, forums (e.g., Twitter, Reddit, news APIs).
Information: Sentiment scores, trending topics, frequency of mentions.

Macroeconomic Indicators:

Sources: Financial news, government reports, economic databases.
Information: Inflation rates, interest rates, global economic trends.

Data Preprocessing:

Cleaning: Remove outliers, handle missing data, and correct inconsistencies.
Normalization: Scale data to ensure uniformity across different sources.
Feature Engineering: Create new variables (e.g., moving averages, volatility indices) that could be predictive.

AI Algorithms:

Time Series Forecasting:
ARIMA (AutoRegressive Integrated Moving Average): For predicting future values based on past values.
LSTM (Long Short-Term Memory): A type of recurrent neural network well-suited for sequence prediction.

Classification and Regression:

Random Forests: For classifying whether a rebase is needed and predicting the amount.
Gradient Boosting Machines (e.g., XGBoost): Similar to Random Forests but often more accurate.

Sentiment Analysis:

Natural Language Processing (NLP): Analyze text data to gauge market sentiment.
BERT (Bidirectional Encoder Representations from Transformers): Pre-trained model fine-tuned for sentiment analysis.

Clustering and Anomaly Detection:

K-Means Clustering: Group similar data points to identify patterns.
Isolation Forest: Detect anomalies in data which could indicate market manipulation.

Decision Making:

Rule-Based Systems: Define rules to trigger actions based on AI predictions (e.g., if predicted price deviation > threshold, initiate rebase).
Reinforcement Learning: Train the model to take actions (e.g., rebase) that maximize a reward (e.g., stability of the token).

Communication with the DyneDollar Smart Contract:

Off-Chain to On-Chain Bridge:

Oracles: Relay the AI’s decisions to the blockchain to trigger smart contract functions.
Web3.js/ethers.js: Libraries used to interact with Ethereum-based smart contracts.

Continuous Learning and Adaptation:

Feedback Loop: Continuously feed the results of the AI’s decisions back into the model for improvement.
Periodic Retraining: Regularly update the model with new data to ensure accuracy.

By employing this multifaceted approach, the AI system can make informed and timely decisions to maintain the stability of DyneDollar during expected events and black swans in the marketplace.