Blockhouse’s Post

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Tons of Alpha was shared at Columbia University's Workshop on Financial Technology and Data Sciences, organized by Agostino Capponi. Blockhouse had the opportunity to present our novel approach of using specialized transformer architectures to minimize transaction costs and optimize trade execution. We demonstrate the key benefits of of our model, including: ✔ Enhanced Temporal Pattern Recognition. ✔ Dynamic Feature Selection. ✔ Multi-Horizon Forecasting. ✔ Interpretability. ✔ Robustness and Flexibility. We also benchmark our method against traditional ML and RL techniques, demonstrating significant improvements in prediction accuracy, execution quality, and adaptability. Our transformer-based TCA models can lead to substantial cost savings and an increased competitive edge in financial markets. For those interested in diving deeper into our findings or exploring collaborative opportunities, feel free to connect with us. #Fintech #QuantitativeTrading #MachineLearning #AI #DataScience

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