An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy

Algorithm 2

Backtesting algorithm of daily trading strategy in R language.

Input: TS #TS is trading signals of a stock.

Output: AR, PR, RR, F1, AUC, WR, ARR, ASR, MDD

(1) N=length of Stock Code List #424 SPICS, and 185 CSICS.

(2) =Benchmark Index [“Closing Price”] # B is the closing price of benchmark index.

(3) WR=NULL; ARR=NULL; ASR=NULL; MDD=NULL

(4) for (i in 1: N)

(5) Stock Data=Stock Code List[i]

(6) =Stock_Data [“Closing Price”]

(7) = Stock_Data [“Label”]

(8) =(-)/ # BDRR is the daily return rate of benchmark index.

(9) = (-)/ #DRR is daily return rate. That is daily return rate of BAH strategy.

(10) =lag ()DR #TDRR is the daily return through trading.

(11) Table=Confusion_Matrix(TS, Label)

(12) AR[i]=sum(adj(Table))/sum(Table)

(13) PR[i]=Table/sum(Table)

(14) RR[i]=Table/sum(Table)

(15) F1=2PR[i]RR[i]/(PR[i]+RR[i])

(16) Pred=prediction (TS, Label)

(17) AUC[i]=performance (Pred, measure=“auc”)@y.values

(18) WR[i]=sum (TDRR>0)/sum(TDRR≠0)

(19) ARR[i]=Return.annualized (TDRR)# TDRR, BDRR, or DRR can be used.

(20) ASR[i]=SharpeRatio.annualized (TDRR)# TDRR, BDRR, or DRR can be used.

(21) MDD[i]=maxDrawDown (TDRR)# TDRR, BDRR, or DRR can be used.

(22) AR=c (AR, AR[i])

(23) PR=c (PR, PR[i])

(24) RR=c (RR, RR[i])

(25) F1=c (F1, F1[i])

(26) AUC=c (AUC, AUC[i])

(27) WR=c (WR, WR[i])

(28) ARR=c (ARR, ARR[i])

(29) ASR=c (ASR, ASR[i])

(30) MDD=c (MDD, MDD[i])

(31)

(32) Performance=cbind (AR, PR, RR, F1, AUC, WR, ARR, ASR, MDD)

(33) return (Performance)