activity
20182020
most citedGenerative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination

25 citations · 25 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG2020

QuantNet: Transferring Learning Across Systematic Trading Strategies

Adriano Koshiyama, Sebastian Flennerhag, Stefano B. Blumberg +2

Systematic financial trading strategies account for over 80% of trade volume in equities and a large chunk of the foreign exchange market. In spite of the availability of data from…

q-fin.PM2019

Optimal Dynamic Strategies on Gaussian Returns

Nick Firoozye, Adriano Koshiyama

Dynamic trading strategies, in the spirit of trend-following or mean-reversion, represent an only partly understood but lucrative and pervasive area of modern finance. Assuming Gau…

q-fin.PM2019

Avoiding Backtesting Overfitting by Covariance-Penalties: an empirical investigation of the ordinary and total least squares cases

Adriano Koshiyama, Nick Firoozye

Systematic trading strategies are rule-based procedures which choose portfolios and allocate assets. In order to attain certain desired return profiles, quantitative strategists mu…

cs.LG201925 cited

Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination

Adriano Koshiyama, Nick Firoozye, Philip Treleaven

Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environm…

q-fin.PM2018

A Machine Learning-based Recommendation System for Swaptions Strategies

Adriano Soares Koshiyama, Nick Firoozye, Philip Treleaven

Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily basis. Up to now, not a single solution is available to aid in their…