Conditional Parameter Optimization : Adapting a Strategy to Different Market Regimes

Conditional Parameter Optimization uses machine learning to place orders optimally based on changing market conditions (regimes) in any market. Traders in these markets typically already possess a basic trading strategy that decides the timing, pricing, type, and/or size of such orders. This trading strategy will usually have a small number of adjustable trading parameters. Conventionally, they are often optimized based on a fixed historical data set (“train set”), or periodically re-optimized using an expanding or rolling train set (“Walk Forward Optimization”.) All these conventional optimization procedures can be called unconditional parameter optimization, as the trading parameters do not intelligently respond to rapidly changing market conditions. To address this adaptability problem, we apply a supervised machine learning algorithm to learn from a large feature set that captures various aspects of the prevailing market conditions, together with specific values of the trading parameters, to predict the outcome of the trading strategy. Once such a machine-learning model is trained to predict the outcome, we can apply it to live trading by feeding in the features that represent the latest market conditions as well as various combinations of the trading parameters. We report on the successes of one such experiment