A potential issue with automated trading systems, that are based on Machine Learning (ML) and/or Artificial Intelligence (AI), is the difficulty of assessing the risk of a trade. An ML/AI algorithm may analyze thousands of parameters in order to come up with a trading decision and applying standard risk management practices might interfere with the algorithms by overriding the algorithm's decision.
What are some basic methodologies for applying risk management to ML/AI-based automated trading systems without hampering the decision of the underlying algorithm(s)?
Update:
An example system would be: Genetic Programming algorithm that produces trading agents. The most profitable agent in the population is used to produce a short/long signal (usually without a confidence interval).
Answer
The risks involved in trading is everywhere and always a multifaceted thing: it includes the volatility of the selected asset, the leverage and concentration of the porfolio, whether there is a stop loss, a hedge, etc. Also, risk management is frequently not tied to the "alpha model" directly (e.g. VaR, shortfall, and scenario testing).
For instance, one well known way of sizing a position is the Kelly formula:
$f^{*} = \frac{bp - q}{b}$
This makes no assumptions about the directional model that is used to enter the position. You can infer the values (e.g. probability of winning) from a historical simulation, regardless of whether the model is black-box, grey-box, or white-box.
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