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library(shiny)
library(shinydashboard)
library(shinydashboardPlus)
library(shinyWidgets)
library(shinycssloaders)
library(DT)
library(plotly)
library(scico)
library(ggthemes)
library(scales)
library(stringr)
library(wesanderson)
library(data.table)
library(dtplyr)
library(parallel)
# devtools::install_github("woobe/Rnumerai")
library(Rnumerai)
# ==============================================================================
# Helper Functions
# ==============================================================================
# Download raw data
download_raw_data <- function(model_name) {
# Download data from Numerai
d_raw <- round_model_performances(model_name)
# Remove rows without CORR
d_raw <- d_raw[!is.na(d_raw$corrWMetamodel), ]
# Add the model name
d_raw$model <- model_name
# Return
return(as.data.table(d_raw))
}
# Reformat
reformat_data <- function(d_raw) {
# Keep some columns only
col_keep <- c("model", "roundNumber",
"roundOpenTime", "roundResolveTime",
"roundResolved", "selectedStakeValue",
"corr20V2", "corr20V2Percentile",
"fncV3", "fncV3Percentile",
"tc", "tcPercentile",
"corrWMetamodel",
"apcwnm", "mcwnm",
"roundPayoutFactor", "payout")
d_munged <- d_raw[, col_keep, with = FALSE]
# Date
d_munged[, roundOpenTime := as.Date(roundOpenTime)]
d_munged[, roundResolveTime := as.Date(roundResolveTime)]
# Reformat percentile
d_munged[, corr20V2Percentile := round(corr20V2Percentile * 100, 6)]
d_munged[, fncV3Percentile := round(fncV3Percentile * 100, 6)]
d_munged[, tcPercentile := round(tcPercentile * 100, 6)]
# Rename columns
colnames(d_munged) <- c("model", "round",
"date_open", "date_resolved",
"resolved", "stake",
"corrV2", "corrV2_pct",
"fncV3", "fncV3_pct",
"tc", "tc_pct",
"corr_meta",
"apcwnm", "mcwnm",
"pay_ftr", "payout")
# Return
return(d_munged)
}
# Generate Colour Palette
gen_custom_palette <- function(ls_model) {
# Extract info
n_limit <- 5
n_coluor <- length(unique(ls_model))
n_pal_rep <- ceiling(n_coluor / n_limit)
wes_pal_themes <- rep(c("Cavalcanti1", "Darjeeling1"), n_pal_rep)
# Generate
custom_palette <- c()
for (n_pal in 1:n_pal_rep) {
tmp_pal_name <- wes_pal_themes[n_pal]
tmp_pal <- wesanderson::wes_palette(name = tmp_pal_name, n = n_limit, type = "continuous")
custom_palette <- c(custom_palette, tmp_pal)
}
# Trim and return
return(custom_palette[1:n_coluor])
}
# ==============================================================================
# UI
# ==============================================================================
ui <- shinydashboardPlus::dashboardPage(
title = "Shiny Numerati",
skin = "black-light",
options = list(sidebarExpandOnHover = TRUE),
header = shinydashboardPlus::dashboardHeader(
title = "✨ Shiny Numerati",
userOutput("user")
),
# ============================================================================
# Sidebar
# ============================================================================
sidebar = shinydashboardPlus::dashboardSidebar(
id = "sidebar",
sidebarMenu(
menuItem(text = "Start Here", tabName = "start", icon = icon("play")),
menuItem(text = "Performance Summary", tabName = "performance", icon = icon("line-chart")), # icon("credit-card")
menuItem(text = "Raw Data", tabName = "raw_data", icon = icon("download")),
menuItem(text = "Community Events", tabName = "community", icon = icon("users")),
menuItem(text = "About", tabName = "about", icon = icon("question-circle"))
),
minified = TRUE,
collapsed = FALSE
),
# ============================================================================
# Main Body
# ============================================================================
body = dashboardBody(
tabItems(
# ========================================================================
# Start Here
# ========================================================================
tabItem(tabName = "start",
fluidPage(
# ==============================================================
# Special script to keep the session alive for a bit longer
# ==============================================================
tags$head(
HTML(
"
<script>
var socket_timeout_interval
var n = 0
$(document).on('shiny:connected', function(event) {
socket_timeout_interval = setInterval(function(){
Shiny.onInputChange('count', n++)
}, 10000)
});
$(document).on('shiny:disconnected', function(event) {
clearInterval(socket_timeout_interval)
});
</script>
"
)
),
# ==============================================================
# First Page
# ==============================================================
markdown("# **Shiny Numerati**"),
markdown("### Community Dashboard for the Numerai Classic Tournament"),
br(),
fluidRow(
column(6,
markdown("## **Step 1: Select Your Models**"),
markdown("### First, click this ⬇"),
pickerInput(inputId = "model",
label = " ",
# choices = sort(Rnumerai::get_leaderboard()$username),
choices = unique(c(sort(Rnumerai::get_leaderboard()$username),
"joe_the_validator_01",
"joe_the_validator_02",
"joe_the_validator_03",
"joe_the_validator_04",
"joe_the_validator_05"
# "joe_the_hedgehog_01",
# "joe_the_hedgehog_02",
# "joe_the_hedgehog_03",
# "joe_the_hedgehog_04",
# "joe_the_hedgehog_05"
)
),
multiple = TRUE,
width = "100%",
options = list(
`title` = "---------->>> HERE <<<----------",
`header` = "Notes: 1) Use the search box below to find and select your models. 2) Use 'Select All' for quick selection.",
size = 20,
`actions-box` = TRUE,
`live-search` = TRUE,
`live-search-placeholder` = "For example, try lgbm_v4 or integration_test",
`virtual-scroll` = TRUE,
`multiple-separator` = ", ",
`selected-text-format`= "count > 3",
`count-selected-text` = "{0} models selected (out of {1})",
`deselect-all-text` = "Deselect All",
`select-all-text` = "Select All"
)
)
),
column(6,
markdown("## **Step 2: Download Data**"),
markdown("### Next, click this ⬇ (it may take a while)"),
br(),
actionBttn(inputId = "button_download",
label = "Download Data from Numerai",
color = "primary",
icon = icon("cloud-download"),
style = "gradient",
block = TRUE
)
)
),
br(),
h3(strong(textOutput(outputId = "text_download"))),
verbatimTextOutput(outputId = "print_download"),
br(),
h3(strong(textOutput(outputId = "text_preview"))),
shinycssloaders::withSpinner(DTOutput("dt_model")),
br(),
h2(strong(textOutput(outputId = "text_next"))),
h3(textOutput(outputId = "text_note")),
br()
)
),
# ========================================================================
# Payout Summary
# ========================================================================
tabItem(tabName = "performance",
fluidPage(
markdown("# **Performance Summary**"),
markdown("### Remember to refresh the charts after making changes to model selection or settings below."),
markdown("### **NOTE**: the charts may take a while to render if you have selected a lot of models."),
br(),
fluidRow(
column(6,
markdown("## **Step 4: Adjust the Filter**"),
sliderInput(inputId = "range_round",
label = "Numerai Classic Tournament Rounds",
width = "100%",
step = 1,
min = 168, # first tournament round
max = Rnumerai::get_current_round(), # note: daily payouts from round 474
value = c(496, Rnumerai::get_current_round())
)
),
column(6,
markdown("## **Step 5: Generate Summary**"),
br(),
actionBttn(inputId = "button_filter",
label = "Generate / Refresh",
color = "primary",
icon = icon("refresh"),
style = "gradient",
block = TRUE)
)
), # end of fluidRow
br(),
tabsetPanel(type = "tabs",
tabPanel("KPI (CORR+TC)",
br(),
h3(strong(textOutput(outputId = "text_performance_models"))),
h4(textOutput(outputId = "text_performance_models_note")),
br(),
fluidRow(
column(width = 6, plotlyOutput("plot_performance_avg")),
column(width = 6, plotlyOutput("plot_performance_sharpe"))
),
br(),
br(),
br(),
fluidRow(DTOutput("dt_performance_summary"),
br(),
markdown("#### **Notes**:
- **avg_corrV2**: Average `CORRv2`
- **sharpe_corrV2**: Sharpe Ratio of `CORRv2`
- **avg_tc**: Average True Contribution (`TC`)
- **sharpe_tc**: Sharpe Ratio of True Contribution (`TC`)
- **avg_2C1T**: Average `2xCORRv2 + 1xTC`
- **sharpe_2C1T**: Sharpe Ratio of `2xCORRv2 + 1xTC`
"),
br()
),
br()
),
# Coming soon
# tabPanel("KPI (All)",
#
# br(),
#
# markdown("xxx"),
#
# br()
# ),
tabPanel("Payout (Overview)",
br(),
h3(strong(textOutput(outputId = "text_payout_overview"))),
br(),
fluidRow(
class = "text-center",
valueBoxOutput("payout_n_round_resolved", width = 3),
valueBoxOutput("payout_resolved", width = 3),
valueBoxOutput("payout_average_resolved", width = 3),
valueBoxOutput("payout_avg_ror_resolved", width = 3),
valueBoxOutput("payout_n_round_pending", width = 3),
valueBoxOutput("payout_pending", width = 3),
valueBoxOutput("payout_average_pending", width = 3),
valueBoxOutput("payout_avg_ror_pending", width = 3),
valueBoxOutput("payout_n_round", width = 3),
valueBoxOutput("payout_total", width = 3),
valueBoxOutput("payout_average", width = 3),
valueBoxOutput("payout_avg_ror", width = 3)
),
br(),
shinycssloaders::withSpinner(plotlyOutput("plot_payout_net")),
br()
),
tabPanel("Payout (Rounds)",
br(),
h3(strong(textOutput(outputId = "text_payout_rnd"))),
br(),
DTOutput("dt_payout_summary"),
br()
),
tabPanel("Payout (Models)",
br(),
h3(strong(textOutput(outputId = "text_payout_ind"))),
br(),
DTOutput("dt_model_payout_summary"),
br()
),
tabPanel("Payout (Simulation)",
br(),
h3(strong(textOutput(outputId = "text_payout_sim"))),
br(),
markdown(""),
br(),
markdown("#### **Notes**:
- **sum_pay**: Sum of Payouts
- **shp_pay**: Sharpe Ratio of Payouts
- **1C0T**: 1xCORRv2 + 0xTC
- **2C0T**: 2xCORRv2 + 0xTC (New Payout Mode)
- **2C1T**: 2xCORRv2 + 1xTC (New Payout Mode)
- **1C3T**: 1xCORRv2 + 3xTC (Original Degen Mode)
"),
br(),
markdown("### **Payout Simulation (Overall)**"),
DTOutput("dt_payout_sim_overall"),
br(),
br(),
markdown("### **Payout Simulation (Individual Models)**"),
br(),
DTOutput("dt_payout_sim_model"),
br()
),
tabPanel("Payout Chart (Rounds)",
br(),
h3(strong(textOutput(outputId = "text_payout_all_models"))),
br(),
shinycssloaders::withSpinner(plotlyOutput("plot_payout_stacked")),
br()
),
tabPanel("Payout Chart (Models)",
br(),
h3(strong(textOutput(outputId = "text_payout_ind_models"))),
br(),
shinycssloaders::withSpinner(plotlyOutput("plot_payout_individual")),
br()
)
) # end of tabsetPanel
) # end of fluidPage
),
# ========================================================================
# Raw Data
# ========================================================================
tabItem(tabName = "raw_data",
markdown("# **Download Raw Data**"),
markdown("### Wanna run your own analysis? No problem."),
markdown("### Remember to select your model(s) first."),
br(),
fluidRow(
column(6,
downloadBttn(outputId = "download_raw",
label = "Download Raw Data CSV",
icon = icon("cloud-download"),
style = "gradient",
block = T)
)
)
),
# ========================================================================
# Community
# ========================================================================
tabItem(tabName = "community",
fluidRow(
column(10,
htmltools::includeMarkdown('https://raw.githubusercontent.com/councilofelders/meetups/master/README.md')
)
)
# markdown("")
),
# ========================================================================
# About
# ========================================================================
tabItem(tabName = "about",
markdown("# **About this App**"),
markdown('### Yet another Numerai community dashboard by <b><a href="https://linktr.ee/jofaichow" target="_blank">Jo-fai Chow</a></b>.'),
br(),
markdown("## **Acknowledgements**"),
markdown("- #### This hobby project was inspired by Rajiv's <b><a href='https://huggingface.co./spaces/rajistics/shiny-kmeans' target='_blank'>shiny-kmeans</a></b> on 🤗 Spaces."),
markdown('- #### The <b><a href="https://linktr.ee/jofaichow" target="_blank">Rnumerai</a></b> package from Omni Analytics Group.'),
br(),
markdown("## **Changelog**"),
markdown(
"
- #### **0.1.0** — First prototype with an interactive table output
- #### **0.1.1** — Added a functional `Payout Summary` page
- #### **0.1.2** — `Payout Summary` layout updates
- #### **0.1.3** — Added `Raw Data`
- #### **0.1.4** — Various improvements in `Payout Summary`
- #### **0.1.5** — Replaced `corrV1` with `corrV2`
- #### **0.1.6** — Added `apcwnm` and `mcwnm`
- #### **0.1.7** — Added CoE Meetup GitHub page to `Community`
- #### **0.1.8** — Various improvements in `Payout Summary`
- #### **0.1.9** — Added `Payout Sim` based on new Corr and TC multipier settings
- #### **0.2.0** — Replaced `Payout Summary` with `Performance Summary`. Added KPIs summary.
"),
br(),
markdown("## **Session Info**"),
verbatimTextOutput(outputId = "session_info"),
br(),
textOutput("keepAlive") # trick to keep session alive
)
# ========================================================================
) # end of tabItems
),
footer = shinydashboardPlus::dashboardFooter(
left = "Powered by ❤️, ☕, Shiny, and 🤗 Spaces",
right = paste0("Version 0.2.0"))
)
# ==============================================================================
# Server
# ==============================================================================
server <- function(input, output) {
# About Joe
output$user <- renderUser({
dashboardUser(
name = "JC",
image = "https://numerai-public-images.s3.amazonaws.com/profile_images/aijoe_v5_compressed-iJWEo1WeHkpH.jpg",
subtitle = "@matlabulous",
footer = p('"THE NMR LIFE CHOSE ME."', class = 'text-center')
)
})
# ============================================================================
# Reactive: Data
# ============================================================================
react_ls_model <- eventReactive(input$button_download, {sort(input$model)})
output$print_download <- renderPrint({react_ls_model()})
output$text_download <- renderText({
if (length(react_ls_model()) >= 1) "Your Selection:" else " "
})
output$text_preview <- renderText({
if (length(react_ls_model()) >= 1) "Data Preview:" else " "
})
output$text_next <- renderText({
if (length(react_ls_model()) >= 1) "Step 3: Performance Summary (see ←)" else " "
})
output$text_note <- renderText({
if (length(react_ls_model()) >= 1) "Note: you can also download [Raw Data] and check out our [Community Events] (see ←)" else " "
})
react_d_raw <- eventReactive(
input$button_download,
{
# Parallelised download
d_raw <- rbindlist(mclapply(X = input$model,
FUN = download_raw_data,
mc.cores = detectCores()))
# Return
d_raw
}
)
# ============================================================================
# Reactive: DataTable
# ============================================================================
output$dt_model <- DT::renderDT({
# Raw Data
d_raw <- react_d_raw()
# Reformat
d_munged <- reformat_data(d_raw)
# Main DT
DT::datatable(
# Data
d_munged,
# Other Options
rownames = FALSE,
extensions = "Buttons",
options =
list(
dom = 'Bflrtip', # https://datatables.net/reference/option/dom
buttons = list('csv', 'excel', 'copy', 'print'), # https://rstudio.github.io/DT/003-tabletools-buttons.html
order = list(list(0, 'asc'), list(1, 'asc')),
pageLength = 10,
lengthMenu = c(10, 20, 100, 500, 1000, 50000),
columnDefs = list(list(className = 'dt-center', targets = "_all")))
) |>
# Reformat individual columns
formatRound(columns = c("corrV2", "tc", "fncV3", "corr_meta", "pay_ftr"), digits = 4) |>
formatRound(columns = c("apcwnm", "mcwnm"), digits = 4) |>
formatRound(columns = c("corrV2_pct", "tc_pct", "fncV3_pct"), digits = 1) |>
formatRound(columns = c("stake", "payout"), digits = 2) |>
formatStyle(columns = c("model"),
fontWeight = "bold") |>
formatStyle(columns = c("stake"),
fontWeight = "bold",
color = styleInterval(cuts = -1e-15, values = c("#D24141", "#2196F3"))) |>
formatStyle(columns = c("corrV2", "fncV3"),
color = styleInterval(cuts = -1e-15, values = c("#D24141", "black"))) |>
formatStyle(columns = c("tc"),
color = styleInterval(cuts = -1e-15, values = c("#D24141", "#A278DC"))) |>
formatStyle(columns = c("corrV2_pct", "tc_pct", "fncV3_pct"),
color = styleInterval(cuts = c(1, 5, 15, 85, 95, 99),
values = c("#692020", "#9A2F2F", "#D24141",
"#D1D1D1", # light grey
"#00A800", "#007000", "#003700"))) |>
formatStyle(columns = c("payout"),
fontWeight = "bold",
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800")))
})
# ============================================================================
# Reactive: filtering data for all charts
# ============================================================================
react_d_filter <- eventReactive(
input$button_filter,
{
# Reformat and Filter
d_filter <- reformat_data(react_d_raw())
d_filter <- d_filter[round >= input$range_round[1], ]
d_filter <- d_filter[round <= input$range_round[2], ]
# Return
d_filter
})
react_d_payout_summary <- eventReactive(
input$button_filter,
{
# Summarise payout
d_smry <-
react_d_filter() |>
lazy_dt() |>
filter(pay_ftr > 0) |>
filter(stake > 0) |>
group_by(round, date_open, date_resolved, resolved) |>
summarise(staked_models = n(),
total_stake = sum(stake, na.rm = T),
net_payout = sum(payout, na.rm = T)) |>
as.data.table()
d_smry$rate_of_return <- (d_smry$net_payout / d_smry$total_stake) * 100
# Return
d_smry
})
react_d_model_payout_summary <- eventReactive(
input$button_filter,
{
# Get filtered data
# d_smry <- as.data.table(react_d_filter() |> filter(pay_ftr > 0))
d_smry <-
react_d_filter() |>
lazy_dt() |>
filter(pay_ftr > 0) |>
filter(stake > 0) |>
as.data.table()
# Calculate rate of return (%)
d_smry[, rate_of_return_percent := payout / stake * 100]
# Summarise
d_smry <-
d_smry |>
lazy_dt() |>
group_by(model) |>
summarise(staked_rounds = n(),
net_payout = sum(payout, na.rm = T),
avg_payout = mean(payout, na.rm = T),
avg_rate_of_return_percent = mean(rate_of_return_percent, na.rm = T),
sharpe_rate_of_return = mean(rate_of_return_percent, na.rm = T) / sd(rate_of_return_percent, na.rm = T)
) |> as.data.table()
# Return
d_smry
})
react_d_payout_sim_model <- eventReactive(
input$button_filter,
{
# Get filtered data
# d_smry <- as.data.table(react_d_filter() |> filter(pay_ftr > 0))
d_payout <-
react_d_filter() |>
lazy_dt() |>
filter(pay_ftr > 0) |>
filter(stake > 0) |>
as.data.table()
# Apply clip to corrV2
d_payout[, corrV2_final := corrV2]
d_payout[corrV2 > 0.25, corrV2_final := 0.25]
d_payout[corrV2 < -0.25, corrV2_final := -0.25]
# Apply clip to tc
d_payout[, tc_final := tc]
d_payout[tc > 0.25, tc_final := 0.25]
d_payout[tc < -0.25, tc_final := -0.25]
# Calculate different payout
d_payout[, payout_1C0T := (corrV2_final) * stake * pay_ftr]
d_payout[, payout_2C0T := (2*corrV2_final) * stake * pay_ftr]
d_payout[, payout_2C1T := (2*corrV2_final + tc_final) * stake * pay_ftr]
d_payout[, payout_1C3T := (corrV2_final + 3*tc_final) * stake * pay_ftr]
# Summarise
d_payout_smry <-
d_payout |>
lazy_dt() |>
group_by(model) |>
summarise(
rounds = n(),
sum_pay_1C0T = sum(payout_1C0T, na.rm = T),
sum_pay_2C0T = sum(payout_2C0T, na.rm = T),
sum_pay_2C1T = sum(payout_2C1T, na.rm = T),
sum_pay_1C3T = sum(payout_1C3T, na.rm = T),
shp_pay_1C0T = mean(payout_1C0T, na.rm = T) / sd(payout_1C0T, na.rm = T),
shp_pay_2C0T = mean(payout_2C0T, na.rm = T) / sd(payout_2C0T, na.rm = T),
shp_pay_2C1T = mean(payout_2C1T, na.rm = T) / sd(payout_2C1T, na.rm = T),
shp_pay_1C3T = mean(payout_1C3T, na.rm = T) / sd(payout_1C3T, na.rm = T)
) |>
as.data.table()
# Return
d_payout_smry
})
react_d_payout_sim_overall <- eventReactive(
input$button_filter,
{
# Get filtered data
# d_payout <- as.data.table(react_d_filter() |> filter(pay_ftr > 0))
d_payout <-
react_d_filter() |>
lazy_dt() |>
filter(pay_ftr > 0) |>
filter(stake > 0) |>
as.data.table()
# Apply clip to corrV2
d_payout[, corrV2_final := corrV2]
d_payout[corrV2 > 0.25, corrV2_final := 0.25]
d_payout[corrV2 < -0.25, corrV2_final := -0.25]
# Apply clip to tc
d_payout[, tc_final := tc]
d_payout[tc > 0.25, tc_final := 0.25]
d_payout[tc < -0.25, tc_final := -0.25]
# Calculate different payout
d_payout[, payout_1C0T := (corrV2_final) * stake * pay_ftr]
d_payout[, payout_2C0T := (2*corrV2_final) * stake * pay_ftr]
d_payout[, payout_2C1T := (2*corrV2_final + tc_final) * stake * pay_ftr]
d_payout[, payout_1C3T := (corrV2_final + 3*tc_final) * stake * pay_ftr]
# Summarise
d_payout_smry <-
d_payout |>
lazy_dt() |>
summarise(
sum_pay_1C0T = sum(payout_1C0T, na.rm = T),
sum_pay_2C0T = sum(payout_2C0T, na.rm = T),
sum_pay_2C1T = sum(payout_2C1T, na.rm = T),
sum_pay_1C3T = sum(payout_1C3T, na.rm = T),
shp_pay_1C0T = mean(payout_1C0T, na.rm = T) / sd(payout_1C0T, na.rm = T),
shp_pay_2C0T = mean(payout_2C0T, na.rm = T) / sd(payout_2C0T, na.rm = T),
shp_pay_2C1T = mean(payout_2C1T, na.rm = T) / sd(payout_2C1T, na.rm = T),
shp_pay_1C3T = mean(payout_1C3T, na.rm = T) / sd(payout_1C3T, na.rm = T)
) |>
as.data.table()
# Return
d_payout_smry
})
react_d_performance_summary <- eventReactive(
input$button_filter,
{
# Get filtered data
d_pref <- as.data.table(react_d_filter())
# Add 2xCORRv2 + 1xTC
d_pref[, twoC_oneT := 2*corrV2 + tc]
# Calculate some high level stats
d_pref <-
d_pref |>
lazy_dt() |>
group_by(model) |>
summarise(total_rounds = n(),
avg_corrV2 = mean(corrV2, na.rm = T),
sharpe_corrV2 = mean(corrV2, na.rm = T) / sd(corrV2, na.rm = T),
# mdd_corrV2 = maxdrawdown(corrV2),
avg_tc = mean(tc, na.rm = T),
sharpe_tc = mean(tc, na.rm = T) / sd(tc, na.rm = T),
# mdd_tc = maxdrawdown(tc),
avg_2C1T = mean(twoC_oneT, na.rm = T),
sharpe_2C1T = mean(twoC_oneT, na.rm = T) / sd(tc, na.rm = T)
# mdd_2C1T = maxdrawdown(twoC_oneT)
) |> as.data.table()
# Return
d_pref
})
# ============================================================================
# Reactive: Payout Value Boxes
# ============================================================================
output$text_payout_overview <- renderText({
if (nrow(react_d_filter()) >= 1) "Payout Summary (Overview)" else " "
})
output$text_payout_rnd <- renderText({
if (nrow(react_d_filter()) >= 1) "Payout Summary (Tournament Rounds)" else " "
})
output$text_payout_ind <- renderText({
if (nrow(react_d_filter()) >= 1) "Payout Summary (Individual Models)" else " "
})
output$text_payout_all_models <- renderText({
if (nrow(react_d_filter()) >= 1) "Payout Summary Chart (All Models - Stacked)" else " "
})
output$text_payout_ind_models <- renderText({
if (nrow(react_d_filter()) >= 1) "Payout Summary Chart (Individual Models)" else " "
})
output$text_payout_sim <- renderText({
if (nrow(react_d_filter()) >= 1) "New Payout Simulation (NOTE: Experimental!)" else " "
})
output$text_performance_models <- renderText({
if (nrow(react_d_filter()) >= 1) "KPI Analysis (CORR and TC)" else " "
})
output$text_performance_models_note <- renderText({
if (nrow(react_d_filter()) >= 1) "NOTE: You may want to find out which models have high CORRv2 Sharpe and high TC Sharpe." else " "
})
output$text_performance_chart <- renderText({
if (nrow(react_d_filter()) >= 1) "KPI Charts" else " "
})
# ============================================================================
# Reactive valueBox outputs: Rounds
# ============================================================================
output$payout_n_round_resolved <- renderValueBox({
# Use rounds with stake > 0 only
valueBox(value = nrow(react_d_payout_summary()[resolved == TRUE & total_stake > 0, ]),
subtitle = "Staked Rounds (Resolved)",
color = "olive")
})
output$payout_n_round_pending <- renderValueBox({
# Use rounds with stake > 0 only
valueBox(value = nrow(react_d_payout_summary()[resolved == FALSE & total_stake > 0, ]),
subtitle = "Staked Rounds (Pending)",
color = "yellow")
})
output$payout_n_round <- renderValueBox({
# Use rounds with stake > 0 only
valueBox(value = nrow(react_d_payout_summary()[total_stake > 0, ]),
subtitle = "Staked Rounds (All)",
color = "light-blue")
})
# ============================================================================
# Reactive valueBox outputs: Payouts
# ============================================================================
output$payout_resolved <- renderValueBox({
valueBox(value = paste(as.character(format(round(sum(react_d_filter()[resolved == T, ]$payout, na.rm = T), 2), nsmall = 2)), "NMR"),
subtitle = "Total Payout (Resolved)",
color = "olive")
})
output$payout_pending <- renderValueBox({
valueBox(value = paste(as.character(format(round(sum(react_d_filter()[resolved == F, ]$payout, na.rm = T), 2), nsmall = 2)), "NMR"),
subtitle = "Total Payout (Pending)",
color = "yellow")
})
output$payout_total <- renderValueBox({
valueBox(value = paste(as.character(format(round(sum(react_d_filter()$payout, na.rm = T), 2), nsmall = 2)), "NMR"),
subtitle = "Total Payout (All)",
color = "light-blue")
})
# ============================================================================
# Reactive valueBox outputs: Average Round Payouts
# ============================================================================
output$payout_average_resolved <- renderValueBox({
# Use rounds with stake > 0 only
valueBox(value = paste(as.character(format(round(mean(react_d_payout_summary()[resolved == T & total_stake > 0, ]$net_payout, na.rm = T), 2), nsmall = 2)), "NMR"),
subtitle = "Avg. Round Payout (Resolved)",
color = "olive")
})
output$payout_average_pending <- renderValueBox({
# Use rounds with stake > 0 only
valueBox(value = paste(as.character(format(round(mean(react_d_payout_summary()[resolved == F & total_stake > 0, ]$net_payout, na.rm = T), 2), nsmall = 2)), "NMR"),
subtitle = "Avg. Round Payout (Pending)",
color = "yellow")
})
output$payout_average <- renderValueBox({
# Use rounds with stake > 0 only
valueBox(value = paste(as.character(format(round(mean(react_d_payout_summary()[total_stake > 0, ]$net_payout, na.rm = T), 2), nsmall = 2)), "NMR"),
subtitle = "Avg. Round Payout (All)",
color = "light-blue")
})
# ============================================================================
# Reactive valueBox outputs: Average Rate of Return
# ============================================================================
output$payout_avg_ror_resolved <- renderValueBox({
# Use rounds with stake > 0 only
valueBox(value = paste(as.character(format(round(mean(react_d_payout_summary()[resolved == T & total_stake > 0, ]$rate_of_return), 2), nsmall = 2)), "%"),
subtitle = "Avg. Round ROR (Resolved)",
color = "olive")
})
output$payout_avg_ror_pending <- renderValueBox({
# Use rounds with stake > 0 only
valueBox(value = paste(as.character(format(round(mean(react_d_payout_summary()[resolved == F & total_stake > 0, ]$rate_of_return), 2), nsmall = 2)), "%"),
subtitle = "Avg. Round ROR (Pending)",
color = "yellow")
})
output$payout_avg_ror <- renderValueBox({
# Use rounds with stake > 0 only
valueBox(value = paste(as.character(format(round(mean(react_d_payout_summary()[total_stake > 0, ]$rate_of_return), 2), nsmall = 2)), "%"),
subtitle = "Avg. Round ROR (All)",
color = "light-blue")
})
# ============================================================================
# Reactive: Payout Charts
# ============================================================================
# Net Payouts Bar Chart
output$plot_payout_net <- renderPlotly({
# Data
d_filter <- react_d_payout_summary()
# Filter
d_filter <- d_filter[total_stake > 0]
# Divider (resolved vs pending)
x_marker <- max(d_filter[resolved == TRUE]$round) + 0.5
y_marker <- max(d_filter$net_payout)
# ggplot
p <- ggplot(d_filter,
aes(x = round, y = net_payout, fill = net_payout,
text = paste("Round:", round,
"\nRound Open Date:", date_open,
"\nRound Resolved Date:", date_resolved,
"\nRound Resolved?:", resolved,
"\nPayout:", round(net_payout,2), "NMR"))) +
geom_bar(position = "stack", stat = "identity") +
theme(
panel.border = element_rect(fill = 'transparent',
color = "grey", linewidth = 0.25),
panel.background = element_rect(fill = 'transparent'),
plot.background = element_rect(fill = 'transparent', color = NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'transparent'),
strip.text = element_text(),
strip.clip = "on",
legend.background = element_rect(fill = 'transparent'),
legend.box.background = element_rect(fill = 'transparent')
) +
geom_vline(aes(xintercept = x_marker), linewidth = 0.25, color = "grey", linetype = "dashed") +
geom_hline(aes(yintercept = 0), linewidth = 0.25, color = "grey") +
annotate("text", x = x_marker, y = y_marker*1.2, label = "← Resolved vs. Pending →") +
scale_fill_scico(palette = "vikO", direction = -1, midpoint = 0) +
# scale_x_date(breaks = breaks_pretty(10),
# labels = label_date_short(format = c("%Y", "%b", "%d"), sep = "\n")
# ) +
xlab("\nTournament Round") +
ylab("Round Payout (NMR)")
# Generate plotly
ggplotly(p, height = 500, tooltip = "text")
})
# Stacked Bar Chart
output$plot_payout_stacked <- renderPlotly({
# Data
d_filter <- react_d_filter()
# Filter
d_filter <- d_filter[stake > 0]
# Divider (resolved vs pending)
x_marker <- max(d_filter[resolved == TRUE]$round) + 0.5
# ggplot
p <- ggplot(d_filter,
aes(x = round, y = payout, fill = payout,
text = paste("Model:", model,
"\nRound:", round,
"\nRound Open Date:", date_open,
"\nRound Resolved Date:", date_resolved,
"\nRound Resolved?:", resolved,
"\nPayout:", round(payout,2), "NMR"))) +
geom_bar(position = "stack", stat = "identity") +
theme(
panel.border = element_rect(fill = 'transparent',
color = "grey", linewidth = 0.25),
panel.background = element_rect(fill = 'transparent'),
plot.background = element_rect(fill = 'transparent', color = NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'transparent'),
strip.text = element_text(),
strip.clip = "on",
legend.background = element_rect(fill = 'transparent'),
legend.box.background = element_rect(fill = 'transparent')
) +
geom_vline(aes(xintercept = x_marker), linewidth = 0.25, color = "grey", linetype = "dashed") +
geom_hline(aes(yintercept = 0), linewidth = 0.25, color = "grey") +
scale_fill_scico(palette = "vikO", direction = -1, midpoint = 0) +
# scale_x_date(breaks = breaks_pretty(10),
# labels = label_date_short(format = c("%Y", "%b", "%d"), sep = "\n")
# ) +
xlab("\nTournament Round") +
ylab("Round Payout (NMR)")
# Generate plotly
ggplotly(p, height = 500, tooltip = "text")
})
# Individual
output$plot_payout_individual <- renderPlotly({
# Data
d_filter <- react_d_filter()
# Filter
d_filter <- d_filter[stake > 0]
# Get the number of unique models
n_model <- length(unique(d_filter$model))
# Base plot
p <- ggplot(d_filter,
aes(x = round, y = payout, fill = payout,
text = paste("Model:", model,
"\nRound:", round,
"\nRound Open Date:", date_open,
"\nRound Resolved Date:", date_resolved,
"\nRound Resolved:", resolved,
"\nPayout:", round(payout,2), "NMR"))) +
geom_bar(stat = "identity") +
theme(
panel.border = element_rect(fill = 'transparent', color = "grey", linewidth = 0.25),
panel.background = element_rect(fill = 'transparent'),
plot.background = element_rect(fill = 'transparent', color = NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'transparent'),
strip.text = element_text(),
strip.clip = "on",
legend.background = element_rect(fill = 'transparent'),
legend.box.background = element_rect(fill = 'transparent'),
axis.text.x = element_text(angle = 45, hjust = 1)
) +
geom_hline(aes(yintercept = 0), linewidth = 0.25, color = "grey") +
scale_fill_scico(palette = "vikO", direction = -1, midpoint = 0) +
scale_x_continuous(breaks = breaks_pretty(5)) +
xlab("\nTournament Round") +
ylab("Payout (NMR)")
# Facet setting
# if ((n_model %% 4) == 0) {
# p <- p + facet_wrap(. ~ model, ncol = 4, scales = "fixed")
# } else if ((n_model %% 5) == 0) {
# p <- p + facet_wrap(. ~ model, ncol = 5, scales = "fixed")
# } else {
# p <- p + facet_wrap(. ~ model, ncol = 6, scales = "fixed")
# }
p <- p + facet_wrap(. ~ model, ncol = 5, scales = "fixed") # fixed
# Dynamic height adjustment
height <- 500 # default minimum height
if (n_model >= 10) height = 800
if (n_model >= 15) height = 1000
if (n_model >= 20) height = 1200
if (n_model >= 25) height = 1400
if (n_model >= 30) height = 1600
if (n_model >= 35) height = 1800
if (n_model >= 40) height = 2000
if (n_model >= 45) height = 2200
if (n_model >= 50) height = 2400
if (n_model >= 55) height = 2600
if (n_model >= 60) height = 2800
if (n_model >= 65) height = 3000
# Generate plotly
ggplotly(p, height = height, tooltip = "text")
})
# KPI Chart: Avg Corr vs. Avg TC
output$plot_performance_avg <- renderPlotly({
# Data
d_pref <- react_d_performance_summary()
# Plot
p_avg <- ggplot(d_pref,
aes(x = avg_tc, y = avg_corrV2,
text = paste("Model:", model,
"\nAverage CORRv2:", round(avg_corrV2, 4),
"\nAverage TC:", round(avg_tc, 4))
)) +
geom_point() +
theme(
panel.border = element_rect(fill = 'transparent', color = "grey", linewidth = 0.25),
panel.background = element_rect(fill = 'transparent'),
plot.background = element_rect(fill = 'transparent', color = NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'transparent'),
strip.text = element_text(),
strip.clip = "on",
legend.background = element_rect(fill = 'transparent'),
legend.box.background = element_rect(fill = 'transparent'),
axis.text.x = element_text(angle = 45, hjust = 1)
) +
scale_x_continuous(breaks = breaks_pretty(5)) +
scale_y_continuous(breaks = breaks_pretty(5)) +
xlab("\nAverage TC") +
ylab("\nAverage CORRv2")
# Add vline and hline if needed
if (min(d_pref$avg_corrV2) <0) p_avg <- p_avg + geom_hline(aes(yintercept = 0), linewidth = 0.25, color = "grey", linetype = "dashed")
if (min(d_pref$avg_tc) <0) p_avg <- p_avg + geom_vline(aes(xintercept = 0), linewidth = 0.25, color = "grey", linetype = "dashed")
# Convert to Plotly
ggplotly(p_avg, tooltip = "text")
})
# KPI Chart: Corr Sharpe vs. TC Sharpe
output$plot_performance_sharpe <- renderPlotly({
# Data
d_pref <- react_d_performance_summary()
# Plot
p_sharpe <- ggplot(d_pref,
aes(x = sharpe_tc, y = sharpe_corrV2,
text = paste("Model:", model,
"\nSharpe Ratio of CORRv2:", round(sharpe_corrV2, 4),
"\nSharpe Ratio of TC:", round(sharpe_tc, 4))
)) +
geom_point() +
theme(
panel.border = element_rect(fill = 'transparent', color = "grey", linewidth = 0.25),
panel.background = element_rect(fill = 'transparent'),
plot.background = element_rect(fill = 'transparent', color = NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'transparent'),
strip.text = element_text(),
strip.clip = "on",
legend.background = element_rect(fill = 'transparent'),
legend.box.background = element_rect(fill = 'transparent'),
axis.text.x = element_text(angle = 45, hjust = 1)
) +
scale_x_continuous(breaks = breaks_pretty(5)) +
scale_y_continuous(breaks = breaks_pretty(5)) +
xlab("\nSharpe Ratio of TC") +
ylab("\nSharpe Ratio of CORRv2")
# Add vline and hline if needed
if (min(d_pref$sharpe_corrV2) <0) p_sharpe <- p_sharpe + geom_hline(aes(yintercept = 0), linewidth = 0.25, color = "grey", linetype = "dashed")
if (min(d_pref$sharpe_tc) <0) p_sharpe <- p_sharpe + geom_vline(aes(xintercept = 0), linewidth = 0.25, color = "grey", linetype = "dashed")
# Convert to Plotly
ggplotly(p_sharpe, tooltip = "text")
})
# ============================================================================
# Reactive: Payout Summary Table
# ============================================================================
# Net Round Payout Summary
output$dt_payout_summary <- DT::renderDT({
# Generate a new DT
DT::datatable(
# Data
react_d_payout_summary(),
# Other Options
rownames = FALSE,
extensions = "Buttons",
options =
list(
dom = 'Bflrtip', # https://datatables.net/reference/option/dom
buttons = list('csv', 'excel', 'copy', 'print'), # https://rstudio.github.io/DT/003-tabletools-buttons.html
order = list(list(0, 'asc'), list(1, 'asc')),
pageLength = 100,
lengthMenu = c(10, 50, 100, 500, 1000),
columnDefs = list(list(className = 'dt-center', targets = "_all")))
) |>
# Reformat individual columns
formatRound(columns = c("total_stake", "net_payout", "rate_of_return"), digits = 2) |>
formatStyle(columns = c("round"), fontWeight = "bold") |>
formatStyle(columns = c("resolved"),
target = "row",
backgroundColor = styleEqual(c(1,0), c("transparent", "#FFF8E1"))) |>
formatStyle(columns = c("total_stake"),
fontWeight = "bold",
color = styleInterval(cuts = -1e-15, values = c("#D24141", "#2196F3"))) |>
formatStyle(columns = c("net_payout"),
fontWeight = "bold",
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800"))) |>
formatStyle(columns = c("rate_of_return"),
fontWeight = "bold",
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800")))
})
# Individual Model Payout Summary
output$dt_model_payout_summary <- DT::renderDT({
# Generate a new DT
DT::datatable(
# Data
react_d_model_payout_summary(),
# Other Options
rownames = FALSE,
extensions = "Buttons",
options =
list(
dom = 'Bflrtip', # https://datatables.net/reference/option/dom
buttons = list('csv', 'excel', 'copy', 'print'), # https://rstudio.github.io/DT/003-tabletools-buttons.html
order = list(list(0, 'asc'), list(1, 'asc')),
pageLength = 100,
lengthMenu = c(10, 50, 100, 500, 1000),
columnDefs = list(list(className = 'dt-center', targets = "_all")))
) |>
# Reformat individual columns
formatRound(columns = c("net_payout", "avg_payout",
"avg_rate_of_return_percent",
"sharpe_rate_of_return"), digits = 4) |>
# formatStyle(columns = c("model"), fontWeight = "bold") |>
formatStyle(columns = c("net_payout", "avg_payout",
"avg_rate_of_return_percent",
"sharpe_rate_of_return"),
# fontWeight = "bold",
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800")))
})
# Payout Sim (Model)
output$dt_payout_sim_model <- DT::renderDT({
# Generate a new DT
DT::datatable(
# Data
react_d_payout_sim_model(),
# Other Options
rownames = FALSE,
extensions = "Buttons",
options =
list(
dom = 'Bflrtip', # https://datatables.net/reference/option/dom
buttons = list('csv', 'excel', 'copy', 'print'), # https://rstudio.github.io/DT/003-tabletools-buttons.html
order = list(list(0, 'asc'), list(1, 'asc')),
pageLength = 100,
lengthMenu = c(10, 50, 100, 500, 1000),
columnDefs = list(list(className = 'dt-center', targets = "_all")))
) |>
# Reformat individual columns
formatRound(columns = c("sum_pay_1C0T", "sum_pay_2C0T", "sum_pay_2C1T", "sum_pay_1C3T",
"shp_pay_1C0T", "shp_pay_2C0T", "shp_pay_2C1T", "shp_pay_1C3T"),
digits = 2) |>
formatStyle(columns = c("sum_pay_1C0T", "sum_pay_2C0T", "sum_pay_2C1T", "sum_pay_1C3T",
"shp_pay_1C0T", "shp_pay_2C0T", "shp_pay_2C1T", "shp_pay_1C3T"),
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800"))) |>
formatStyle(columns = c("model",
"sum_pay_2C1T", "sum_pay_1C3T",
"shp_pay_2C1T", "shp_pay_1C3T"
), fontWeight = "bold")
})
# Payout Sim (Overall)
output$dt_payout_sim_overall <- DT::renderDT({
# Generate a new DT
DT::datatable(
# Data
react_d_payout_sim_overall(),
# Other Options
rownames = FALSE,
# extensions = "Buttons",
options =
list(
dom = 't', # https://datatables.net/reference/option/dom
# buttons = list('csv', 'excel', 'copy', 'print'), # https://rstudio.github.io/DT/003-tabletools-buttons.html
# order = list(list(0, 'asc'), list(1, 'asc')),
# pageLength = 10,
# lengthMenu = c(10, 50, 100, 500, 1000),
columnDefs = list(list(className = 'dt-center', targets = "_all")))
) |>
# Reformat individual columns
formatRound(columns = c("sum_pay_1C0T", "sum_pay_2C0T", "sum_pay_2C1T", "sum_pay_1C3T",
"shp_pay_1C0T", "shp_pay_2C0T", "shp_pay_2C1T", "shp_pay_1C3T"),
digits = 2) |>
formatStyle(columns = c("sum_pay_1C0T", "sum_pay_2C0T", "sum_pay_2C1T", "sum_pay_1C3T",
"shp_pay_1C0T", "shp_pay_2C0T", "shp_pay_2C1T", "shp_pay_1C3T"),
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800"))) |>
formatStyle(columns = c("sum_pay_2C1T", "sum_pay_1C3T",
"shp_pay_2C1T", "shp_pay_1C3T"
), fontWeight = "bold")
})
# Performance Summary
output$dt_performance_summary <- DT::renderDT({
# Generate a new DT
DT::datatable(
# Data
react_d_performance_summary(),
# Other Options
rownames = FALSE,
extensions = "Buttons",
options =
list(
dom = 'Bflrtip', # https://datatables.net/reference/option/dom
buttons = list('csv', 'excel', 'copy', 'print'), # https://rstudio.github.io/DT/003-tabletools-buttons.html
order = list(list(0, 'asc'), list(1, 'asc')),
pageLength = 10,
lengthMenu = c(10, 50, 100, 500, 1000),
columnDefs = list(list(className = 'dt-center', targets = "_all")))
) |>
# Reformat individual columns
formatRound(columns = c("avg_corrV2", "sharpe_corrV2",
"avg_tc", "sharpe_tc",
"avg_2C1T", "sharpe_2C1T"
),
digits = 4) |>
formatStyle(columns = c("avg_corrV2", "sharpe_corrV2",
"avg_tc", "sharpe_tc",
"avg_2C1T", "sharpe_2C1T"
),
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800")))
# formatStyle(columns = c("model",
# "sum_pay_2C1T", "sum_pay_1C3T",
# "shp_pay_2C1T", "shp_pay_1C3T"
# ), fontWeight = "bold")
})
# ============================================================================
# Reactive: Model Performance Charts
# ============================================================================
# Boxplot - TC Percentile
output$plot_boxplot_tcp <- renderPlotly({
# Data
d_filter <- react_d_filter()
# Order by TC_PCT
d_model_order <- with(d_filter, reorder(model, tc_pct, median))
d_filter$model_order <- factor(d_filter$model, levels = levels(d_model_order))
# ggplot2
p <- ggplot(d_filter, aes(x = model_order, y = tc_pct, group = model_order, color = model_order)) +
geom_boxplot() +
theme(
panel.border = element_blank(),
panel.background = element_rect(fill = 'transparent'),
plot.background = element_rect(fill = 'transparent', color = NA),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color = "grey", linewidth = 0.25),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'transparent'),
strip.text = element_text(),
strip.clip = "on",
legend.position = "none"
) +
scale_color_manual(values = gen_custom_palette(d_filter$model)) +
xlab("Model") +
ylab("TC Percentile") +
scale_y_continuous(limits = c(0,100), breaks = breaks_pretty(4)) +
coord_flip()
# Dynamic height adjustment
n_model <- length(unique(d_filter$model))
height <- 600 # default
if (n_model > 10) height = 800
if (n_model > 15) height = 1000
if (n_model > 20) height = 1200
if (n_model > 25) height = 1400
if (n_model > 30) height = 1600
if (n_model > 35) height = 1800
if (n_model > 40) height = 2000
if (n_model > 45) height = 2200
if (n_model > 50) height = 2400
# Generate plotly
ggplotly(p, height = height)
})
# ============================================================================
# Reactive: Downloads
# ============================================================================
output$download_raw <- downloadHandler(
filename = "raw_data.csv",
content = function(file) {fwrite(react_d_raw(), file, row.names = FALSE)}
)
# ============================================================================
# Session Info
# ============================================================================
output$session_info <- renderPrint({
sessionInfo()
})
# ============================================================================
# Trick to keep session alive
# https://tickets.dominodatalab.com/hc/en-us/articles/360015932932-Increasing-the-timeout-for-Shiny-Server
# ============================================================================
output$keepAlive <- renderText({
req(input$count)
# paste("keep alive ", input$count)
" "
})
}
# ==============================================================================
# App
# ==============================================================================
shinyApp(ui, server)
|