The Team
The Program
Alumni
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6 month quantitative research training.
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Implement state of the art deep learning and reinforcement learning algorithms.
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Interview with leading technology and financial firms.
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Publish literature on strategies developed.
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Network with 40 previous researchers.
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Work under the supervision quantitative team.
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Collaborate with peers on findings.
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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.
Benefits:
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
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Program support till you receive a full time job offer
Network with previous Algo Depth quants
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Reinforcement Learning
Research in policy gradient, actor critic, and Q learning algorithms. Use cases include order execution, alpha generation, and asset allocation.
Deep Learning
Primary focus on convolutional and recurrent neural networks used in alpha generation and feature selection.
Signal Processing
Better estimate future prices by decomposing trends. Wavelet and Fischer Transform, among others, used to predict equity markets.
ALGO DEPTH RESEARCHERS GO ON TO WORK AT TOP FIRMS
QUANTITATIVE RESEARCH
INVESTMENT BANKING
TECHNOLOGY
Xiao Shi
Princeton PhD, Computational Chemistry
Quantitative Researcher - Barclays
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Isaac Chen
Brown PhD, Computational Physics
Machine Learning Engineer Intern - Twitter
Zixuan Zhang
Cornell Masters, Chemical Engineering
Software Developer - Uber
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