Use cases of ML for trading


Use cases of ML for trading defi, dao and financial engineering

ML extracts signals from a wide range of market, fundamental, and alternative data, and can be applied at all steps of the algorithmic trading-strategy process.


Key applications include:

– Data mining to identify patterns, extract features and generate insights

– Supervised learning to generate risk factors or alphas and create trade ideas

– Aggregation of individual signals into a strategy

– Allocation of assets according to risk profiles learned by an algorithm

– The testing and evaluation of strategies, including through the use of synthetic data

– The interactive, automated refinement of a strategy using reinforcement learning


ML driven funds attract $1 trillion

Morgan Stanley estimated in 2017 that algorithmic strategies have grown at 15% per year over the past six years and control about $1.5 trillion between hedge funds, mutual funds, and smart beta ETFs. Other reports suggest the quantitative hedge fund industry was about to exceed $1 trillion AUM, nearly doubling its size since 2010 amid outflows from traditional hedge funds. In contrast, total hedge fund industry capital hit $3.21 trillion according to the latest global Hedge Fund Research report.

Global Algorithmic Trading Market to Surpass US$ 21,685.53 Million by 2026
The stockmarket is now run by computers, algorithms and passive managers, Economist, Oct 5, 20