CUSTOM SOFTWARE FOR QUANTITATIVE RESEARCHERS & TRADERS
Custom research platforms tailored to your quantitative strategies. Develop backtesting engines, statistical analysis tools, and data visualization frameworks that empower your researchers to test and validate trading hypotheses with precision.
Build robust data infrastructure for market data ingestion, cleaning, and storage. Create high-performance pipelines that handle tick data, order book information, and alternative datasets, ensuring your quant models have access to clean, reliable data.
Transform your trading ideas into production-ready algorithms. Implement complex quantitative strategies, from statistical arbitrage to machine learning models, with a focus on performance optimization and risk management tailored to your requirements.
Deploy scalable infrastructure for your quantitative operations. From low-latency execution systems to distributed computing clusters for portfolio optimization, build the technical foundation that supports your trading edge.
Build comprehensive research environments with Python-based frameworks including pandas for data manipulation, NumPy for numerical computing, and scikit-learn for machine learning. Custom backtesting engines support vectorized operations for rapid strategy evaluation across multiple timeframes and instruments.
Deploy low-latency execution systems using C++ for performance-critical components and Python for strategy logic. Implement FIX protocol connectivity, smart order routing, and real-time risk management to ensure reliable trade execution across multiple venues.
Process and store massive datasets with time-series databases optimized for financial data. Custom pipelines handle tick data ingestion, normalization, and storage with microsecond precision, enabling accurate historical analysis and real-time strategy monitoring.
Develop mean-reversion and pairs trading strategies using cointegration analysis, Kalman filters, and advanced statistical methods. Implement sophisticated models that identify and exploit temporary price inefficiencies across correlated instruments.
Build predictive models using ensemble methods, neural networks, and reinforcement learning. From feature engineering to model validation, create ML pipelines that adapt to changing market conditions while avoiding overfitting.
Implement modern portfolio theory, risk parity, and factor-based allocation strategies. Develop optimization frameworks that balance return objectives with risk constraints, incorporating transaction costs and market impact models.
Develop sophisticated systems that process Level 1 (best bid/ask) and Level 2 (order book depth) data for market making and liquidity analysis. Custom solutions decode market microstructure signals, order flow imbalances, and depth dynamics to identify trading opportunities in real-time.
