Quantegies is an AI research lab deploying next-generation quantitative strategies into global markets. We bridge the gap between deep learning and alpha generation.
For decades, quantitative finance has been dominated by "Quant 1.0": linear regressions, statistical arbitrage, and mean reversion. These methods assume markets are stationary and noise is normally distributed. In a hyper-connected world, these assumptions are collapsing.
We believe we are entering the "AI Native" era of finance. Just as LLMs learned the underlying structure of language, our Foundation Models for Finance (FMFs) are learning the latent physics of price action.
By treating the entire market history not as a spreadsheet, but as a sequential language problem, we can identify non-linear causal chains that are invisible to traditional statistical methods.
Human intuition. High latency. Unscalable.
Linear models. Feature engineering. Crowded trades.
End-to-end Deep Learning. Emergent strategies. Adaptive.
We don't just fit curves. We build agents that understand market microstructure.
Adapting the "Attention Mechanism" from NLP to financial time-series. Unlike RNNs, our Transformer architecture can attend to distant historical events (e.g., 2008 crisis patterns) to contextualize current volatility in real-time, regardless of the time lag.
Data is sparse. History only happened once. We use Generative Adversarial Networks (GANs) to generate infinite "synthetic futures"—stress-testing our agents against billions of simulated market crashes and melt-ups before they deploy a single dollar.
Alpha is worthless if you can't capture it. Our execution algorithms are Deep RL agents rewarded for minimizing market impact. They learn to hide order flow in the noise, deciphering the intentions of other HFT algorithms in the order book.
Price is a lagging indicator. We ingest petabytes of alternative data: satellite imagery of parking lots, credit card transaction receipts, and real-time semantic analysis of central bank speeches to predict price moves before they appear on the ticker.
A compute layer that scales elastically. Our Kubernetes clusters dynamically reallocate GPUs between training (research) and inference (trading) based on market volatility regimes.
We are a small team of researchers and engineers solving the hardest problems in finance. We are currently hiring for the following roles.
Don't fit these exact roles? Email us your CV anyway.