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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)
library(googlesheets4)
# devtools::install_github("woobe/Rnumerai")
library(Rnumerai)
# Options
options(encoding = "UTF-8")
# Pre-download all usernames
options(timeout = max(1000, getOption("timeout")))
ls_username <- sort(get_leaderboard()$username)
# Prepare survey results data
if (TRUE) {
# Download
gs4_deauth()
d_survey_raw <- read_sheet(ss = "https://docs.google.com/spreadsheets/d/18AM4RkG5KiK3TlDGMx0z7X5Y5-eQE9kgLXM9ng_yXUk",
sheet = "Form responses 1") %>% data.table()
# Rename
colnames(d_survey_raw) <- c("timestamp", "country", "comments")
# Summarise
d_survey_summary <-
d_survey_raw %>%
lazy_dt() %>%
group_by(country) %>%
summarise(count = n()) %>%
arrange(desc(count), country) %>%
as.data.table()
}
# ==============================================================================
# Helper Functions
# ==============================================================================
# Download raw data
download_raw_data <- function(model_name) {
# Download data from Numerai
d_raw <- round_model_performances(model_name)
# Remove rows without CORR
d_raw <- d_raw[!is.na(d_raw$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",
"mmc", "mmcPercentile",
"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)]
d_munged[, mmcPercentile := round(mmcPercentile * 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",
"mmc", "mmc_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 = ls_username, # Replace this with your own models if needed
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: V43_LGBM_CYRUS20",
`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",
# First Page - All KPIs
tabPanel("KPI (All)",
br(),
h3(strong(textOutput(outputId = "text_performance_chart"))),
h4(textOutput(outputId = "text_performance_chart_note")),
br(),
# Controls
fluidRow(
column(6,
markdown("#### **Pick ONE of the KPIs:**"),
pickerInput(
inputId = "kpi_choice",
choices = c("MMCv2: The Latest and the Greatest MMC",
"CORRv2: CORRelation with target cyrus_v4_20",
"TC: True Contribtuion to the hedge fund's returns",
"FNCv3: Feature Neutral Correlation with respect to the FNCv3 features",
# "CORJ60: CORRelation with target Jerome_v4_60", # add this later
"Percentile: MMCv2",
"Percentile: CORRv2",
"Percentile: TC",
"Percentile: FNCv3",
"CWMM: Correlation With the Meta Model",
"MCWNM: Maximum Correlation With Numerai Models staked at least 10 NMR",
"APCWNM: Average Pairwise Correlation With Numerai Models staked at least 10 NMR",
"Score Multipliers: 0.5 x CORRv2 + 2.0 x MMCv2",
"Score Multipliers: 0.5 x CORRv2",
"Score Multipliers: 1.5 x CORRv2",
"Score Multipliers: 2.0 x CORRv2",
"Score Multipliers: 2.0 x CORRv2 + 0.5 x TC",
"Score Multipliers: 2.0 x CORRv2 + 1.0 x TC",
"Payout",
"Rate of Return (%): Payout / Stake x 100"),
multiple = FALSE,
width = "95%")
),
column(2,
markdown("#### **Cumulative**"),
switchInput(
inputId = "kpi_cumulative",
onLabel = "Yes",
offLabel = "No",
value = TRUE)
),
column(2,
markdown("#### **Hide Pending**"),
switchInput(
inputId = "kpi_hide_pending",
onLabel = "Yes",
offLabel = "No",
value = FALSE)
),
column(2,
markdown("#### **Facet**"),
switchInput(
inputId = "kpi_facet",
onLabel = "Yes",
offLabel = "No",
value = FALSE)
)
),
h4(strong(textOutput(outputId = "text_performance_chart_data"))),
br(),
DTOutput("dt_kpi"),
br(),
br(),
h4(strong(textOutput(outputId = "text_performance_chart_title"))),
fluidRow(column(12, plotlyOutput("plot_kpi"))),
br()
),
# tabPanel("KPI (C&T)",
#
# 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()
#
# ), # End of KPI (C&T)
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 Table (Rounds)",
br(),
h3(strong(textOutput(outputId = "text_payout_rnd"))),
br(),
DTOutput("dt_payout_summary"),
br()
),
tabPanel("Payout Table (Models)",
br(),
h3(strong(textOutput(outputId = "text_payout_ind"))),
br(),
DTOutput("dt_model_payout_summary"),
br()
),
# tabPanel("Payout (Sim)",
#
# br(),
#
# h3(strong(textOutput(outputId = "text_payout_sim"))),
#
# br(),
#
# # markdown(""),
# markdown(""),
#
#
# br(),
#
# markdown("#### **Notes**:
#
# - **sum_pay**: Sum of Payouts
# - **shp_pay**: Sharpe Ratio of Payouts
# - **1C3T**: 1xCORRv2 + 3xTC (Original Degen Mode)
# - **1C0T**: 1xCORRv2 + 0xTC (Until the End of 2023)
# - **2C0T**: 2xCORRv2 + 0xTC (Until the End of 2023)
# - **2C1T**: 2xCORRv2 + 1xTC (Until the End of 2023)
# - **05C2M**: 0.5xCORRv2 + 2xMMCv2 (**New Payout 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()
#
# ), # End of Payout Sim
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()
)
# tabPanel("KPI (x~y)",
#
# br(),
#
# markdown(""),
#
# br(),
#
# fluidRow(
#
# column(6,
# markdown("#### **X-Axis:**"),
# pickerInput(
# inputId = "kpi_xy_x",
# choices = c("CORRv2: CORRelation with target cyrus_v4_20",
# "MMCv2: The Latest and the Greatest MMC",
# "TC: True Contribtuion to the hedge fund's returns",
# "FNCv3: Feature Neutral Correlation with respect to the FNCv3 features",
# "Percentile: MMCv2",
# "Percentile: CORRv2",
# "Percentile: TC",
# "Percentile: FNCv3",
# "CWMM: Correlation With the Meta Model",
# "MCWNM: Maximum Correlation With Numerai Models staked at least 10 NMR",
# "APCWNM: Average Pairwise Correlation With Numerai Models staked at least 10 NMR"),
#
# multiple = FALSE,
# width = "95%")
# ),
#
# column(6,
# markdown("#### **Y-Axis:**"),
# pickerInput(
# inputId = "kpi_xy_y",
# choices = c("MMCv2: The Latest and the Greatest MMC",
# "CORRv2: CORRelation with target cyrus_v4_20",
# "TC: True Contribtuion to the hedge fund's returns",
# "FNCv3: Feature Neutral Correlation with respect to the FNCv3 features",
# "Percentile: MMCv2",
# "Percentile: CORRv2",
# "Percentile: TC",
# "Percentile: FNCv3",
# "CWMM: Correlation With the Meta Model",
# "MCWNM: Maximum Correlation With Numerai Models staked at least 10 NMR",
# "APCWNM: Average Pairwise Correlation With Numerai Models staked at least 10 NMR"),
#
# multiple = FALSE,
# width = "95%")
# ),
#
# column(2,
# markdown("#### **Control 1**"),
# switchInput(
# inputId = "kpi_xy_ctrl_1",
# onLabel = "Yes",
# offLabel = "No",
# value = TRUE)
# ),
#
# column(2,
# markdown("#### **Control 2**"),
# switchInput(
# inputId = "kpi_xy_ctrl_2",
# onLabel = "Yes",
# offLabel = "No",
# value = FALSE)
# ),
#
# column(2,
# markdown("#### **Control 3**"),
# switchInput(
# inputId = "kpi_xy_ctrl_3",
# onLabel = "Yes",
# offLabel = "No",
# value = FALSE)
# ),
#
# column(2,
# markdown("#### **Control 4**"),
# switchInput(
# inputId = "kpi_xy_ctrl_4",
# onLabel = "Yes",
# offLabel = "No",
# value = FALSE)
# ),
#
# column(2,
# markdown("#### **Control 5**"),
# switchInput(
# inputId = "kpi_xy_ctrl_5",
# onLabel = "Yes",
# offLabel = "No",
# value = FALSE)
# ),
#
# column(2,
# markdown("#### **Control 6**"),
# switchInput(
# inputId = "kpi_xy_ctrl_6",
# onLabel = "Yes",
# offLabel = "No",
# value = FALSE)
# )
#
#
# ),
#
# br()
#
# ) # end of KPI (x~y)
) # 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",
fluidPage(
markdown("# **Numerai Coummunity**"),
br(),
tabsetPanel(type = "tabs",
# First Page - About
tabPanel("About",
markdown("## **Around the World with Numeratis**"),
markdown("### **Overview**
NumerCon (2022) and the time I spent with my fellow Numeratis were the exact “booster jab” I needed to wake the “meetup organizer Joe” up
after two crazy years of COVID and hibernation in a man cave. After NumerCon, I am confident that
1) many countries are now ready for in-person tech events and
2) we need to bring this fantastic in-person experience to more Numeratis around the world.
So, fam, here is my [proposal](https://forum.numer.ai/t/proposal-around-the-world-with-numeratis) for **Global Numerai Community Meetups**.
"),
markdown("### **Goals**
- **Demystifying Numerai** - many top data scientists out there are just not too sure about the crypto elements and/or the tournament format. Speaking with and listening to long-term participants can be an effective way to build trust. This was exactly how I learned to trust Numerai after watching Jon’s Office Hours with Arbitrage.
- **Knowledge sharing and brainstorming** - although we may not share all our secret sauce, meetups are great opportunities to bounce ideas off each other and may lead to new models with crazily high TC.
- **Face-to-face discussions between Numeratis and Numerai team** - a lot more than just asking the team “wen scores” in person, these events can provide a quick and direct feedback loop for both participants and the Numerai team.
"),
markdown("### **What's Here?**
- Materials (slides and videos) from previous events
- Survey results for upcoming events
- Memes and (de)GenAI stuff from the community
### **Enjoy :)**
")
),
# Second Page - Materials
tabPanel("Materials",
fluidRow(
column(10,
htmltools::includeMarkdown('https://raw.githubusercontent.com/councilofelders/meetups/master/README.md')
))
),
# Third Page - Survey Results
tabPanel("Survey Results",
markdown("## **Survey for Upcoming Events**"),
br(),
markdown("Hello everyone!
We’re reaching out to gather **anonymous data** on your location, which will greatly assist us in planning future Numerai community events.
Your support in providing this information would be much appreciated.
You can find the latest survey results below.
Here is the [**Google Form**](https://forms.gle/1USUa7YCn2EgZG3NA). Please share it with your fellow Numeratis.
Thank you!
"),
fluidRow(
column(4,
markdown("### **Summary**"),
br(),
DTOutput("dt_survey_summary")
),
column(8,
markdown("### **Raw Data**"),
br(),
DTOutput("dt_survey_raw"))
)
),
# 4th Page - Memes
tabPanel("Memes",
markdown("")
)
) # end of tabsetPanel
) # end of fluidPage
),
# ========================================================================
# 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("## **Source Code**"),
markdown("- #### GitHub: https://github.com/woobe/shiny-numerati"),
markdown("- #### Hugging Face: https://huggingface.co./spaces/jofaichow/shiny-numerati/tree/main"),
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
- #### **0.2.1** — Added `KPI (All)`
- #### **0.2.2** — Sped up chart rendering with `toWebGL()`
- #### **0.2.3** — Added new `MMC` - Ref: https://forum.numer.ai/t/changing-scoring-payouts-again-to-mmc-only
- #### **0.2.4** — Added `MMC` to `Payout Sim`
- #### **0.2.5** — Added more features related to MMC
- #### **0.2.6** — Added survey results - Ref: https://forum.numer.ai/t/around-the-world-with-numeratis-survey-for-upcoming-events
- #### **0.2.7** — Removed `KPI (C&T)` and `Payout Simulation`
"),
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 0.2.7"))
)
# ==============================================================================
# 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)})
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
}
)
output$dt_model <- DT::renderDT({
# Raw Data
d_raw <- react_d_raw()
# Reformat
d_munged <- reformat_data(d_raw)
# Main DT
DT::datatable(
# Data
d_munged,
# Other Options
rownames = FALSE,
extensions = "Buttons",
options =
list(
dom = 'Bflrtip', # https://datatables.net/reference/option/dom
buttons = list('csv', 'excel', 'copy', 'print'), # https://rstudio.github.io/DT/003-tabletools-buttons.html
order = list(list(0, 'asc'), list(1, 'asc')),
pageLength = 10,
lengthMenu = c(10, 20, 100, 500, 1000, 50000),
columnDefs = list(list(className = 'dt-center', targets = "_all")))
) |>
# Reformat individual columns
formatRound(columns = c("corrV2", "tc", "fncV3", "corr_meta", "pay_ftr", "mmc"), digits = 4) |>
formatRound(columns = c("apcwnm", "mcwnm"), digits = 4) |>
formatRound(columns = c("corrV2_pct", "tc_pct", "fncV3_pct", "mmc_pct"), digits = 1) |>
formatRound(columns = c("stake", "payout"), digits = 2) |>
formatStyle(columns = c("model"),
fontWeight = "bold") |>
formatStyle(columns = c("stake"),
fontWeight = "bold",
color = styleInterval(cuts = -1e-15, values = c("#D24141", "#2196F3"))) |>
formatStyle(columns = c("corrV2", "fncV3", "mmc"),
color = styleInterval(cuts = -1e-15, values = c("#D24141", "black"))) |>
formatStyle(columns = c("tc"),
color = styleInterval(cuts = -1e-15, values = c("#D24141", "#A278DC"))) |>
formatStyle(columns = c("corrV2_pct", "tc_pct", "fncV3_pct", "mmc_pct"),
color = styleInterval(cuts = c(1, 5, 15, 85, 95, 99),
values = c("#692020", "#9A2F2F", "#D24141",
"#D1D1D1", # light grey
"#00A800", "#007000", "#003700"))) |>
formatStyle(columns = c("payout"),
fontWeight = "bold",
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800")))
})
# ============================================================================
# Static: Survey Data
# ============================================================================
output$dt_survey_summary <- DT::renderDT({
# Return
DT::datatable(
# Data
d_survey_summary,
# Other Options
rownames = FALSE,
# extensions = "Buttons",
options =
list(
dom = 'Blrtip', # https://datatables.net/reference/option/dom
pageLength = 20,
lengthMenu = c(10, 20, 100, 500)
)
)
})
output$dt_survey_raw <- DT::renderDT({
# Return
DT::datatable(
# Data
d_survey_raw,
# Other Options
rownames = FALSE,
# extensions = "Buttons",
options =
list(
dom = 'Blrtip', # https://datatables.net/reference/option/dom
pageLength = 20,
lengthMenu = c(10, 20, 100, 500)
)
)
})
# ============================================================================
# Reactive: Text Printout
# ============================================================================
output$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 " "
})
# ============================================================================
# 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$button_filter,
{
# Get filtered data
# d_smry <- as.data.table(react_d_filter() |> filter(pay_ftr > 0))
d_payout <-
react_d_filter() |>
lazy_dt() |>
filter(pay_ftr > 0) |>
filter(stake > 0) |>
as.data.table()
# Apply clip to corrV2
d_payout[, corrV2_final := corrV2]
d_payout[corrV2 > 0.25, corrV2_final := 0.25]
d_payout[corrV2 < -0.25, corrV2_final := -0.25]
# Apply clip to tc
d_payout[, tc_final := tc]
d_payout[tc > 0.25, tc_final := 0.25]
d_payout[tc < -0.25, tc_final := -0.25]
# Calculate different payout
d_payout[, payout_1C0T := (corrV2_final) * stake * pay_ftr]
d_payout[, payout_2C0T := (2*corrV2_final) * stake * pay_ftr]
d_payout[, payout_2C1T := (2*corrV2_final + tc_final) * stake * pay_ftr]
d_payout[, payout_1C3T := (corrV2_final + 3*tc_final) * stake * pay_ftr]
d_payout[, payout_05C2M := (0.5*corrV2_final + 2*mmc) * stake * pay_ftr]
# Summarise
d_payout_smry <-
d_payout |>
lazy_dt() |>
group_by(model) |>
summarise(
rounds = n(),
sum_pay_1C0T = sum(payout_1C0T, na.rm = T),
sum_pay_2C0T = sum(payout_2C0T, na.rm = T),
sum_pay_2C1T = sum(payout_2C1T, na.rm = T),
sum_pay_1C3T = sum(payout_1C3T, na.rm = T),
sum_pay_05C2M = sum(payout_05C2M, na.rm = T),
shp_pay_1C0T = mean(payout_1C0T, na.rm = T) / sd(payout_1C0T, na.rm = T),
shp_pay_2C0T = mean(payout_2C0T, na.rm = T) / sd(payout_2C0T, na.rm = T),
shp_pay_2C1T = mean(payout_2C1T, na.rm = T) / sd(payout_2C1T, na.rm = T),
shp_pay_1C3T = mean(payout_1C3T, na.rm = T) / sd(payout_1C3T, na.rm = T),
shp_pay_05C2M = mean(payout_05C2M, na.rm = T) / sd(payout_05C2M, na.rm = T)
) |>
as.data.table()
# Return
d_payout_smry
})
react_d_payout_sim_overall <- eventReactive(
input$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]
d_payout[, payout_05C2M := (0.5*corrV2 + 2*mmc) * 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),
sum_pay_05C2M = sum(payout_05C2M, 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),
shp_pay_05C2M = mean(payout_05C2M, na.rm = T) / sd(payout_05C2M, na.rm = T)
) |>
as.data.table()
# Return
d_payout_smry
})
react_d_performance_summary <- eventReactive(
input$button_filter,
{
# Get filtered data
d_pref <- as.data.table(react_d_filter())
# Add 2xCORRv2 + 1xTC
d_pref[, twoC_oneT := 2*corrV2 + tc]
# Calculate some high level stats
d_pref <-
d_pref |>
lazy_dt() |>
group_by(model) |>
summarise(total_rounds = n(),
avg_corrV2 = mean(corrV2, na.rm = T),
sharpe_corrV2 = mean(corrV2, na.rm = T) / sd(corrV2, na.rm = T),
# mdd_corrV2 = maxdrawdown(corrV2),
avg_tc = mean(tc, na.rm = T),
sharpe_tc = mean(tc, na.rm = T) / sd(tc, na.rm = T),
# mdd_tc = maxdrawdown(tc),
avg_2C1T = mean(twoC_oneT, na.rm = T),
sharpe_2C1T = mean(twoC_oneT, na.rm = T) / sd(tc, na.rm = T)
# mdd_2C1T = maxdrawdown(twoC_oneT)
) |> as.data.table()
# Return
d_pref
})
react_d_kpi <- eventReactive(
input$button_filter,
{
# Get filtered data
d_pref <- as.data.table(react_d_filter())
# Hide Pending?
if (input$kpi_hide_pending) d_pref <- d_pref[resolved == TRUE]
# Add Rate of Return
d_pref[stake >0, rate_of_return := payout / stake * 100]
# Extract Raw KPI
if (input$kpi_choice == "MMCv2: The Latest and the Greatest MMC") d_pref[, KPI := mmc]
if (input$kpi_choice == "CORRv2: CORRelation with target cyrus_v4_20") d_pref[, KPI := corrV2]
if (input$kpi_choice == "TC: True Contribtuion to the hedge fund's returns") d_pref[, KPI := tc]
if (input$kpi_choice == "FNCv3: Feature Neutral Correlation with respect to the FNCv3 features") d_pref[, KPI := fncV3]
if (input$kpi_choice == "CWMM: Correlation With the Meta Model") d_pref[, KPI := corr_meta]
if (input$kpi_choice == "MCWNM: Maximum Correlation With Numerai Models staked at least 10 NMR") d_pref[, KPI := mcwnm]
if (input$kpi_choice == "APCWNM: Average Pairwise Correlation With Numerai Models staked at least 10 NMR") d_pref[, KPI := apcwnm]
# Calculate Score Multiplies
if (input$kpi_choice == "Score Multipliers: 0.5 x CORRv2 + 2.0 x MMCv2") d_pref[, KPI := 0.5 * corrV2 + 2.0 * mmc]
if (input$kpi_choice == "Score Multipliers: 0.5 x CORRv2") d_pref[, KPI := 0.5 * corrV2]
if (input$kpi_choice == "Score Multipliers: 1.5 x CORRv2") d_pref[, KPI := 1.5 * corrV2]
if (input$kpi_choice == "Score Multipliers: 2.0 x CORRv2") d_pref[, KPI := 2.0 * corrV2]
if (input$kpi_choice == "Score Multipliers: 2.0 x CORRv2 + 0.5 x TC") d_pref[, KPI := 2.0 * corrV2 + 0.5 * tc]
if (input$kpi_choice == "Score Multipliers: 2.0 x CORRv2 + 1.0 x TC") d_pref[, KPI := 2.0 * corrV2 + 1.0 * tc]
# Extract Percentile
if (input$kpi_choice == "Percentile: MMCv2") d_pref[, KPI := mmc_pct]
if (input$kpi_choice == "Percentile: CORRv2") d_pref[, KPI := corrV2_pct]
if (input$kpi_choice == "Percentile: TC") d_pref[, KPI := tc_pct]
if (input$kpi_choice == "Percentile: FNCv3") d_pref[, KPI := fncV3_pct]
# Extract Payout info
if (input$kpi_choice == "Payout") d_pref[, KPI := payout]
if (input$kpi_choice == "Rate of Return (%): Payout / Stake x 100") d_pref[, KPI := rate_of_return]
# Remove rows with NA (quick hack for now)
d_pref <- d_pref[!is.na(KPI)]
# Calculate Cumulative KPI (if needed)
if (input$kpi_cumulative) {
# Sort before doing cumsum
setorderv(d_pref, c("model", "round"))
# The data.table way
d_pref[, KPI := cumsum(KPI), "model"]
}
# Trim and sort
d_trim <- d_pref[, c("model", "round", "date_resolved", "resolved", "KPI")]
setorderv(d_trim, c("model", "round"))
# Return
d_trim
})
# ============================================================================
# Reactive: Payout Value Boxes
# ============================================================================
output$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 (CORRv2 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 Analysis (Model Comparison)" else " "
})
output$text_performance_chart_note <- renderText({
if (nrow(react_d_filter()) >= 1) "NOTE: Remember to refresh the chart (Step 5 ↑) after making any changes." else " "
})
output$text_performance_chart_title <- renderText({
if (nrow(react_d_filter()) >= 1) "KPI Chart (Remember to Click the 'Refresh' Button ↑↑↑)" else " "
})
output$text_performance_chart_data <- renderText({
if (nrow(react_d_filter()) >= 1) "KPI Data" else " "
})
# ============================================================================
# Reactive valueBox outputs: Rounds
# ============================================================================
output$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$net_payout)
# ggplot
p <- ggplot(d_filter,
aes(x = round, y = net_payout, fill = net_payout,
text = paste("Round:", round,
"\nRound Open Date:", date_open,
"\nRound Resolved Date:", date_resolved,
"\nRound Resolved?:", resolved,
"\nPayout:", round(net_payout,2), "NMR"))) +
geom_bar(position = "stack", stat = "identity") +
theme(
panel.border = element_rect(fill = 'transparent',
color = "grey", linewidth = 0.25),
panel.background = element_rect(fill = 'transparent'),
plot.background = element_rect(fill = 'transparent', color = NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'transparent'),
strip.text = element_text(),
strip.clip = "on",
legend.background = element_rect(fill = 'transparent'),
legend.box.background = element_rect(fill = 'transparent')
) +
geom_vline(aes(xintercept = x_marker), linewidth = 0.25, color = "grey", linetype = "dashed") +
geom_hline(aes(yintercept = 0), linewidth = 0.25, color = "grey") +
annotate("text", x = x_marker, y = y_marker*1.2, label = "← Resolved vs. Pending →") +
scale_fill_scico(palette = "vikO", direction = -1, midpoint = 0) +
# scale_x_date(breaks = breaks_pretty(10),
# labels = label_date_short(format = c("%Y", "%b", "%d"), sep = "\n")
# ) +
xlab("\nTournament Round") +
ylab("Round Payout (NMR)")
# Generate plotly
ggplotly(p, height = 500, tooltip = "text") |> toWebGL()
})
# Stacked Bar Chart
output$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]$round) + 0.5
# ggplot
p <- ggplot(d_filter,
aes(x = round, y = payout, fill = payout,
text = paste("Model:", model,
"\nRound:", round,
"\nRound Open Date:", date_open,
"\nRound Resolved Date:", date_resolved,
"\nRound Resolved?:", resolved,
"\nPayout:", round(payout,2), "NMR"))) +
geom_bar(position = "stack", stat = "identity") +
theme(
panel.border = element_rect(fill = 'transparent',
color = "grey", linewidth = 0.25),
panel.background = element_rect(fill = 'transparent'),
plot.background = element_rect(fill = 'transparent', color = NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill = 'transparent'),
strip.text = element_text(),
strip.clip = "on",
legend.background = element_rect(fill = 'transparent'),
legend.box.background = element_rect(fill = 'transparent')
) +
geom_vline(aes(xintercept = x_marker), linewidth = 0.25, color = "grey", linetype = "dashed") +
geom_hline(aes(yintercept = 0), linewidth = 0.25, color = "grey") +
scale_fill_scico(palette = "vikO", direction = -1, midpoint = 0) +
# scale_x_date(breaks = breaks_pretty(10),
# labels = label_date_short(format = c("%Y", "%b", "%d"), sep = "\n")
# ) +
xlab("\nTournament Round") +
ylab("Round Payout (NMR)")
# Generate plotly
ggplotly(p, height = 500, tooltip = "text") |> toWebGL()
})
# Individual
output$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") |> toWebGL()
})
# 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") |> toWebGL()
})
# 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$sharpe_tc) <0) p_sharpe <- p_sharpe + geom_vline(aes(xintercept = 0), linewidth = 0.25, color = "grey", linetype = "dashed")
# Convert to Plotly
ggplotly(p_sharpe, tooltip = "text") |> toWebGL()
})
# KPI Chart: All KPIs
output$plot_kpi <- renderPlotly({
# Data
d_kpi <- react_d_kpi()
# Dynamic Labels
if (input$kpi_choice == "MMCv2: The Latest and the Greatest MMC") y_label <- "mmc"
if (input$kpi_choice == "CORRv2: CORRelation with target cyrus_v4_20") y_label <- "CORRv2"
if (input$kpi_choice == "TC: True Contribtuion to the hedge fund's returns") y_label <- "TC"
if (input$kpi_choice == "FNCv3: Feature Neutral Correlation with respect to the FNCv3 features") y_label <- "FNCv3"
if (input$kpi_choice == "CWMM: Correlation With the Meta Model") y_label <- "CWMM"
if (input$kpi_choice == "MCWNM: Maximum Correlation With Numerai Models staked at least 10 NMR") y_label <- "MCWNM"
if (input$kpi_choice == "APCWNM: Average Pairwise Correlation With Numerai Models staked at least 10 NMR") y_label <- "APCWNM"
if (input$kpi_choice == "Score Multipliers: 0.5 x CORRv2 + 2.0 x MMCv2") y_label <- "0.5 x CORRv2 + 2.0 x MMCv2"
if (input$kpi_choice == "Score Multipliers: 0.5 x CORRv2") y_label <- "0.5 x CORRv2"
if (input$kpi_choice == "Score Multipliers: 1.5 x CORRv2") y_label <- "1.5 x CORRv2"
if (input$kpi_choice == "Score Multipliers: 2.0 x CORRv2") y_label <- "2.0 x CORRv2"
if (input$kpi_choice == "Score Multipliers: 2.0 x CORRv2 + 0.5 x TC") y_label <- "2.0 x CORRv2 + 0.5 x TC"
if (input$kpi_choice == "Score Multipliers: 2.0 x CORRv2 + 1.0 x TC") y_label <- "2.0 x CORRv2 + 1.0 x TC"
if (input$kpi_choice == "Percentile: MMCv2") y_label <- "MMCv2 Percentile"
if (input$kpi_choice == "Percentile: CORRv2") y_label <- "CORRv2 Percentile"
if (input$kpi_choice == "Percentile: TC") y_label <- "TC Percentile"
if (input$kpi_choice == "Percentile: FNCv3") y_label <- "FNCv3 Percentile"
if (input$kpi_choice == "Payout") y_label <- "Payout (NMR)"
if (input$kpi_choice == "Rate of Return (%): Payout / Stake x 100") y_label <- "Rate of Return (%)"
# If cumulative
if (input$kpi_cumulative) y_label <- paste("Cumulative", y_label)
# Other settings
y_min <- min(d_kpi$KPI) * 0.95
if (y_min > 0) y_min <- 0
y_max <- max(d_kpi$KPI) * 1.05
height <- 500 # default minimum height
# Plot
p <- ggplot(d_kpi, aes(x = round, y = KPI,
ymin = y_min, ymax = y_max,
color = model)) +
geom_line() +
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)) +
geom_hline(aes(yintercept = 0), linewidth = 0.25, color = "grey", linetype = "dashed") +
ylab(y_label) +
xlab("\nTournament Round")
# Facet wrap?
if (input$kpi_facet) {
# Extract no. of models
n_model <- length(unique(d_kpi$model))
# Add facet_wrap
p <- p + facet_wrap(. ~ model, ncol = 5, scales = "fixed") # fixed
# Dynamic height adjustment
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
}
# Convert to Plotly
ggplotly(p, height = height) |> toWebGL()
})
# ============================================================================
# Reactive: Payout Summary Table
# ============================================================================
# Net Round Payout Summary
output$dt_payout_summary <- DT::renderDT({
# Generate a new DT
DT::datatable(
# Data
react_d_payout_summary(),
# Other Options
rownames = FALSE,
extensions = "Buttons",
options =
list(
dom = 'Bflrtip', # https://datatables.net/reference/option/dom
buttons = list('csv', 'excel', 'copy', 'print'), # https://rstudio.github.io/DT/003-tabletools-buttons.html
order = list(list(0, 'asc'), list(1, 'asc')),
pageLength = 1000,
lengthMenu = c(10, 50, 100, 500, 1000, 10000),
columnDefs = list(list(className = 'dt-center', targets = "_all")))
) |>
# Reformat individual columns
formatRound(columns = c("total_stake", "net_payout", "rate_of_return"), digits = 2) |>
formatStyle(columns = c("round"), fontWeight = "bold") |>
formatStyle(columns = c("resolved"),
target = "row",
backgroundColor = styleEqual(c(1,0), c("transparent", "#FFF8E1"))) |>
formatStyle(columns = c("total_stake"),
fontWeight = "bold",
color = styleInterval(cuts = -1e-15, values = c("#D24141", "#2196F3"))) |>
formatStyle(columns = c("net_payout"),
fontWeight = "bold",
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800"))) |>
formatStyle(columns = c("rate_of_return"),
fontWeight = "bold",
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800")))
})
# Individual Model Payout Summary
output$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$dt_payout_sim_model <- DT::renderDT({
# Generate a new DT
DT::datatable(
# Data
react_d_payout_sim_model(),
# Other Options
rownames = FALSE,
extensions = "Buttons",
options =
list(
dom = 'Bflrtip', # https://datatables.net/reference/option/dom
buttons = list('csv', 'excel', 'copy', 'print'), # https://rstudio.github.io/DT/003-tabletools-buttons.html
order = list(list(0, 'asc'), list(1, 'asc')),
pageLength = 100,
lengthMenu = c(10, 50, 100, 500, 1000),
columnDefs = list(list(className = 'dt-center', targets = "_all")))
) |>
# Reformat individual columns
formatRound(columns = c("sum_pay_1C0T", "sum_pay_2C0T", "sum_pay_2C1T", "sum_pay_1C3T",
"sum_pay_05C2M", "shp_pay_05C2M",
"shp_pay_1C0T", "shp_pay_2C0T", "shp_pay_2C1T", "shp_pay_1C3T"),
digits = 2) |>
formatStyle(columns = c("sum_pay_1C0T", "sum_pay_2C0T", "sum_pay_2C1T", "sum_pay_1C3T",
"sum_pay_05C2M", "shp_pay_05C2M",
"shp_pay_1C0T", "shp_pay_2C0T", "shp_pay_2C1T", "shp_pay_1C3T"),
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800"))) |>
formatStyle(columns = c("model",
"sum_pay_05C2M", "shp_pay_05C2M"
# "sum_pay_2C1T", "sum_pay_1C3T",
# "shp_pay_2C1T", "shp_pay_1C3T"
), fontWeight = "bold")
})
# Payout Sim (Overall)
output$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",
"sum_pay_05C2M", "shp_pay_05C2M",
"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",
"sum_pay_05C2M", "shp_pay_05C2M",
"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_05C2M", "shp_pay_05C2M"),
fontWeight = "bold")
})
# Performance Summary
output$dt_performance_summary <- DT::renderDT({
# Generate a new DT
DT::datatable(
# Data
react_d_performance_summary(),
# Other Options
rownames = FALSE,
extensions = "Buttons",
options =
list(
dom = 'Bflrtip', # https://datatables.net/reference/option/dom
buttons = list('csv', 'excel', 'copy', 'print'), # https://rstudio.github.io/DT/003-tabletools-buttons.html
order = list(list(0, 'asc'), list(1, 'asc')),
pageLength = 10,
lengthMenu = c(10, 50, 100, 500, 1000),
columnDefs = list(list(className = 'dt-center', targets = "_all")))
) |>
# Reformat individual columns
formatRound(columns = c("avg_corrV2", "sharpe_corrV2",
"avg_tc", "sharpe_tc",
"avg_2C1T", "sharpe_2C1T"
),
digits = 4) |>
formatStyle(columns = c("avg_corrV2", "sharpe_corrV2",
"avg_tc", "sharpe_tc",
"avg_2C1T", "sharpe_2C1T"
),
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800")))
})
# KPI Filter
output$dt_kpi <- DT::renderDT({
# Generate a new DT
DT::datatable(
# Data
react_d_kpi(),
# 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 = 5,
lengthMenu = c(5, 10, 50, 100, 500, 1000),
columnDefs = list(list(className = 'dt-center', targets = "_all")))
) |>
# Reformat individual columns
formatRound(columns = c("KPI"), digits = 4) |>
formatStyle(columns = c("KPI"),
color = styleInterval(cuts = c(-1e-15, 1e-15),
values = c("#D24141", "#D1D1D1", "#00A800")))
})
# ============================================================================
# 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)
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