6 month quantitative research training.
Implement state of the art deep learning and reinforcement learning algorithms.
Interview with leading technology and financial firms.
Publish literature on strategies developed.
Network with 40 previous researchers.
Work under the supervision quantitative team.
Collaborate with peers on findings.
High energy work environment at Cambridge Innovation Center.
Gain the experience necessary to succeed in high tech, quantitative research, or data science positions. Implement real world machine learning problems, publish findings, land a position with top firms.
Fellows come from top universities and often complete non-computer science PhD. They pursue technology, data and quantitative research roles.
Learn how to solve problems in quantitative finance
Needs based scholarships available
Research state of the art learning algorithms
Develop skills sought by top tech and quant firms
Program support till you receive a full time job offer
Network with previous Algo Depth quants
Research in policy gradient, actor critic, and Q learning algorithms. Use cases include order execution, alpha generation, and asset allocation.
Primary focus on convolutional and recurrent neural networks used in alpha generation and feature selection.
Better estimate future prices by decomposing trends. Wavelet and Fischer Transform, among others, used to predict equity markets.