## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
##
## ######################### Warning from 'xts' package ##########################
## # #
## # The dplyr lag() function breaks how base R's lag() function is supposed to #
## # work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or #
## # source() into this session won't work correctly. #
## # #
## # Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
## # conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop #
## # dplyr from breaking base R's lag() function. #
## # #
## # Code in packages is not affected. It's protected by R's namespace mechanism #
## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning. #
## # #
## ###############################################################################
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:xts':
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## first, last
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## Loading required package: ggplot2
## Loading required package: lattice
## [1] "SPY"
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning in geom_text(aes(x = which.max(accuracy), y = max(accuracy), label = paste("Max:", : All aesthetics have length 1, but the data has 20 rows.
## ℹ Please consider using `annotate()` or provide this layer with data containing
## a single row.
## Confusion Matrix and Statistics
##
## Reference
## Prediction -1 0 1
## -1 879 3 431
## 0 0 0 0
## 1 542 21 1225
##
## Overall Statistics
##
## Accuracy : 0.6785
## 95% CI : (0.6617, 0.6949)
## No Information Rate : 0.534
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3545
##
## Mcnemar's Test P-Value : 5.423e-08
##
## Statistics by Class:
##
## Class: -1 Class: 0 Class: 1
## Sensitivity 0.6186 0.000000 0.7397
## Specificity 0.7417 1.000000 0.6104
## Pos Pred Value 0.6695 NaN 0.6851
## Neg Pred Value 0.6969 0.992261 0.6717
## Prevalence 0.4582 0.007739 0.5340
## Detection Rate 0.2835 0.000000 0.3950
## Detection Prevalence 0.4234 0.000000 0.5766
## Balanced Accuracy 0.6801 0.500000 0.6751
## Sensitivity Specificity Precision Recall F1
## Class: -1 0.619 0.742 0.669 0.619 0.643
## Class: 0 0.000 1.000 NA 0.000 NA
## Class: 1 0.740 0.610 0.685 0.740 0.711