Systematic Equity Alphas Library
Find out our latest insights in systematic equity alphas

Technical Architectures
Insights to quantitative methods applied to investment strategies, focused on machine learning and advanced computing.

Hyper-parameter Tuning
We have a set of hyper-parameters and we aim to find the right combination of their values which can help us to find either the minimum (eg. loss) or the maximum (eg. accuracy) of a function.
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16-Dec-2019
16-Dec-2019

WHITE PAPER
Extreme Learning Machine
Algo Depth focuses much of its attention on signal processing, machine learning, and deep learning. In this extreme learning machine strategy, we apply a neural network to predict upper and lower bounds of future stock movements for the largest 25 companies traded on the Nasdaq exchange.
2017
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WHITE PAPER
Application of Reinforcement Learning For Order Execution
We present a large scale application of reinforcement learning to optimize trading execution. The experiment is based on Apple Inc. The results generate a 1.2% annual cost savings, and show the promise of applying reinforcement learning methods to solve market microstructure problems. Our algorithm can further improve by including tick data, and market order books. It can be tested on any stock.