Evolution of high-frequency systematic trading: a performance-driven gradient boosting model

Abstract

This paper proposes a performance-driven gradient boosting model (pdGBM) which predicts shorthorizon price movements by combining nonlinear response functions of selected predictors. This model performs gradient descent in a constrained functional space by directly minimizing loss functions customized with different trading performance measurements. To demonstrate its practical applications, a simple trading system was designed with trading signals constructed from pdGBM predictions and fixed holding period in each trade. We tested this trading system on the highfrequency data of SPDR S&P 500 index ETF (SPY). In the out-of-sample period, it generated an average of 0.045% return per trade and an annualized Sharpe ratio close to 20 after transaction costs. Various empirical results also showed the model robustness to different parameters. These superior performances confirm the predictability of short-horizon price movements in the US equity market. We also compared the performance of this trading system with similar trading systems based on other predictive models like the gradient boosting model with L2 loss function and the penalized linear model. Results showed that pdGBM substantially outperformed all other models by higher returns in each month of the testing period. Additionally, pdGBM has many advantages including its capability of automatic predictor selection and nonlinear pattern recognition, as well as its simply structured and interpretable output function.

Model Confidence Bounds for Variable Selection

“…Thus, we may consider a richer structure of MCB with multiple LBMs or even multiple UBMs. In addition, MCB can be extended to other classes models, such as, GLM and time series models (Meier et al, 2008;Zhou et al, 2015). We leave these topics for future research.…”

Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume

“…We have used the GBM implementation from Scikit-learn library (Pedregosa et al 2011) for all our experiments. Furthermore, note that different variants of tree boosting have been empirically proven to be state-of-the-art methods in predictive tasks across different machine learning challenges (Bentéjac et al 2020;Chen and Guestrin 2016;Lu and Mazumder 2020;Taieb and Hyndman 2014;Gulin et al 2011) and more recently in finance (Zhou et al 2015;Sun et al 2018). Note, that although by default GBM does not provide confidence intervals, we are not claiming that is not possible to construct confidence intervals for GBM.…”

“…GBM is the gradient boosting machine (Friedman 2001). It has been empirically proven to be highly effective in predictive tasks across different machine learning challenges (Gulin et al 2011;Taieb and Hyndman 2014) and more recently in finance (Zhou et al 2015;Sun et al 2018). The feature vector fed into GBM is also the concatenation of features from order book and transaction data of two markets.…”

Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets

“…See Appendix for more details. Furthermore, note that different variants of tree boosting have been empirically proven to be state-of-the-art methods in predictive tasks across different machine learning challenges [26,27] and more recently in finance [28,29].…”

“…m h(x t ; a m )) (28). However, for practical purposes first we make the initial guess F 0 (x) = arg min c T t=1 Ψ (y t , c) and then parameters are jointly fit in a forward incremental way m = 1, ..., M :(β m , a m ) = arg min β,a T t=1 Ψ (y t , F m−1 (x t ) + βh(x t ; a))(29)andF m (x t ) = F m−1 (x t ) + β m h(x t ; a m ).…”