Quantitative Research
Engineering intelligence for financial markets.
Alpha Neuron applies machine learning to develop systematic trading strategies.
Our Focus
Signal Research
Exploring predictive patterns in financial data using machine learning and statistical methods.
Strategy Prototyping
Building and testing systematic trading strategies through rigorous backtesting and simulation.
Systems Development
Developing infrastructure for data pipelines, model training, and strategy evaluation.
Research Pipeline
Machine Learning Throughout
We approach quantitative research as an end-to-end machine learning problem. From data ingestion to strategy evaluation, every stage is designed to be systematic and reproducible.
This integrated approach allows us to iterate quickly, test hypotheses rigorously, and build on what works.
Data Collection
Aggregating and processing diverse financial and alternative datasets.
Feature Engineering
Transforming raw data into meaningful inputs for machine learning models.
Model Development
Training and validating predictive models for market behavior.
Backtesting & Evaluation
Rigorously testing strategies against historical data.
Research & Insights
View allShallow Neural Networks for Trading: Why Simple Models Win
A shallow MLP with aggressive regularization outperformed RSI mean reversion by 175 percentage points in out-of-sample testing. Simple models beat complex ones when data is limited.
Read MoreAn Honest Take on Algorithmic Trading
An honest look at what algorithmic trading is actually like - the failures, the lessons, and why you shouldn't quit your day job just yet.
Read MoreWhy Basic Momentum and Mean Reversion Don't Work Anymore
A data-driven autopsy of momentum and mean reversion strategies, showing why textbook approaches fail in modern markets.
Read MoreGet in Touch
Interested in our research or want to discuss quantitative finance and machine learning? We're always open to conversations with like-minded researchers and practitioners.
Contact Us