The 8-Layer Intelligence

The 8-layer intelligence explained with signals, inputs, and outputs

1) Data Aggregation

• Inputs: Exchange candles, trades, order books, DEX pools, on-chain events

• Process: Normalization, time alignment, gap-filling, and outlier detection

• Outputs: Cleaned feature store for downstream models

2) Sentiment Intelligence

• Inputs: Curated news, social streams, forums

• Models: Transformer-based classifiers; entity/event extraction

• Outputs: Asset-level sentiment scores with velocity and confidence

3) Predictive Forecasting

• Models: Ensemble of LSTM/Temporal CNNs; horizon-specific heads (e.g., 5m, 1h, 1d)

• Losses: Directional accuracy + calibration regularizers

• Outputs: Probabilistic directional forecasts and uncertainty bands

4) Model Fusion

• Technique: Stacking meta-model (e.g., gradient boosting + shallow neural nets)

• Goal: Cross-validate signals and suppress false positives

• Outputs: Final conviction score per asset and horizon

5) Risk Management

• Controls: Volatility-scaled sizing, per-asset exposure caps, portfolio VaR checks

• Objectives: Sharpe maximization subject to drawdown constraints

• Outputs: Target position sizes and global exposure limits

6) Strategy Orchestration

• Candidates: Momentum, mean-reversion, triangular arbitrage, hedging

• Policy: Contextual bandit or RL policy chooses per-market regime

• Outputs: Strategy choice and parameters (e.g., lookbacks, thresholds)

7) Smart Execution

• Routing: Best-path across DEXs with slippage, gas, and liquidity constraints

• Protections: MEV-aware routing, min-receive guards, and time-to-live

• Outputs: Signed transactions ready for submission

8) Continuous Learning

• Feedback: Realized PnL, slippage, and risk rule activations

• Updates: Policy weights and exploration rate; periodic model retraining

• Outputs: Updated model versions and policy priors