Deploying deep learning models in low-latency environments is notoriously difficult. We discuss the difficulties of overfitting on historical data.
While deep neural networks have conquered image recognition and natural language processing, their application in high-frequency trading is fraught with peril. Financial time-series data is notoriously noisy, non-stationary, and prone to regime shifts. A deep learning model that looks like a money-printing machine in backtesting is almost always suffering from extreme overfitting.
Successful algorithmic firms utilizing HarvestGroup360’s data pipelines prioritize simpler, highly regularized models (like Random Forests or regularized linear regressions) for signal generation. By utilizing our clean, unadulterated historical data to train robust, stationary features, quants can deploy machine learning models that generalize to unseen live-market data.