Research institute advancing fundamental understanding of algorithmic systems, applied AI, and market pattern formation.
A framework to study transformations that preserve causal features in noisy sequences, improving regime detection in market microstructure data.
Methods for uncertainty calibration and drift monitoring when learning from imbalanced, high-frequency sequences.
Characterization of recurring microstructure motifs and their role in liquidity provision during regime shifts.
Systematic Trading Lab is a private research initiative dedicated to rigorous study of market dynamics, algorithms, and AI. We build open methods, datasets, and tools that improve understanding of risk and patterns in financial markets. Our research focuses on quantitative finance methodologies, machine learning applications for time series analysis, and ethical AI development for financial systems.
Through open-source contributions and collaborative research, we aim to democratize access to advanced financial modeling tools while maintaining the highest standards of academic rigor. Our work bridges the gap between theoretical research and practical applications in systematic trading and risk management. We do not provide portfolio management, brokerage, or investment advice.
Our diverse team comprises experts in quantitative finance, computer science, and data ethics, united by a passion for rigorous research and open innovation.
Self funded research programs.
We handle personal information in accordance with PIPEDA, Québec's Law 25, CASL, and applicable US privacy laws. We collect minimal data and use it solely for academic purposes and operations.
Materials are provided "as is" for academic use. No warranties. No individualized guidance or recommendations are provided. Usage must respect licenses.
We respect US and Canadian securities laws. Content is strictly academic. No content constitutes investment, legal, or financial advice. We do not publish signals, recommendations, or individualized guidance.
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