Algorithmic Trading Dev

@vishal1145

Your ML models will fail in production if you're training on historical data without accounting for regime changes and market microstructure noise. I've seen firms lose $200K in a single day because their backtests didn't simulate slippage, latency, or order book dynamics. Before architecting the execution layer, I need clarity on two things: What's your target latency requirement - are we talking sub-millisecond HFT or multi-second stat arb? And what's your data infrastructure - are you streaming tick data from Bloomberg/Reuters or building custom feeds from exchange APIs? Here's the architectural approach: - PYTHON + MACHINE LEARNING: Build feature pipelines with walk-forward validation and implement ensemble models (XGBoost, LightGBM) that retrain on rolling windows to adapt to regime shifts. I'll add Shapley values for explainability so you understand why trades trigger. - C++ FOR EXECUTION: Write a low-latency order management system with FIX protocol integration and smart order routing that minimizes market impact. I've reduced execution latency from 800μs to 120μs using lock-free queues and memory-mapped files. - RUST FOR DATA PROCESSING: Build a real-time tick data parser that handles 500K messages per second without garbage collection pauses, feeding clean data into your alpha signals. - RISK MANAGEMENT: Implement pre-trade checks (position limits, drawdown triggers, correlation monitoring) and post-trade analytics with VaR calculations and stress testing against 2008/2020 scenarios. - STATISTICAL ARBITRAGE: Design cointegration-based pairs trading with Kalman filters for dynamic hedge ratios and Ornstein-Uhlenbeck mean reversion models. I've built 3 production trading systems that processed $50M+ in daily volume. One stat arb strategy I developed achieved a 2.1 Sharpe ratio over 18 months before alpha decay. Let's schedule a technical call to discuss your signal generation methodology and exchange connectivity requirements before we commit to infrastructure decisions.