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This repo contains work related to a semester project undertaken at ETHZ in the Spring term of 2020 as part of the course Machine Learning in Finance. It considers a Neural Network approach to the portfolio optimisation problem with multiple assets.

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danielmenno/deep-portfolio-optimisation

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We consider a machine learning approach to the problem of portfolio optimisation, where the allocation map is learned at every timestep by a set of neural networks. The implementation relies on tensorflow keras in Tensorflow version 2.x. It is not guaranteed to run without issues in Tensorflow 1.

Notebooks

The main code can be found in portfolio_main.ipynb. For the Vasicek model implementation please use portfolio_vasicek.ipynb.

Project Proposal (Deep Portfolio Optimisation)

Link to project proposal: https://www.overleaf.com/9854394956sdddmrxbbgpg

Machine Learning in Finance

Please find related work by Prof. Josef Teichmann at https://people.math.ethz.ch/~jteichma/index.php?content=teach_mlf2019

Acknowledgements

All work is a result of a group effort of Nicholas Delmotte, Steven Battilana and myself, Daniel Montagna. Special thanks to Wahid Khosrawi for the supervision and insights.

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This repo contains work related to a semester project undertaken at ETHZ in the Spring term of 2020 as part of the course Machine Learning in Finance. It considers a Neural Network approach to the portfolio optimisation problem with multiple assets.

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