「咨询开源硬件」支援高频量化对冲算筹

[englianhu]

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Need to upgraded my equipement to enchance the efficiency...

Originally posted by @englianhu in englianhu/ali-zonghui#9 (comment)

[englianhu]

• cannot allocate vector of size 1.1 Gb JinmiaoChenLab/cytofkit#17 (comment) and cannot allocate vector of size 1.1 Gb JinmiaoChenLab/cytofkit#17 (comment)

> mts <- smp %>% 
+     msts(seasonal.periods = c(1440, nrow(smp)))
>   
>   mts <- llply(1:ncol(mts), function(i) {
+     y <- mts[,i] %>% 
+       tbats %>% 
+       forecast(h = 1440) %>% 
+       as_tibble
+       names(y)[1] <- names(smp)[i]
+       y
+     }) %>% 
+     bind_rows %>% 
+     mutate(Model = factor('tbats'), Period = factor('dy.qt'), type = case_when(
+       !is.na(open) ~ 'open', 
+       !is.na(high) ~ 'high', 
+       !is.na(low) ~ 'low', 
+       !is.na(close) ~ 'close')) %>% 
+     dlply(.(type, Period), function(x) {
+       x %<>% dplyr::rename(open.Point.Forecast = open, 
+                            high.Point.Forecast = high, 
+                            low.Point.Forecast = low, 
+                            close.Point.Forecast = close)
+       names(x)[str_detect(names(x), '80|95')] <- paste0(x$type[1], '.', names(x)[str_detect(names(x), '80|95')])
+       x[colSums(!is.na(x)) > 0] %>% 
+         data.frame %>% 
+         as_tibble %>% 
+         dplyr::select(-type)
+       
+       }) %>% 
+     join_all %>% 
+     as_tibble
Joining by: Model, Period
Joining by: Model, Period
- Error: cannot allocate vector of size 11.1 Gb
>   memory.size()
[1] 11984.01
> gc()
           used  (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells  3767976 201.3    7315067   390.7    7315067   390.7
Vcells 12814470  97.8 1474157596 11247.0 1534873241 11710.2
> memory.limit()
[1] 16274

memory.size() occupied 75% of memory.limit()

> mts <- smp %>% 
+     msts(seasonal.periods = c(1440, nrow(smp)))
>   
>   mts <- llply(1:ncol(mts), function(i) {
+     y <- mts[,i] %>% 
+       tbats %>% 
+       forecast(h = 1440) %>% 
+       as_tibble
+       names(y)[1] <- names(smp)[i]
+       y
+     }) %>% 
+     bind_rows %>% 
+     mutate(Model = factor('tbats'), Period = factor('dy.qt'), type = case_when(
+       !is.na(open) ~ 'open', 
+       !is.na(high) ~ 'high', 
+       !is.na(low) ~ 'low', 
+       !is.na(close) ~ 'close')) %>% 
+     dlply(.(type, Period), function(x) {
+       x %<>% dplyr::rename(open.Point.Forecast = open, 
+                            high.Point.Forecast = high, 
+                            low.Point.Forecast = low, 
+                            close.Point.Forecast = close)
+       names(x)[str_detect(names(x), '80|95')] <- paste0(x$type[1], '.', names(x)[str_detect(names(x), '80|95')])
+       x[colSums(!is.na(x)) > 0] %>% 
+         data.frame %>% 
+         as_tibble %>% 
+         dplyr::select(-type)
+       
+       })
> mtss <- mts %>% bind_cols
New names:
* Model -> Model...6
* Period -> Period...7
* Model -> Model...13
* Period -> Period...14
* Model -> Model...20
* ...
> mtss
# A tibble: 1,440 x 28
   close.Lo.80 close.Hi.80 close.Lo.95 close.Hi.95 close.Point.For~ Model...6
         <dbl>       <dbl>       <dbl>       <dbl>            <dbl> <fct>    
 1        118.        118.        118.        118.             118. tbats    
 2        118.        118.        118.        118.             118. tbats    
 3        118.        118.        118.        118.             118. tbats    
 4        118.        118.        118.        118.             118. tbats    
 5        118.        118.        118.        118.             118. tbats    
 6        118.        118.        118.        118.             118. tbats    
 7        118.        118.        118.        118.             118. tbats    
 8        118.        118.        118.        118.             118. tbats    
 9        118.        118.        118.        118.             118. tbats    
10        118.        118.        118.        118.             118. tbats    
# ... with 1,430 more rows, and 22 more variables: Period...7 <fct>,
#   high.Lo.80 <dbl>, high.Hi.80 <dbl>, high.Lo.95 <dbl>, high.Hi.95 <dbl>,
#   high.Point.Forecast <dbl>, Model...13 <fct>, Period...14 <fct>,
#   low.Lo.80 <dbl>, low.Hi.80 <dbl>, low.Lo.95 <dbl>, low.Hi.95 <dbl>,
#   low.Point.Forecast <dbl>, Model...20 <fct>, Period...21 <fct>,
#   open.Point.Forecast <dbl>, open.Lo.80 <dbl>, open.Hi.80 <dbl>,
#   open.Lo.95 <dbl>, open.Hi.95 <dbl>, Model...27 <fct>, Period...28 <fct>
> mtss[str_detect(names(mtss), 'Model.|Period.')]
# A tibble: 1,440 x 8
   Model...6 Period...7 Model...13 Period...14 Model...20 Period...21 Model...27
   <fct>     <fct>      <fct>      <fct>       <fct>      <fct>       <fct>     
 1 tbats     dy.qt      tbats      dy.qt       tbats      dy.qt       tbats     
 2 tbats     dy.qt      tbats      dy.qt       tbats      dy.qt       tbats     
 3 tbats     dy.qt      tbats      dy.qt       tbats      dy.qt       tbats     
 4 tbats     dy.qt      tbats      dy.qt       tbats      dy.qt       tbats     
 5 tbats     dy.qt      tbats      dy.qt       tbats      dy.qt       tbats     
 6 tbats     dy.qt      tbats      dy.qt       tbats      dy.qt       tbats     
 7 tbats     dy.qt      tbats      dy.qt       tbats      dy.qt       tbats     
 8 tbats     dy.qt      tbats      dy.qt       tbats      dy.qt       tbats     
 9 tbats     dy.qt      tbats      dy.qt       tbats      dy.qt       tbats     
10 tbats     dy.qt      tbats      dy.qt       tbats      dy.qt       tbats     
# ... with 1,430 more rows, and 1 more variable: Period...28 <fct>
> mtss[str_detect(names(mtss), 'Model.|Period.')] <- NULL
> mtss

I used bind_cols() as alternative method to solve my issue, there has different methods depends on your issue (example: normally matrix will be more efficient than data.frame)...

lineprof in Advanced R by Hadley Wickham
microbenchmark in Advanced R by Hadley Wickham

There has eventually need to use better pc for high efficiency computing...

[englianhu]

出处：国际知名 Linux 发行版 deepin V23 正式发布

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