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