We put “only need” in quotes because of course if the number of parameters is large, it can take very long to try out different sets of parameters to find the optimal. With this method, we “only need” to try different sets of hypothetical parameters to see which gives the best Sharpe ratio and adopt that set as the optimal. Once trained, this neural network can then predict the future 1-month Sharpe ratio based on any hypothetical set of trading parameters and the current market features.
#QUANT TRADING PLUS#
In this case you would feed historical training samples to the neural network that include the trading parameters, the market features, plus the resulting forward 1-month Sharpe ratio of the trading strategy as “labels” (i.e. For example, maximizing the future 1-month Sharpe ratio of a trading strategy is a typical outcome. The output of this neural network would be the outcome you want to optimize. To help our clients efficiently run their models, Predictnow.ai provides hundreds of such market features. For example, to represent a “market regime”, we may include market volatility, behaviors of different market sectors, macroeconomic conditions, and many other input features. The inputs to this neural network will not only include the parameters that we originally set out to optimize, but also the vast set of features that measure the external conditions. (Recall that a neural network is able to approximate almost any function, but you can use many other machine learning algorithms instead of neural networks for this task). What better method than machine learning to solve this problem!īy using machine learning, we can approximate this objective function using a neural network, by training its many nodes using historical data.
Such objective functions mean that traditional optimization methods do not usually give the optimal results under a particular set of external conditions.Furthermore, even if you specify that exact set of conditions, the outcome is not deterministic. In the case of a manufacturing process, the optimal conveyor belt rate may depend on dozens of sensor readings. In the case of a trading strategy, the optimal stop loss may depend on the market regime, which may not be clearly defined. What concerns us at PredictNow.ai, is when the objective function is not only nonlinear, but also depends on external time varying and stochastic conditions. There are already standard methods to handle these difficulties. For example, if the number of parameters is large, or if the objective function that relates the parameters to the outcome is nonlinear, or if there are numerous constraints on the parameters. Of course, the numerical optimization procedure can become quite complicated based on a number of different factors. Or running the conveyor belt at 1m per minute led to the lowest defect rate in a manufacturing process. For example, setting a stop loss of 1% gave the best Sharpe ratio for a trading strategy backtested over the last 10 years. Traditionally, optimizing the parameters of any business process (such as a trading strategy) is a matter of finding out what parameters give an optimal outcome over past data. Let’s recap what Conditional Parameter Optimization is. We call this Conditional Portfolio Optimization (which fortuitously shares the same acronym). It appeared to work well on optimizing the operating parameters of trading strategies, but increasingly, we found that its greatest power lies in its potential to optimize portfolio allocations. Previously on this blog, we wrote about a machine-learning-based parameter optimization technique we invented, called Conditional Parameter Optimization (CPO). By Ernest Chan, Ph.D., Haoyu Fan, Ph.D., Sudarshan Sawal, and Quentin Viville, Ph.D.
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