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$head( HTML( " " ) ), # ============================================================== # First Page # ============================================================== markdown("# **Shiny Numerati**"), markdown("### Community Dashboard for the Numerai Classic Tournament"), br(), fluidRow( column(6, markdown("## **Step 1: Select Your Models**"), markdown("### First, click this ⬇"), pickerInput(inputId = "model", label = " ", # choices = sort(Rnumerai::get_leaderboard()$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: Define the Range**"), 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("KPIs (Models)", 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() ), tabPanel("KPI Charts", br(), h3(strong(textOutput(outputId = "text_performance_chart"))), br(), fluidRow( column(4, markdown("## **CORR Multiper**"), sliderInput(inputId = "multiper_corr", label = "Numerai Classic Tournament Rounds", width = "100%", step = 0.5, min = 0, max = 2, value = 1) ), column(4, markdown("## **TC Multiper**"), sliderInput(inputId = "multiper_tc", label = "Numerai Classic Tournament Rounds", width = "100%", step = 0.5, min = 0, max = 1, value = 1) ), column(4, markdown("## **Refresh**"), br(), actionBttn(inputId = "button_kpi_chart", label = "Generate / Refresh", color = "primary", icon = icon("refresh"), style = "gradient", block = TRUE) ) ) # end of fluidRow ), 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("![new_tc_change](https://i.ibb.co/XjKwtzr/screenshot-2023-10-05-at-10.png)"), 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(), # materialSwitch(inputId = "switch_scale_payout", # label = "Fixed Scale?", # value = TRUE, # status = "primary", # ), br(), h3(strong(textOutput(outputId = "text_payout_ind_models"))), br(), shinycssloaders::withSpinner(plotlyOutput("plot_payout_individual")) ) ) # 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("![image](https://media.giphy.com/media/cftSzNoCTfSyAWctcl/giphy.gif)") ), # ======================================================================== # About # ======================================================================== tabItem(tabName = "about", markdown("# **About this App**"), markdown('### Yet another Numerai community dashboard by Jo-fai Chow.'), br(), markdown("## **Acknowledgements**"), markdown("- #### This hobby project was inspired by Rajiv's shiny-kmeans on 🤗 Spaces."), markdown('- #### The Rnumerai 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) "KPIs Summary (Individual Models)" 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) "Model Performance Comparison" 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, 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, 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 <- 600 # 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)