Strategy & Signal Processing Layer

The strategy layer is where market intelligence resides. VampedAlpha structures strategies as pure functions: given a current market snapshot, historical data, and user parameters, produce a list of trade intents. This design ensures strategies are deterministic, testable, and composable.

A strategy manager orchestrates execution frequency. Strategies can run on a time schedule (cron), be event-triggered (new block or price tick), or hybrid (conditional thresholds). This flexibility enables deployment of both reactive scalping algorithms and longer-term DCA or arbitrage strategies.

Market data ingestion relies on WebSocket subscriptions to Solana DEX aggregators and oracles (Pyth, Switchboard). Data normalization pipelines maintain rolling windows of OHLCV and order-book depth. These pre-processed feeds are essential for latency-sensitive computations like trend detection or liquidity scoring.

VampedAlpha also integrates a backtesting simulator. The simulator replays historical Solana market data, running the same strategy logic as live mode but without broadcasting transactions. This facilitates regression testing and parameter optimization, providing risk-adjusted metrics (Sharpe ratio, drawdown, fill rate) before deployment.

Each strategy module exports a metadata manifest: version, parameters, and performance stats. This allows auditability and reproducibility—important in professional trading where deterministic behavior must be provable. Through modular interfaces, developers can add new strategies without modifying the core engine.