From raw market data to executed trades — here's every step of the ML-powered pipeline.
Six stages transform raw market noise into precision trade execution — fully automated, continuously learning.
Real-time price feeds from multiple exchanges are ingested into our AWS infrastructure. We capture tick data, order book snapshots, volume profiles, and macro indicators across 14+ instruments including NQ, ES, RTY, CL, and GC.
Raw data is transformed into 200+ engineered features: momentum oscillators, volatility regime classifiers, order flow imbalance metrics, cross-asset correlation matrices, and micro-structure signals. Each feature is normalized and validated before model consumption.
Our ensemble model stack — LSTM sequence models, Transformer attention layers, and Gradient Boosted Trees — processes the feature set in parallel. A meta-learner aggregates predictions, weighting each model by its recent accuracy. The output is a directional probability with confidence score.
Before any signal reaches execution, it passes through multi-layer risk checks: position size validation against Kelly Criterion, portfolio correlation screening, drawdown circuit breaker status, and max daily exposure limits. Only signals that pass all gates proceed.
Validated signals are routed through AWS Lambda to the Tradovate API with sub-millisecond latency. The execution layer handles order type selection (limit, market, bracket), slippage estimation, and real-time fill confirmation. Every execution is logged to DynamoDB.
Trade outcomes feed back into the training pipeline. The system analyzes what worked, identifies regime shifts, and triggers model retraining via AWS Step Functions. This closed-loop architecture ensures the engine continuously evolves and adapts to changing market conditions.
A fully serverless, event-driven architecture built for reliability, scalability, and speed.
Every signal goes through this journey — from detection to execution to learning.
Pine Script strategies on TradingView scan for setups using custom indicators. When conditions align, a webhook fires to our API Gateway endpoint.
Lambda receives the alert, validates the payload, checks ML confidence threshold (minimum 0.87), and runs risk pre-checks before proceeding.
Approved signals are routed to Tradovate with optimal order type, calculated position size, and bracket orders for stop-loss and take-profit management.
Start your free trial and watch the ML pipeline process live market signals in real-time.