3/ Machine Learning Engine

The Machine Learning Engine is the core component of Dash AI framework, enabling predictive analytics, anomaly detection, and market forecasting with precision. It integrates a suite of machine learning models, each optimized for specific data types and use cases, ensuring robustness and scalability in the dynamic Sonic ecosystem.

Model Frameworks

Dash multi-model architecture blends the strengths of supervised, unsupervised, and probabilistic learning algorithms:

1. Recurrent Neural Networks (RNNs):

   • Designed to capture sequential dependencies in time-series data, RNNs predict token price movements and liquidity trends based on historical patterns.

   • Formula: ht=σ(W⋅xt+U⋅ht−1+b)h_t = \sigma(W \cdot x_t + U \cdot h_{t-1} + b)ht​=σ(W⋅xt​+U⋅ht−1​+b) Where:

     • hth_tht​: Hidden state at time ttt.

     • xtx_txt​: Input vector (e.g., token price or trading volume at time ttt).

     • W,U,bW, U, bW,U,b: Trainable weights and biases.

   • Example:

     • Predicting the next price Pt+1P_{t+1}Pt+1​ given historical prices [P1,P2,...,Pt][P_1, P_2, ..., P_t][P1​,P2​,...,Pt​]: Pt+1=ϕ(ht)P_{t+1} = \phi(h_t)Pt+1​=ϕ(ht​)

2. Graph Neural Networks (GNNs):

   • GNNs analyze wallet-to-wallet interactions and liquidity flows, constructing a graph G=(V,E)G = (V, E)G=(V,E), where VVV represents wallets and EEE represents transactions.

   • Centrality Metrics:

     • Identifies influential wallets using metrics like PageRank or Betweenness Centrality.

     Cnode=∑i∈neighbors1diC_{\text{node}} = \sum_{i \in \text{neighbors}} \frac{1}{d_i}Cnode​=i∈neighbors∑​di​1​

     • did_idi​: Degree of node iii.

   • Use Case:

     • Detecting whales accumulating tokens or coordinated behaviors between wallets.

3. Bayesian Inference Models:

   • These models calculate the likelihood of specific market events based on observed data.

   • Bayes’ Theorem: P(E∣X)=P(X∣E)⋅P(E)P(X)P(E|X) = \frac{P(X|E) \cdot P(E)}{P(X)}P(E∣X)=P(X)P(X∣E)⋅P(E)​ Where:

     • EEE: Event (e.g., price increase).

     • XXX: Observed evidence (e.g., increased trading volume).

Training Methodology

Dash ML Engine is trained using a comprehensive methodology to ensure accuracy and adaptability:

1. Historical Data:

   • Models are trained on three years of Sonic blockchain logs, encompassing transaction volumes, wallet interactions, and liquidity metrics.

2. Reinforcement Learning (RL):

   • RL agents simulate trading strategies to optimize decision-making.

   • Reward Function:

     R(a)=(ΔP−C(a))⋅Volume Impact FactorR(a) = (\Delta P - C(a)) \cdot \text{Volume Impact Factor}R(a)=(ΔP−C(a))⋅Volume Impact Factor

     Where:

     • aaa: Action (e.g., buy, sell).

     • ΔP\Delta PΔP: Price change.

     • C(a)C(a)C(a): Transaction cost.

   • Example:

     • An agent learns to minimize slippage and maximize profit by dynamically adjusting trade sizes based on liquidity depth.

3. Real-Time Fine-Tuning:

   • Models are continuously retrained every 24 hours, leveraging the latest market data to adapt to evolving conditions.

   • Gradient Descent Update: Wt=Wt−1−η⋅∇L(Wt−1)W_t = W_{t-1} - \eta \cdot \nabla L(W_{t-1})Wt​=Wt−1​−η⋅∇L(Wt−1​) Where:

     • WtW_tWt​: Model weights at time ttt.

     • η\etaη: Learning rate.

     • LLL: Loss function.

Key Metrics

Dash ML Engine achieves best-in-class performance across multiple benchmarks:

1. Prediction Accuracy:

   • Short-Term Price Predictions:

     • Mean Absolute Error (MAEMAEMAE) of <1.5%: MAE=1n∑i=1n∣Pactual,i−Ppredicted,i∣MAE = \frac{1}{n} \sum_{i=1}^n |P_{\text{actual}, i} - P_{\text{predicted}, i}|MAE=n1​i=1∑n​∣Pactual,i​−Ppredicted,i​∣

2. Anomaly Detection:

   • GNN-based anomaly detection identifies suspicious wallet behaviors with a True Positive Rate (TPR) of 98%.

3. Latency:

   • Predictions are generated within <10ms per token, ensuring real-time usability.

Use Cases

The ML Engine powers several core functionalities of Dash:

1. Market Forecasting:

   • Predicts token price trends based on liquidity, trading volume, and historical price movements.

2. Whale Tracking:

   • Identifies large wallet accumulations or dumps to alert users of significant market movements.

3. Fraud Detection:

   • Flags suspic