Order-Imbalance-Trading-Simulation-R-Code/BuildLinearModel.R at master · jfenner/Order-Imbalance-Trading-Simulation-R-Code · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
source (file ='BuildLinearData.R')
BuildLinearModel <-
  function (key,
            data,
            full.day = T,
            morning = T,
            open.int = F,
            delay = 20,
            lags = 5,
            strategy ='',
            functions = NULL) {
    # check if we need a full - day linear model or for a single trading session
    if (full.day == T) {
      morning.data <-
        BuildLinearData (
          data ,
          morning = T,
          open.int = open.int ,
          delay = delay ,
          lags = lags ,
          functions = functions
        )
      evening.data <-
        BuildLinearData (
          data ,
          morning = F,
          open.int = open.int ,
          delay = delay ,
          lags = lags ,
          functions = functions
        )
      dMid.Response <-
        c(morning.data$dMid.Response , evening.data$dMid.Response)
      VOI <- rbind (morning.data$VOI , evening.data$VOI)
      OIR <- rbind (morning.data$OIR , evening.data$OIR)
      time.secs <-
        c(morning.data$time.secs , evening.data$time.secs)
      mid.price <-
        c(morning.data$mid.price , evening.data$mid.price)
      spread <- c(morning.data$spread , evening.data$spread)
      AvgTrade.price <-
        c(morning.data$AvgTrade.price ,
          evening.data$AvgTrade.price)
      MPB <- rbind (morning.data$MPB , evening.data$MPB)
      trading.data <-
        rbind (morning.data$data , evening.data$data)
    } else {
      trading.data <-
        BuildLinearData (
          data ,
          morning = morning ,
          open.int = open.int ,
          delay = delay ,
          lags = lags ,
          functions = functions
        )
      dMid.Response <- trading.data$dMid.Response
      VOI <- trading.data$VOI
      OIR <- trading.data$OIR
      time.secs <- trading.data$time.secs
      mid.price <- trading.data$mid.price

      spread <- trading.data$spread
      AvgTrade.price <- trading.data$AvgTrade.price
      MPB <- trading.data$MPB
      trading.data <- trading.data$data
    }


    ## build the features matrix (x- variable ) based on strategy
    ## transform the variables if necessary
    identity <- function (x)
      x
    inverse <- function (x)
      1 / x
    f.VOI <-
      if (is.null (functions [['VOI']]))
        identity
    else
      functions [['VOI']]
    f.OIR <-
      if (is.null (functions [['OIR']]))
        identity
    else
      functions [['OIR']]

    ## build the explanatory variables
    x <- list ()
    x[['A']] <- data.frame (y = dMid.Response , VOI = f.VOI(VOI))
    x[['B']] <-
      data.frame (
        y = dMid.Response,
        VOI = f.VOI(VOI) / spread,
        OIR = f.OIR(OIR) / spread,
        MPB = MPB[,1] / spread
      )

    value <- {}
    # build the linear model using OLS
    if (strategy != '') {
      s <- strategy
      value$model <- lm(y ~ ., data = x[[s]])
    }

    ## return values
    value$dMid.Response <- dMid.Response ## y- value
    value$VOI <- VOI
    value$OIR <- OIR
    value$spread <- spread
    value$y <- dMid.Response
    value$x <- x
    value$data <- trading.data
    value$AvgTrade.price <- AvgTrade.price
    value$mid.price <- mid.price
    value$MPB <- MPB
    value$time.secs <- time.secs

    return (value)
  }