更新：应用策略时出现 quantstrat 错误：chart_Series 中的错误
UPDATED: quantstrat error when applying a strategy: Error in chart_Series
编辑：这个问题的第一部分似乎已经解决了，但是我还有另一个问题。
加载数据后，我试图绘制 chart.Posn 但现在 运行 遇到此错误：
> chart.Posn(portfolio.st, 'GOOG')
Error in chart_Series(Prices, name = Symbol, TA = TA, ...) :
'x' must be a time-series object
我不知道这对我最初使用 tidyquant 而不是从 quantmod
获取符号是否有副作用
代码（我在下面提供了新数据）
GOOG <- xts(GOOG, order.by = GOOG$date)
GOOG$date <- NULL
GOOG$predictions <- as.numeric(as.character(GOOG$predictions))
currency('USD')
Sys.setenv(TZ="UTC")
stock("GOOG", currency="USD", multiplier=1)
symbols <- c("GOOG")
initDate="2007-01-01"
strategy.st <- portfolio.st <- account.st <- "SVMstrat"
rm.strat(strategy.st)
initPortf(portfolio.st, symbols=symbols, initDate=initDate, currency='USD')
initAcct(account.st, portfolios=portfolio.st, initDate=initDate, currency='USD')
initOrders(portfolio.st, initDate=initDate)
strategy(strategy.st, store=TRUE)
GOOG$predictions <- as.numeric(as.character(GOOG$predictions))
mean(GOOG$direction == GOOG$predictions)
add.signal(strategy.st,
name = sigThreshold,
arguments = list(column = "predictions",
threshold = 0.5,
relationship = "gte",
cross = TRUE),
label = "longEntry")
add.signal(strategy.st,
name = sigThreshold,
arguments = list(column = "predictions",
threshold = 0.5,
relationship = "lte",
cross = TRUE),
label = "longExit")
applySignals(strategy = strategy.st, mktdata=GOOG)
add.rule(strategy.st,
name="ruleSignal",
arguments=list(sigcol="longEntry",
sigval=TRUE,
ordertype="market",
orderside="long",
replace=FALSE,
prefer="Open",
orderqty = 1),
type="enter", path.dep=TRUE)
add.rule(strategy.st,
name="ruleSignal",
arguments=list(sigcol="longExit",
sigval=TRUE,
orderqty="all",
ordertype="market", orderside="long",
replace=FALSE, prefer="Open"),
type="exit", path.dep=TRUE)
applyStrategy(strategy=strategy.st,portfolios=portfolio.st)
updatePortf(portfolio.st)
dateRange <- time(getPortfolio(portfolio.st)$summary)[-1]
updateAcct(portfolio.st,dateRange)
updateEndEq(account.st)
chart.Posn(portfolio.st, 'GOOG')
数据：
GOOG <- structure(list(date = structure(c(17375, 17378, 17379, 17380,
17381, 17382, 17385, 17386, 17387, 17388, 17389, 17392, 17393,
17394, 17395, 17396, 17399, 17400, 17401, 17402, 17403, 17406,
17407, 17408, 17409, 17410, 17414, 17415, 17416, 17417, 17420,
17421, 17422, 17423, 17424, 17427, 17428, 17429, 17430, 17431,
17434, 17435, 17436, 17437, 17438, 17441, 17442, 17443, 17444,
17445, 17448, 17449, 17450, 17451, 17452, 17455, 17456, 17457,
17458, 17459, 17462, 17463, 17464, 17465, 17466, 17469, 17470,
17471, 17472, 17473, 17476, 17477, 17478, 17479, 17480, 17483,
17484, 17485, 17486, 17487, 17490, 17491, 17492, 17494, 17497,
17498, 17499, 17500, 17501, 17504, 17505, 17506, 17507, 17508,
17511, 17512, 17513, 17514, 17515, 17518), class = "Date"), open = c(929.400024,
941.890015, 932.380005, 928.609985, 930.340027, 926.75, 929.059998,
927.090027, 920.609985, 917.549988, 907.969971, 922.530029, 924.22998,
925.289978, 925.780029, 910.309998, 910, 912.719971, 921.929993,
928.659973, 923.48999, 916, 905.099976, 920.049988, 931.76001,
941.130005, 933.080017, 930.150024, 931.72998, 936.48999, 934.25,
932.590027, 930.659973, 931.25, 924.659973, 920.01001, 917.419983,
922.97998, 933, 927.75, 925.450012, 923.719971, 927.73999, 941.359985,
952, 959.97998, 954, 957, 955.48999, 966.700012, 980, 980, 973.719971,
987.450012, 992, 992.099976, 990.289978, 991.77002, 986, 989.440002,
989.52002, 970, 968.369995, 980, 1009.190002, 1014, 1015.219971,
1017.210022, 1021.76001, 1022.109985, 1028.98999, 1027.27002,
1030.52002, 1033.98999, 1026.459961, 1023.419983, 1022.590027,
1019.210022, 1022.52002, 1034.01001, 1020.26001, 1023.309998,
1035, 1035.869995, 1040, 1055.089966, 1042.680054, 1022.369995,
1015.799988, 1012.659973, 995.940002, 1001.5, 1020.429993, 1037.48999,
1035.5, 1039.630005, 1046.119995, 1045, 1054.609985, 1066.079956
), high = c(943.830017, 943.590027, 937.447021, 932.599976, 932.23999,
930.307007, 931.700012, 935.814026, 925.97998, 919.26001, 917.780029,
924.66803, 926.549988, 932.700012, 926.859985, 915.275024, 913,
925.859985, 929.929993, 930.840027, 925.554993, 919.244995, 923.330017,
930.81897, 941.97998, 942.47998, 937, 930.914978, 936.409973,
936.98999, 938.380005, 933.47998, 937.25, 932.77002, 926.48999,
922.080017, 922.419983, 933.880005, 936.530029, 934.72998, 926.400024,
930.820007, 949.900024, 950.690002, 959.786011, 962.539978, 958,
960.390015, 970.909973, 979.460022, 985.424988, 981.570007, 990.710022,
994.119995, 997.210022, 993.906982, 996.440002, 996.719971, 988.880005,
991, 989.52002, 972.22998, 976.090027, 987.599976, 1048.390015,
1024.969971, 1024, 1029.670044, 1028.089966, 1032.650024, 1034.869995,
1033.969971, 1043.521973, 1033.98999, 1030.76001, 1031.579956,
1026.810059, 1024.089966, 1035.920044, 1034.420044, 1022.609985,
1035.109985, 1039.706055, 1043.177979, 1055.459961, 1062.375,
1044.079956, 1028.48999, 1022.48999, 1016.099976, 1020.609985,
1024.969971, 1034.23999, 1042.050049, 1043.800049, 1050.310059,
1046.665039, 1058.5, 1067.619995, 1078.48999), low = c(927.5,
926.039978, 929.26001, 916.679993, 922.23999, 923.030029, 926.5,
925.609985, 917.25, 906.130005, 905.580017, 918.190002, 919.820007,
923.445007, 910.97998, 907.153992, 903.400024, 911.474976, 919.359985,
915.5, 915.5, 911.869995, 905, 919.650024, 931.76001, 935.150024,
921.960022, 919.27002, 923.619995, 924.880005, 926.919983, 923.861023,
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947.840027, 949.140015, 950.690002, 955.179993, 963.359985, 976.109985,
966.080017, 972.25, 985, 989, 984, 988.590027, 986.974976, 978.390015,
984.580017, 966.119995, 961, 960.52002, 972.200012, 1008.200012,
1007.5, 1010.419983, 1016.950012, 1013.01001, 1020.309998, 1025,
1025.130005, 1028.449951, 1019.666016, 1025.280029, 1022.570007,
1014.150024, 1015.419983, 1022.52002, 1017.75, 1017.5, 1022.655029,
1031.430054, 1035, 1038.439941, 1040, 1015.650024, 1015, 1002.02002,
995.570007, 988.280029, 1001.140015, 1018.070984, 1032.521973,
1032.050049, 1033.689941, 1038.380005, 1043.109985, 1049.5, 1062
), close = c(941.530029, 930.5, 930.830017, 930.390015, 923.650024,
927.960022, 929.359985, 926.789978, 922.900024, 907.23999, 914.390015,
922.669983, 922.219971, 926.960022, 910.97998, 910.669983, 906.659973,
924.690002, 927, 921.280029, 915.890015, 913.809998, 921.289978,
929.570007, 939.330017, 937.340027, 928.450012, 927.809998, 935.950012,
926.5, 929.080017, 932.070007, 935.090027, 925.109985, 920.289978,
915, 921.809998, 931.580017, 932.450012, 928.530029, 920.969971,
924.859985, 944.48999, 949.5, 959.109985, 953.27002, 957.789978,
951.679993, 969.960022, 978.890015, 977, 972.599976, 989.25,
987.830017, 989.679993, 992, 992.179993, 992.809998, 984.450012,
988.200012, 968.450012, 970.539978, 973.330017, 972.559998, 1019.27002,
1017.109985, 1016.640015, 1025.5, 1025.579956, 1032.47998, 1025.900024,
1033.329956, 1039.849976, 1031.26001, 1028.069946, 1025.75, 1026,
1020.909973, 1032.5, 1019.090027, 1018.380005, 1034.48999, 1035.959961,
1040.609985, 1054.209961, 1047.410034, 1021.659973, 1021.409973,
1010.169983, 998.679993, 1005.150024, 1018.380005, 1030.930054,
1037.050049, 1041.099976, 1040.47998, 1040.609985, 1049.150024,
1064.189941, 1077.140015), volume = c(1846400, 1970100, 1277700,
1824400, 1202500, 1082300, 1032200, 1061600, 1192100, 1824000,
1206800, 1064500, 883400, 1006700, 1277200, 1342700, 943400,
1166700, 1090200, 1270300, 1053400, 1086500, 1185600, 1301200,
1582600, 947400, 1326400, 1527700, 1212700, 1011500, 1267000,
1134400, 1102600, 1397600, 2505400, 1306900, 936700, 1669800,
1290600, 1052700, 1856800, 1666900, 2239400, 1020300, 1581000,
1283400, 888300, 952400, 1213800, 1173900, 891400, 968400, 1693300,
1262400, 1169800, 910500, 1290200, 1057600, 1313600, 1183200,
1478400, 1212200, 1211300, 2042100, 5167700, 2085100, 1330700,
1373400, 1049000, 1076400, 1125200, 1112300, 1088700, 1245200,
720000, 885800, 959200, 854000, 1129700, 1397100, 953500, 1097000,
746300, 537000, 1307900, 1424400, 2459400, 1724000, 1909600,
1906400, 2067300, 1272000, 1458200, 1290800, 1192800, 1279500,
1282700, 1558700, 3275900, 1554600), adjusted = c(941.530029,
930.5, 930.830017, 930.390015, 923.650024, 927.960022, 929.359985,
926.789978, 922.900024, 907.23999, 914.390015, 922.669983, 922.219971,
926.960022, 910.97998, 910.669983, 906.659973, 924.690002, 927,
921.280029, 915.890015, 913.809998, 921.289978, 929.570007, 939.330017,
937.340027, 928.450012, 927.809998, 935.950012, 926.5, 929.080017,
932.070007, 935.090027, 925.109985, 920.289978, 915, 921.809998,
931.580017, 932.450012, 928.530029, 920.969971, 924.859985, 944.48999,
949.5, 959.109985, 953.27002, 957.789978, 951.679993, 969.960022,
978.890015, 977, 972.599976, 989.25, 987.830017, 989.679993,
992, 992.179993, 992.809998, 984.450012, 988.200012, 968.450012,
970.539978, 973.330017, 972.559998, 1019.27002, 1017.109985,
1016.640015, 1025.5, 1025.579956, 1032.47998, 1025.900024, 1033.329956,
1039.849976, 1031.26001, 1028.069946, 1025.75, 1026, 1020.909973,
1032.5, 1019.090027, 1018.380005, 1034.48999, 1035.959961, 1040.609985,
1054.209961, 1047.410034, 1021.659973, 1021.409973, 1010.169983,
998.679993, 1005.150024, 1018.380005, 1030.930054, 1037.050049,
1041.099976, 1040.47998, 1040.609985, 1049.150024, 1064.189941,
1077.140015), direction = c(1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1,
1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0,
1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1,
1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1,
1, 0, 1, 1, 1), daily.returns = c(0.00796497316633915, -0.0117150050027772,
0.000354666308436391, -0.000472698550717299, -0.00724426411648449,
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42.3277780878597, 34.916071046742, 38.3146934968324, 31.6795222837921,
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0.0158938717770809, 0.00705213034020158, 0.00683994790619824,
-0.0137528275407712, -0.00885123791251541, 0.00389138470011119,
0.00781584192612872, -0.0116494359217736, -0.0067252318670965,
-0.0152708022295656, -0.0110688002733577, -0.00376072262811622,
0.00790291050001635, 0.00891390782234858, 0.00650336567082288,
0.00330323208950656, 0.0137630010983862, 0.0181199866055621,
0.0324030338008408, 0.0344707694066191, 0.0349861658038035, 0.00758374779102411,
0.02131932596276, 0.0204135365091132, 0.0245884516898194, 0.0153443490021248,
0.0387182437892122, 0.018255779160036, 0.0109623594909625, 0.0152364552420901,
0.019931662088692, 0.00359222408181115, -0.00342751359774773,
-0.00149653292664187, -0.0240262396056901, -0.0220519404222213,
-0.0198161049507126, -0.0121513535862787, 0.0309568648450718,
0.0490236690313068, 0.046405770875606, 0.0522123781158035, 0.0530817741025684,
0.0128769514166818, 0.00860504177727073, 0.0162834675770531,
0.0138961503712416, 0.00552310197477013, -0.00428045026022694,
-0.000146247165464075, -0.00711880768197481, -0.0183820889249988,
0.00120168056673897, -0.00877310685546551, -0.00721091801104201,
0.00824079523600485, 0.0146341354305113, 0.00782401940069555,
0.0338815355466462, 0.0281073484004102, -0.0124798129988672,
-0.0141444961739703, -0.0296884485614344, -0.0541125132421794,
-0.0411836747473417, -0.0032155946565986, 0.00927736062930951,
0.0262615746498476, 0.0415987024226974, 0.0345453179346213, 0.0215939309246291,
0.0175189755318357, 0.0258336996226562, 0.0340315709208765),
predictions = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L), .Label = c("0", "1"), class = "factor")), row.names = c("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"), class = "data.frame")
老问题（我离开这里是为了完整）
老问题（带数据）
我一直在使用 quantstrat 包，但我 运行 遇到了一个错误。
当我应用 applySignals(strategy = strategy.st, mktdata=GOOG) 行时出现错误。我收到此错误：
Error in `colnames<-`(`*tmp*`, value = label) :
attempt to set 'colnames' on an object with less than two dimensions
我使用 traceback() 并获得此报告：
5: stop("attempt to set 'colnames' on an object with less than two dimensions")
4: `colnames<-`(`*tmp*`, value = label)
3: (function (label, data = mktdata, column, threshold = 0, relationship = c("gt",
"lt", "eq", "gte", "lte"), cross = FALSE)
{
relationship = relationship[1]
ret_sig = NULL
colNum <- match.names(column, colnames(data))
switch(relationship, `>` = , gt = {
ret_sig = data[, colNum] > threshold
}, `<` = , lt = {
ret_sig = data[, colNum] < threshold
}, eq = {
ret_sig = data[, colNum] == threshold
}, gte = , gteq = , ge = {
ret_sig = data[, colNum] >= threshold
}, lte = , lteq = , le = {
ret_sig = data[, colNum] <= threshold
})
if (isTRUE(cross))
ret_sig <- diff(ret_sig) == 1
if (!missing(label))
colnames(ret_sig) <- label
return(ret_sig)
})(label = "longEntry", data = mktdata, column = "predictions",
threshold = 0.5, relationship = "gte", cross = TRUE)
2: do.call(sigFun, .formals)
1: applySignals(strategy = strategy.st, mktdata = GOOG)
根据我的研究和寻找解决方案 here，这是我的信号问题。
解决方案的 link 建议 head(mktdata) - 这是在 运行 applySignals() 行之后创建的。一种不同的策略（如果需要我可以添加代码）创建 mktdata 列名如下：
> colnames(mktdata)
[1] "SPY.Open" "SPY.High" "SPY.Low"
[4] "SPY.Close" "SPY.Volume" "SPY.Adjusted"
[7] "nonDerivedIndicator" "longEntry" "longExit"
但是我使用 tidyquant 导入了数据，列名是；
> colnames(GOOG)
[1] "open" "high" "low" "close"
[5] "volume" "adjusted" "daily.returns" "predictions"
这是我面临的问题吗？只是一个列名问题？也就是说，quantmod 将数据导入为 SPY.Open 并且 quantstrat 是在考虑到这一点的情况下构建的，并期望这种格式而不是整洁的格式？还是我遇到了不同的错误？
量化代码：
currency('USD')
Sys.setenv(TZ="UTC")
stock("GOOG", currency="USD", multiplier=1)
symbols <- c("GOOG")
strategy.st <- portfolio.st <- account.st <- "SVMstrat"
rm.strat(strategy.st)
initPortf(portfolio.st, symbols=symbols, initDate=initDate, currency='USD')
initAcct(account.st, portfolios=portfolio.st, initDate=initDate, currency='USD')
initOrders(portfolio.st, initDate=initDate)
strategy(strategy.st, store=TRUE)
GOOG$predictions <- as.numeric(as.character(GOOG$predictions))
mean(GOOG$direction == GOOG$predictions)
add.signal(strategy.st,
name = sigThreshold,
arguments = list(column = "predictions",
threshold = 0.5,
relationship = "gte",
cross = TRUE),
label = "longEntry")
add.signal(strategy.st,
name = sigThreshold,
arguments = list(column = "predictions",
threshold = 0.5,
relationship = "lte",
cross = TRUE),
label = "longExit")
applySignals(strategy = strategy.st, mktdata=GOOG)
数据：
(Had to remove old data for new question)
编辑：
我已经重新运行模型，但仍然运行进入同样的错误：
colnames(GOOG) <- c("GOOG.Open", "GOOG.High", "GOOG.Low",
"GOOG.Close", "GOOG.Volume", "GOOG.Adjusted",
"daily.returns", "predictions")
回答我自己的问题。
看起来 quantstrat 接受 xts 个对象而 quantmod 产生 tibbles。
我所要做的就是在 运行 实施策略之前将我的数据转换为 xts 对象。
GOOG <- xts(GOOG, order.by = GOOG$date)
GOOG$date <- NULL
编辑：
对于工作策略 (SPY)，我得到以下 xts 结构输出。
An ‘xts’ object on 2003-01-02/2012-12-28 containing:
Data: num [1:2516, 1:7] 72.9 74.6 74.8 76.2 75.6 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:7] "SPY.Open" "SPY.High" "SPY.Low" "SPY.Close" ...
Indexed by objects of class: [Date] TZ: UTC
xts Attributes:
List of 2
$ src : chr "yahoo"
$ updated: POSIXct[1:1], format: "2018-12-09 12:43:49"
对于我的代码 - 使用 tidyquant 导入，添加一些指标，运行 SVM 模型，然后将其转换回 xts 我有以下结构输出。
An ‘xts’ object on 2017-07-28/2017-12-18 containing:
Data: chr [1:100, 1:11] " 929.40" " 941.89" " 932.38" " 928.61" ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:11] "open" "high" "low" "close" ...
Indexed by objects of class: [Date] TZ: UTC
xts Attributes:
NULL
我错过了
List of 2
$ src : chr "yahoo"
$ updated: POSIXct[1:1], format: "2018-12-09 12:43:49"
部分。
编辑：这个问题的第一部分似乎已经解决了，但是我还有另一个问题。
加载数据后，我试图绘制 chart.Posn 但现在 运行 遇到此错误：
> chart.Posn(portfolio.st, 'GOOG')
Error in chart_Series(Prices, name = Symbol, TA = TA, ...) :
'x' must be a time-series object
我不知道这对我最初使用 tidyquant 而不是从 quantmod
代码（我在下面提供了新数据）
GOOG <- xts(GOOG, order.by = GOOG$date)
GOOG$date <- NULL
GOOG$predictions <- as.numeric(as.character(GOOG$predictions))
currency('USD')
Sys.setenv(TZ="UTC")
stock("GOOG", currency="USD", multiplier=1)
symbols <- c("GOOG")
initDate="2007-01-01"
strategy.st <- portfolio.st <- account.st <- "SVMstrat"
rm.strat(strategy.st)
initPortf(portfolio.st, symbols=symbols, initDate=initDate, currency='USD')
initAcct(account.st, portfolios=portfolio.st, initDate=initDate, currency='USD')
initOrders(portfolio.st, initDate=initDate)
strategy(strategy.st, store=TRUE)
GOOG$predictions <- as.numeric(as.character(GOOG$predictions))
mean(GOOG$direction == GOOG$predictions)
add.signal(strategy.st,
name = sigThreshold,
arguments = list(column = "predictions",
threshold = 0.5,
relationship = "gte",
cross = TRUE),
label = "longEntry")
add.signal(strategy.st,
name = sigThreshold,
arguments = list(column = "predictions",
threshold = 0.5,
relationship = "lte",
cross = TRUE),
label = "longExit")
applySignals(strategy = strategy.st, mktdata=GOOG)
add.rule(strategy.st,
name="ruleSignal",
arguments=list(sigcol="longEntry",
sigval=TRUE,
ordertype="market",
orderside="long",
replace=FALSE,
prefer="Open",
orderqty = 1),
type="enter", path.dep=TRUE)
add.rule(strategy.st,
name="ruleSignal",
arguments=list(sigcol="longExit",
sigval=TRUE,
orderqty="all",
ordertype="market", orderside="long",
replace=FALSE, prefer="Open"),
type="exit", path.dep=TRUE)
applyStrategy(strategy=strategy.st,portfolios=portfolio.st)
updatePortf(portfolio.st)
dateRange <- time(getPortfolio(portfolio.st)$summary)[-1]
updateAcct(portfolio.st,dateRange)
updateEndEq(account.st)
chart.Posn(portfolio.st, 'GOOG')
数据：
GOOG <- structure(list(date = structure(c(17375, 17378, 17379, 17380,
17381, 17382, 17385, 17386, 17387, 17388, 17389, 17392, 17393,
17394, 17395, 17396, 17399, 17400, 17401, 17402, 17403, 17406,
17407, 17408, 17409, 17410, 17414, 17415, 17416, 17417, 17420,
17421, 17422, 17423, 17424, 17427, 17428, 17429, 17430, 17431,
17434, 17435, 17436, 17437, 17438, 17441, 17442, 17443, 17444,
17445, 17448, 17449, 17450, 17451, 17452, 17455, 17456, 17457,
17458, 17459, 17462, 17463, 17464, 17465, 17466, 17469, 17470,
17471, 17472, 17473, 17476, 17477, 17478, 17479, 17480, 17483,
17484, 17485, 17486, 17487, 17490, 17491, 17492, 17494, 17497,
17498, 17499, 17500, 17501, 17504, 17505, 17506, 17507, 17508,
17511, 17512, 17513, 17514, 17515, 17518), class = "Date"), open = c(929.400024,
941.890015, 932.380005, 928.609985, 930.340027, 926.75, 929.059998,
927.090027, 920.609985, 917.549988, 907.969971, 922.530029, 924.22998,
925.289978, 925.780029, 910.309998, 910, 912.719971, 921.929993,
928.659973, 923.48999, 916, 905.099976, 920.049988, 931.76001,
941.130005, 933.080017, 930.150024, 931.72998, 936.48999, 934.25,
932.590027, 930.659973, 931.25, 924.659973, 920.01001, 917.419983,
922.97998, 933, 927.75, 925.450012, 923.719971, 927.73999, 941.359985,
952, 959.97998, 954, 957, 955.48999, 966.700012, 980, 980, 973.719971,
987.450012, 992, 992.099976, 990.289978, 991.77002, 986, 989.440002,
989.52002, 970, 968.369995, 980, 1009.190002, 1014, 1015.219971,
1017.210022, 1021.76001, 1022.109985, 1028.98999, 1027.27002,
1030.52002, 1033.98999, 1026.459961, 1023.419983, 1022.590027,
1019.210022, 1022.52002, 1034.01001, 1020.26001, 1023.309998,
1035, 1035.869995, 1040, 1055.089966, 1042.680054, 1022.369995,
1015.799988, 1012.659973, 995.940002, 1001.5, 1020.429993, 1037.48999,
1035.5, 1039.630005, 1046.119995, 1045, 1054.609985, 1066.079956
), high = c(943.830017, 943.590027, 937.447021, 932.599976, 932.23999,
930.307007, 931.700012, 935.814026, 925.97998, 919.26001, 917.780029,
924.66803, 926.549988, 932.700012, 926.859985, 915.275024, 913,
925.859985, 929.929993, 930.840027, 925.554993, 919.244995, 923.330017,
930.81897, 941.97998, 942.47998, 937, 930.914978, 936.409973,
936.98999, 938.380005, 933.47998, 937.25, 932.77002, 926.48999,
922.080017, 922.419983, 933.880005, 936.530029, 934.72998, 926.400024,
930.820007, 949.900024, 950.690002, 959.786011, 962.539978, 958,
960.390015, 970.909973, 979.460022, 985.424988, 981.570007, 990.710022,
994.119995, 997.210022, 993.906982, 996.440002, 996.719971, 988.880005,
991, 989.52002, 972.22998, 976.090027, 987.599976, 1048.390015,
1024.969971, 1024, 1029.670044, 1028.089966, 1032.650024, 1034.869995,
1033.969971, 1043.521973, 1033.98999, 1030.76001, 1031.579956,
1026.810059, 1024.089966, 1035.920044, 1034.420044, 1022.609985,
1035.109985, 1039.706055, 1043.177979, 1055.459961, 1062.375,
1044.079956, 1028.48999, 1022.48999, 1016.099976, 1020.609985,
1024.969971, 1034.23999, 1042.050049, 1043.800049, 1050.310059,
1046.665039, 1058.5, 1067.619995, 1078.48999), low = c(927.5,
926.039978, 929.26001, 916.679993, 922.23999, 923.030029, 926.5,
925.609985, 917.25, 906.130005, 905.580017, 918.190002, 919.820007,
923.445007, 910.97998, 907.153992, 903.400024, 911.474976, 919.359985,
915.5, 915.5, 911.869995, 905, 919.650024, 931.76001, 935.150024,
921.960022, 919.27002, 923.619995, 924.880005, 926.919983, 923.861023,
929.859985, 924, 916.359985, 910.599976, 912.549988, 922, 923.830017,
926.47998, 909.700012, 921.140015, 927.73999, 940.549988, 951.51001,
947.840027, 949.140015, 950.690002, 955.179993, 963.359985, 976.109985,
966.080017, 972.25, 985, 989, 984, 988.590027, 986.974976, 978.390015,
984.580017, 966.119995, 961, 960.52002, 972.200012, 1008.200012,
1007.5, 1010.419983, 1016.950012, 1013.01001, 1020.309998, 1025,
1025.130005, 1028.449951, 1019.666016, 1025.280029, 1022.570007,
1014.150024, 1015.419983, 1022.52002, 1017.75, 1017.5, 1022.655029,
1031.430054, 1035, 1038.439941, 1040, 1015.650024, 1015, 1002.02002,
995.570007, 988.280029, 1001.140015, 1018.070984, 1032.521973,
1032.050049, 1033.689941, 1038.380005, 1043.109985, 1049.5, 1062
), close = c(941.530029, 930.5, 930.830017, 930.390015, 923.650024,
927.960022, 929.359985, 926.789978, 922.900024, 907.23999, 914.390015,
922.669983, 922.219971, 926.960022, 910.97998, 910.669983, 906.659973,
924.690002, 927, 921.280029, 915.890015, 913.809998, 921.289978,
929.570007, 939.330017, 937.340027, 928.450012, 927.809998, 935.950012,
926.5, 929.080017, 932.070007, 935.090027, 925.109985, 920.289978,
915, 921.809998, 931.580017, 932.450012, 928.530029, 920.969971,
924.859985, 944.48999, 949.5, 959.109985, 953.27002, 957.789978,
951.679993, 969.960022, 978.890015, 977, 972.599976, 989.25,
987.830017, 989.679993, 992, 992.179993, 992.809998, 984.450012,
988.200012, 968.450012, 970.539978, 973.330017, 972.559998, 1019.27002,
1017.109985, 1016.640015, 1025.5, 1025.579956, 1032.47998, 1025.900024,
1033.329956, 1039.849976, 1031.26001, 1028.069946, 1025.75, 1026,
1020.909973, 1032.5, 1019.090027, 1018.380005, 1034.48999, 1035.959961,
1040.609985, 1054.209961, 1047.410034, 1021.659973, 1021.409973,
1010.169983, 998.679993, 1005.150024, 1018.380005, 1030.930054,
1037.050049, 1041.099976, 1040.47998, 1040.609985, 1049.150024,
1064.189941, 1077.140015), volume = c(1846400, 1970100, 1277700,
1824400, 1202500, 1082300, 1032200, 1061600, 1192100, 1824000,
1206800, 1064500, 883400, 1006700, 1277200, 1342700, 943400,
1166700, 1090200, 1270300, 1053400, 1086500, 1185600, 1301200,
1582600, 947400, 1326400, 1527700, 1212700, 1011500, 1267000,
1134400, 1102600, 1397600, 2505400, 1306900, 936700, 1669800,
1290600, 1052700, 1856800, 1666900, 2239400, 1020300, 1581000,
1283400, 888300, 952400, 1213800, 1173900, 891400, 968400, 1693300,
1262400, 1169800, 910500, 1290200, 1057600, 1313600, 1183200,
1478400, 1212200, 1211300, 2042100, 5167700, 2085100, 1330700,
1373400, 1049000, 1076400, 1125200, 1112300, 1088700, 1245200,
720000, 885800, 959200, 854000, 1129700, 1397100, 953500, 1097000,
746300, 537000, 1307900, 1424400, 2459400, 1724000, 1909600,
1906400, 2067300, 1272000, 1458200, 1290800, 1192800, 1279500,
1282700, 1558700, 3275900, 1554600), adjusted = c(941.530029,
930.5, 930.830017, 930.390015, 923.650024, 927.960022, 929.359985,
926.789978, 922.900024, 907.23999, 914.390015, 922.669983, 922.219971,
926.960022, 910.97998, 910.669983, 906.659973, 924.690002, 927,
921.280029, 915.890015, 913.809998, 921.289978, 929.570007, 939.330017,
937.340027, 928.450012, 927.809998, 935.950012, 926.5, 929.080017,
932.070007, 935.090027, 925.109985, 920.289978, 915, 921.809998,
931.580017, 932.450012, 928.530029, 920.969971, 924.859985, 944.48999,
949.5, 959.109985, 953.27002, 957.789978, 951.679993, 969.960022,
978.890015, 977, 972.599976, 989.25, 987.830017, 989.679993,
992, 992.179993, 992.809998, 984.450012, 988.200012, 968.450012,
970.539978, 973.330017, 972.559998, 1019.27002, 1017.109985,
1016.640015, 1025.5, 1025.579956, 1032.47998, 1025.900024, 1033.329956,
1039.849976, 1031.26001, 1028.069946, 1025.75, 1026, 1020.909973,
1032.5, 1019.090027, 1018.380005, 1034.48999, 1035.959961, 1040.609985,
1054.209961, 1047.410034, 1021.659973, 1021.409973, 1010.169983,
998.679993, 1005.150024, 1018.380005, 1030.930054, 1037.050049,
1041.099976, 1040.47998, 1040.609985, 1049.150024, 1064.189941,
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predictions = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
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2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L), .Label = c("0", "1"), class = "factor")), row.names = c("1",
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"91", "92", "93", "94", "95", "96", "97", "98", "99", "100"), class = "data.frame")
老问题（我离开这里是为了完整）
老问题（带数据）
我一直在使用 quantstrat 包，但我 运行 遇到了一个错误。
当我应用 applySignals(strategy = strategy.st, mktdata=GOOG) 行时出现错误。我收到此错误：
Error in `colnames<-`(`*tmp*`, value = label) :
attempt to set 'colnames' on an object with less than two dimensions
我使用 traceback() 并获得此报告：
5: stop("attempt to set 'colnames' on an object with less than two dimensions")
4: `colnames<-`(`*tmp*`, value = label)
3: (function (label, data = mktdata, column, threshold = 0, relationship = c("gt",
"lt", "eq", "gte", "lte"), cross = FALSE)
{
relationship = relationship[1]
ret_sig = NULL
colNum <- match.names(column, colnames(data))
switch(relationship, `>` = , gt = {
ret_sig = data[, colNum] > threshold
}, `<` = , lt = {
ret_sig = data[, colNum] < threshold
}, eq = {
ret_sig = data[, colNum] == threshold
}, gte = , gteq = , ge = {
ret_sig = data[, colNum] >= threshold
}, lte = , lteq = , le = {
ret_sig = data[, colNum] <= threshold
})
if (isTRUE(cross))
ret_sig <- diff(ret_sig) == 1
if (!missing(label))
colnames(ret_sig) <- label
return(ret_sig)
})(label = "longEntry", data = mktdata, column = "predictions",
threshold = 0.5, relationship = "gte", cross = TRUE)
2: do.call(sigFun, .formals)
1: applySignals(strategy = strategy.st, mktdata = GOOG)
根据我的研究和寻找解决方案 here，这是我的信号问题。
解决方案的 link 建议 head(mktdata) - 这是在 运行 applySignals() 行之后创建的。一种不同的策略（如果需要我可以添加代码）创建 mktdata 列名如下：
> colnames(mktdata)
[1] "SPY.Open" "SPY.High" "SPY.Low"
[4] "SPY.Close" "SPY.Volume" "SPY.Adjusted"
[7] "nonDerivedIndicator" "longEntry" "longExit"
但是我使用 tidyquant 导入了数据，列名是；
> colnames(GOOG)
[1] "open" "high" "low" "close"
[5] "volume" "adjusted" "daily.returns" "predictions"
这是我面临的问题吗？只是一个列名问题？也就是说，quantmod 将数据导入为 SPY.Open 并且 quantstrat 是在考虑到这一点的情况下构建的，并期望这种格式而不是整洁的格式？还是我遇到了不同的错误？
量化代码：
currency('USD')
Sys.setenv(TZ="UTC")
stock("GOOG", currency="USD", multiplier=1)
symbols <- c("GOOG")
strategy.st <- portfolio.st <- account.st <- "SVMstrat"
rm.strat(strategy.st)
initPortf(portfolio.st, symbols=symbols, initDate=initDate, currency='USD')
initAcct(account.st, portfolios=portfolio.st, initDate=initDate, currency='USD')
initOrders(portfolio.st, initDate=initDate)
strategy(strategy.st, store=TRUE)
GOOG$predictions <- as.numeric(as.character(GOOG$predictions))
mean(GOOG$direction == GOOG$predictions)
add.signal(strategy.st,
name = sigThreshold,
arguments = list(column = "predictions",
threshold = 0.5,
relationship = "gte",
cross = TRUE),
label = "longEntry")
add.signal(strategy.st,
name = sigThreshold,
arguments = list(column = "predictions",
threshold = 0.5,
relationship = "lte",
cross = TRUE),
label = "longExit")
applySignals(strategy = strategy.st, mktdata=GOOG)
数据：
(Had to remove old data for new question)
编辑：
我已经重新运行模型，但仍然运行进入同样的错误：
colnames(GOOG) <- c("GOOG.Open", "GOOG.High", "GOOG.Low",
"GOOG.Close", "GOOG.Volume", "GOOG.Adjusted",
"daily.returns", "predictions")
回答我自己的问题。
看起来 quantstrat 接受 xts 个对象而 quantmod 产生 tibbles。
我所要做的就是在 运行 实施策略之前将我的数据转换为 xts 对象。
GOOG <- xts(GOOG, order.by = GOOG$date)
GOOG$date <- NULL
编辑：
对于工作策略 (SPY)，我得到以下 xts 结构输出。
An ‘xts’ object on 2003-01-02/2012-12-28 containing:
Data: num [1:2516, 1:7] 72.9 74.6 74.8 76.2 75.6 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:7] "SPY.Open" "SPY.High" "SPY.Low" "SPY.Close" ...
Indexed by objects of class: [Date] TZ: UTC
xts Attributes:
List of 2
$ src : chr "yahoo"
$ updated: POSIXct[1:1], format: "2018-12-09 12:43:49"
对于我的代码 - 使用 tidyquant 导入，添加一些指标，运行 SVM 模型，然后将其转换回 xts 我有以下结构输出。
An ‘xts’ object on 2017-07-28/2017-12-18 containing:
Data: chr [1:100, 1:11] " 929.40" " 941.89" " 932.38" " 928.61" ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:11] "open" "high" "low" "close" ...
Indexed by objects of class: [Date] TZ: UTC
xts Attributes:
NULL
我错过了
List of 2
$ src : chr "yahoo"
$ updated: POSIXct[1:1], format: "2018-12-09 12:43:49"
部分。