Macro trading strategies11/3/2023 There are just too many of these variables, and you don’t know how to incorporate them to improve your trading strategy…But that’s not a problem for machine learning…The machine learning algorithm will get rid of the useless features via…feature selection.” “ The way to compute this conditional probability is machine learning…Intuition tells you that there are some variables that you didn’t take into account in your original, simple, trading strategy. The conditions that can determine the probability may even be quantifiable.” Approaches to classifying market regimes Random forest If you are trading during a financial crisis, it could be very low. “ you are trading a short volatility strategy…during a very calm market, it is likely that your conditional probability of profit would be quite high. Such periods are often referred to as market regimes… Within the arena of…deep learning-based methods…detection of significant shifts in market behaviour is a central tool for their model governance since it serves as an indicator for the need to retrain the machine learning model”. “It is well understood that return series are non-stationary in the strong sense, and exhibit volatility clustering…An observed sequence of asset returns exhibits periods of similar behaviour, followed by potentially distinct periods that indicate a significantly different underlying distribution. “Financial markets have the tendency to change their behaviour over time, which can create regimes or periods of fairly persistent market conditions…Modelling various market regimes…can enable macroeconomically aware investment decision-making and better management of tail risks.” This post ties in with this site’s summary on quantitative methods for macro information efficiency, particularly the section on unsupervised learning. Headings, cursive text, and text in brackets has been added. Sources are linked next to the below quotes. Recent proposals include supervised ensemble learning with random forests, which relate the market state to values of regime-relevant time series, unsupervised learning with Gaussian mixture models, which fit various distinct Gaussian distributions to capture states of the data, unsupervised learning with hidden Markov models, which relate observable market data, such as volatility, to latent state vectors, and unsupervised learning with Wasserstein k-means clustering, which classifies market regimes based on the distance of observed points in a metric space. Machine learning offers a range of approaches to that end. The practical challenge is to detect market regime changes quickly and to backtest methods that may do the job. They affect the relevance of investment factors and the success of trading strategies. Market regimes are clusters of persistent market conditions.
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