crest_history_prophet.R

packages.list <- c(
  "data.table",
  "quantmod",
  "jsonlite",
  "prophet",
  "dplyr",
  "ggplot2",
  "ggthemes",
  "cowplot",
  "zoo"
)
packages.new  <- packages.list[!(packages.list %in% installed.packages()[,"Package"])]
if(length(packages.new)){
  install.packages(packages.new)
}
library(data.table)
library(quantmod)
library(jsonlite)
library(prophet)
library(dplyr)
library(ggplot2)
library(ggthemes)
library(cowplot)
library(zoo)

CREST_BASE = "https://crest-tq.eveonline.com/"
## GET TYPEID/REGION ID FROM http://eve-marketdata.com/developers/index.php ##
typeID = "34"
#typeID = "29668"
#typeID = "40520"

regionID = "10000002"
chart_path = paste0(getwd(),"/Plots/")
dir.create(chart_path, showWarnings=FALSE)
plot.width  = 1600
plot.height = 900
plot.predict = 60
plot.sma.slow = 15
plot.sma.fast = 5

## SEE quantmod.com for TA ARGS ##
TA_args = "addBBands(15,2);addVo();addMACD(5,15,5);addRSI();addLines(h=30, on=4);addLines(h=70, on=4)"
graph_subset = "last 400 days"
#graph_subset = "last 1 years"

## FETCH ID/NAME CONVERSIONS FROM CREST ##
typeID_addr <- paste0(CREST_BASE, "inventory/types/", typeID, "/")
typeID.json <- fromJSON(readLines(typeID_addr))
typeName <- typeID.json$name

regionID_addr <- paste0(CREST_BASE, "regions/", regionID, "/")
regionID.json <- fromJSON(readLines(regionID_addr))
regionName <- regionID.json$name

## FETCH PRICE HISTORY FROM CREST AND INSERT INTO QUANTMOD ##
priceHistory_addr = paste0(CREST_BASE, "market/", regionID, "/history/?type=", CREST_BASE, "inventory/types/", typeID, "/")
market.json <- fromJSON(readLines(priceHistory_addr))
market.data.json <- data.table(market.json$items)
market.data <- market.data.json[,list(Date = as.Date(date)[-1],
                                      Volume= volume[-1],
                                      High  = highPrice[-1],
                                      Low   = lowPrice[-1],
                                      Close =avgPrice[-1],
                                      Open  = avgPrice)]
n <- nrow(market.data)
market.data <- market.data[1:n-1,]

## FILTER OUT HIGH/LOW FLIERS ##
low_flag  = quantile(market.data$Low,.25)/5
high_flag = quantile(market.data$High,.75)*5
market.data$Low[market.data$Low<=low_flag] <-min(market.data$Open,market.data$Close)
market.data$High[market.data$High>=high_flag] <-max(market.data$Open,market.data$Close)				

market.data$move <- market.data$Close - market.data$Open
market.data$move.normal <- market.data$move / market.data$Close
market.data$sma.fast <- rollmean(
  market.data$Close,
  k=plot.sma.fast,
  align='right',
  na.pad=TRUE
)
market.data$sma.slow <- rollmean(
  market.data$Close,
  k=plot.sma.slow,
  align='right',
  na.pad=TRUE
)

market.data$deviation.fast <- market.data$Close - market.data$sma.fast
market.data$deviation.slow <- market.data$Close - market.data$sma.slow

## Build Forecasts ##
prophet.move <- prophet(data.frame(
  ds=market.data$Date,
  y=market.data$move
))
prophet.deviation.fast <- prophet(data.frame(
  ds=market.data$Date,
  y=market.data$deviation.fast
))
prophet.deviation.slow <- prophet(data.frame(
  ds=market.data$Date,
  y=market.data$deviation.slow
))

prophet.move.final <- predict(
  prophet.move,
  make_future_dataframe(prophet.move, periods=plot.predict)
)
plot(prophet.move, prophet.move.final)
prophet.deviation.fast.final <- predict(
  prophet.deviation.fast,
  make_future_dataframe(prophet.deviation.fast, periods=plot.predict)
)
plot(prophet.deviation.fast, prophet.deviation.fast.final)
prophet.deviation.slow.final <- predict(
  prophet.deviation.slow,
  make_future_dataframe(prophet.deviation.slow, periods=plot.predict)
)
plot(prophet.deviation.slow, prophet.deviation.slow.final)

market.prophet.tmp <- data.frame(
  ds=market.data$Date,
  y=market.data$Close
)
market.prophet <- prophet(market.prophet.tmp)
market.future <- make_future_dataframe(market.prophet, periods=plot.predict)
market.predict <- predict(market.prophet, market.future)
market.predict <- merge(
  market.predict,
  market.prophet.tmp,
  by='ds',
  all=TRUE
)

market.predict$monte <- NA
monte.date <- max(market.prophet.tmp$ds, na.rm=TRUE)
monte.price <- market.data$sma.slow[market.data$Date == monte.date]
for(val in 1:plot.predict){
  step_date <- monte.date + val
  monte.price <- monte.price + prophet.move.final$yhat[prophet.move.final$ds == step_date]
  monte.high <- monte.price + prophet.move.final$yhat_upper[prophet.move.final$ds == step_date]
  monte.low  <- monte.price + prophet.move.final$yhat_lower[prophet.move.final$ds == step_date]
  
  market.predict$monte[market.predict$ds == step_date] <- monte.price
  
}
#plot(market.prophet, market.predict)

## Build Plot ##
market.max_scale = max(market.predict$y, na.rm=TRUE)
plot.future <- ggplot(
  market.predict,
  aes(
    x=ds,
    ymin=yhat_lower,
    ymax=yhat_upper
  )
)
plot.future <- plot.future + geom_ribbon(
  color='light blue',
  fill='light blue',
  alpha=0.5
)
plot.future <- plot.future + geom_line(
  aes(
    x=ds,
    y=y
  ),
  data=market.predict
)
plot.future <- plot.future + geom_line(
  aes(
    x=ds,
    y=monte
  ),
  data=market.predict,
  color='red'
)
if(market.max_scale > 1e9){
  plot.future <- plot.future + scale_y_continuous(
    #limits=c(min(market.max_scale),NA),
    labels=function(x)sprintf("%.2fB",x/1e9),
    position='right'
  )
}else if(market.max_scale > 1e6){
  plot.future <- plot.future + scale_y_continuous(
    #limits=c(min(market.max_scale),NA),
    labels=function(x)sprintf("%.2fM",x/1e6),
    position='right'
  )
}else if(market.max_scale > 1e3){
  plot.future <- plot.future + scale_y_continuous(
    #limits=c(min(market.max_scale),NA),
    labels=function(x)sprintf("%.2fK",x/1e3),
    position='right'
  )
}
plot.future <- plot.future + labs(
  x='Date',
  y='Price',
  title=paste0(typeName, ' + Prediction ', plot.predict, ' days -- ', regionName)
)
plot.future <- plot.future + theme_fivethirtyeight()
print(plot.future)

## Save to File ##
image_title <- paste(
  typeName,
  paste0("prediction-", plot.predict),
  Sys.Date(),
  regionID, 
  sep="_"
)
png(paste0(chart_path,image_title,".png"), width=plot.width, height=plot.height)
print(plot.future)
dev.off()


## SAVE DATA TO CSV FOR OTHER PLATFORMS ##
write.csv(market.json, paste0(chart_path,image_title,".csv"), row.names=FALSE)
write.csv(market.data, paste0(chart_path,image_title,"_OHLC.csv"), row.names=FALSE)

Using Prophet, generate (naive) forecasts for EVE items using the CREST market history endpoint

DISCLAIMER: forecasting strictly on price isn't a sustainable practice. ClosingPrice is the answer to a complicated non-linear set of dependencies.

Further work is required to instead forecast off something with more-normal behavior such as:

• day-to-day change
• deviation from moving average
• MER Data

Updated to include gbm/montecarlo style walker using day-to-day variation forecast to plot alternate predictions

Sometimes to comedic effect :(