Yield Optimiser Model

High level overview of Lumia's Off-Chain Model

1. Sentiment Analysis with Natural Language Processing (NLP)

To capture the social media presence and sentiment around DeFi platforms, particularly on Twitter, we will employ NLP techniques:

• Collect tweets related to DeFi platforms using relevant hashtags, token tickers, and keywords.

• Preprocess the tweet data by removing noise, handling abbreviations, and normalizing the text.

• Apply sentiment analysis models, such as VADER or BERT-based models, to quantify the sentiment associated with each platform.

• Incorporate the sentiment scores as features in the yield optimization model to capture the impact of social media sentiment on potential yields.

2. Time Series Forecasting with LSTM

To predict future APYs and capture the temporal dynamics of yield rates, we can use LSTM (Long Short-Term Memory) networks, a variant of recurrent neural networks (RNNs):

• Collect historical data on APYs, TVL growth, and other relevant time-dependent variables for each DeFi platform.

• Preprocess the time series data by normalizing, handling missing values, and creating appropriate time lags.

• Train an LSTM model to learn the temporal patterns and dependencies in the yield data.

• Use the trained LSTM model to forecast future APYs and capture potential APY drops based on TVL acquisition speed.

3. Ensemble Modelling

To incorporate multiple factors and improve the robustness of the yield optimization model, we will create an ensemble of different models:

• Build individual models for each factor, such as:

  • Regression models for the relationship between social media sentiment and yields.

  • Decision tree models for the impact of fees on yield optimization.

  • Gradient boosting models for the relationship between TVL growth and yields.

• Combine the predictions from these individual models using ensemble techniques like stacking or weighted averaging.

• The ensemble model will take into account the outputs from the sentiment analysis, time series forecasting, and other factor-specific models to provide a comprehensive yield recommendation.

4. Reinforcement Learning for Dynamic Optimization

To adapt to changing market conditions and optimize yields dynamically, we will employ reinforcement learning (RL) techniques:

• Formulate the yield optimization problem as a sequential decision-making task, where the RL agent (model) learns to make optimal decisions based on the current state of the DeFi ecosystem.

• Define the state space to include relevant factors such as social media sentiment, TVL growth, APYs, and fees.

• Define the action space as the selection of DeFi platforms or investment strategies.

• Design a reward function that incentivizes the RL agent to maximize yields while considering factors like APY drops and platform stability.

• Train the RL agent using algorithms like Q-learning or policy gradients to learn the optimal yield optimization strategy.

5. Continuous Learning and Adaptation

To ensure the model remains up-to-date and adapts to new trends and platforms, we will implement a continuous learning framework:

• Regularly collect new data on social media sentiment, APYs, TVL growth, and other relevant factors.

• Retrain the individual models (sentiment analysis, time series forecasting, factor-specific models) with the updated data.

• Fine-tune the ensemble model and the RL agent based on the latest data and user feedback.

• Monitor the performance of the yield optimization model and make adjustments as necessary.

High Level Overview of Lumia's Off-Chain Yield Optimiser Model

By combining these AI model methods - sentiment analysis with NLP, time series forecasting with LSTM, ensemble modelling, reinforcement learning, and continuous learning - Lumia L2 will create a robust and adaptive yield optimization model. This model will take into account various factors influencing DeFi yields, adapt to changing market conditions, and provide data-driven recommendations to maximize user returns.