“Financial markets are complex systems like no other. Extracting signal from financial data requires specialized tools that are distinct from those used in general machine learning. The MlFinLab package compiles important algorithms that every quant should know and use.”

“Those who doubt open-source libraries just need to look at the impact of Pandas, Scikit-learn, and the like. MIFinLab is doing to financial machine learning what Tensorflow and PyTorch are doing to deep learning.”

“For many decades, finance has relied on overly simplistic statistical techniques to identify patterns in data. Machine learning promises to change that by allowing researchers to use modern nonlinear and highly dimensional techniques. Yet, applying those machine learning algorithms to model financial problems is easier said than done: finance is not a plug-and-play subject as it relates to machine learning.

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Correlation networks have been studied extensively in academia for the past 20 years; Yet, they are not part of the standard quant toolbox.

Moreover, even among researchers in this field, there is no strong consensus on which method is state of the art. I think this can be partly explained because there is no widely available open-source code (leading to re-implementation biases, and unfair comparisons), and also a lack of publicly available common datasets and benchmarks (due to data licensing and confidentiality reasons).

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