Day Trader

Overview

This project is a full algorithmic trading platform — from data ingestion and feature engineering through model training, backtesting, and live execution. It uses ensemble ML models trained on historical market data combined with event-driven signals from news pipelines. The backtesting framework simulates realistic market conditions including slippage and commission. The live trader connects to brokerage APIs for real-time execution with position management, stop-losses, and portfolio rebalancing. Everything runs as background processes with monitoring and alerting.

Highlights

• Ensemble ML models with automated training and evaluation
• Event-driven strategy engine processing real-time news signals
• Full backtesting framework with realistic market simulation
• Live execution with position management and risk controls
• Dockerized deployment with background workers and monitoring