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indicatorPlots.r
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indicatorPlots.r
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library(grid)
library(paramtest)
library(purrr)
source("./analysis.r")
todayDate <- as.Date(Sys.Date())
analyzeSecurity <- function(security) {
setClassicAspectsSet()
setPlanetsMOMEVESUMACEJUNNSAURNEPL()
span <- 22
drawSecurityPriceSerie <- function() {
securityPeriod <- security[Date >= chartPeriod[1] & Date <= chartPeriod[2],]
p <- ggplot(data = securityPeriod) +
geom_line(aes(x = Date, y = Mid), colour = "white", alpha = 0.7) +
geom_vline(xintercept = as.Date(datesHighs), linetype = "dashed", color = "green", size = 0.6, alpha = 0.7) +
geom_vline(xintercept = as.Date(datesLows), linetype = "dashed", color = "red", size = 0.6, alpha = 0.7) +
scale_x_date(date_breaks = dateBreaks, date_labels = "%Y-%m-%d", limits = chartPeriod) +
scale_y_log10() +
theme_black() +
theme(panel.spacing = unit(c(0, 0, 0, 0), "null")) +
theme(plot.margin = unit(c(0, 0, 0, 0), "null")) +
theme(panel.grid = element_blank()) +
theme(panel.border = element_blank()) +
theme(plot.margin = unit(c(0, 0, 0, 0), "null")) +
theme(panel.spacing = unit(c(0, 0, 0, 0), "null")) +
theme(axis.ticks = element_blank()) +
theme(axis.text = element_blank()) +
theme(axis.title = element_blank()) +
theme(axis.line = element_blank()) +
theme(legend.position = "none") +
theme(axis.ticks.length = unit(0, "null")) +
theme(axis.ticks.margin = unit(0, "null")) +
theme(legend.margin = unit(0, "null")) +
theme(axis.text.x = element_text(angle = 90, size = 10), axis.title.x = element_blank(), axis.title.y = element_blank())
}
dailyPlanets = buildPlanetsIndicators()
dailyPlanets <<- dailyPlanets
dailyPlanetsResearch <- dailyPlanets[Date > as.Date('2017-01-01') & Date < as.Date('2021-01-01'),]
chartPeriod <- c(as.Date("2018-01-10"), as.Date("2020-12-31"))
currentDates <- c(todayDate, todayDate + 1)
dateBreaks <- "7 days"
security <- mainOpenSecurity(symbol, 14, 28, "%Y-%m-%d", "2010-01-01")
dailyPlanetPriceResearch <- merge(dailyPlanets, security, by = c('Date'))
cat("Relevant peak dates:\n")
datesHighs <- security$Date[peaks(security$Mid, span)]
datesLows <- security$Date[peaks(-security$Mid, span)]
# Indicator / Price charts.
p1 <- uranusIndicators()
p2 <- drawSecurityPriceSerie()
grid.newpage()
grid.draw(rbind(ggplotGrob(p1), ggplotGrob(p2), size = "last"))
cat("1 day daily price diff percentages\n")
print(summary(abs(Delt(security$Mid, k = 1))))
cat("3 day daily price diff percentages:\n")
print(summary(abs(Delt(security$Mid, k = 3))))
return(dailyPlanetPriceResearch)
}
dailyAspectsAddSpeed <- function(dailyAspects, dailyPlanets, idCols = c('Date')) {
# Melt speeds.
dailySpeed <- data.table::melt(
dailyPlanets, id.var = idCols, variable.name = 'origin',
value.name = 'sp', measure.var = planetsSpCols
)
# Aggregate by date.
dailySpeed <- dailySpeed[, mean(sp), by = list(Date, origin)]
setnames(dailySpeed, c('Date', 'origin', 'sp'))
dailySpeed[, spn := normalize(sp), by = c('origin')]
dailySpeedX <- copy(dailySpeed)
dailySpeedX[, p.x := substr(origin, 1, 2)]
dailySpeedX[, sp.x := round(sp, 3)]
dailySpeedX[, spn.x := spn]
dailySpeedY <- copy(dailySpeed)
dailySpeedY[, p.y := substr(origin, 1, 2)]
dailySpeedY[, sp.y := round(sp, 3)]
dailySpeedY[, spn.y := spn]
selectColsY <- c('Date', 'p.y', 'sp.y', 'spn.y')
dailyAspects <- merge(dailyAspects, dailySpeedY[, ..selectColsY], by = c('Date', 'p.y'))
selectColsX <- c('Date', 'p.x', 'sp.x', 'spn.x')
dailyAspects <- merge(dailyAspects, dailySpeedX[, ..selectColsX], by = c('Date', 'p.x'))
}
dailyAspectsAddDeclination <- function(dailyAspects, dailyPlanets, idCols = c('Date')) {
# Melt speeds.
dailyDeclination <- data.table::melt(
dailyPlanets, id.var = idCols, variable.name = 'origin',
value.name = 'dc', measure.var = planetsDecCols
)
# Aggregate by date.
dailyDeclination <- dailyDeclination[, mean(dc), by = list(Date, origin)]
setnames(dailyDeclination, c('Date', 'origin', 'dc'))
dailyDeclination[, dcn := normalize(dc), by = c('origin')]
dailyDeclinationX <- copy(dailyDeclination)
dailyDeclinationX[, p.x := substr(origin, 1, 2)]
dailyDeclinationX[, dc.x := round(dc, 3)]
dailyDeclinationX[, dcn.x := dcn]
dailyDeclinationY <- copy(dailyDeclination)
dailyDeclinationY[, p.y := substr(origin, 1, 2)]
dailyDeclinationY[, dc.y := round(dc, 3)]
dailyDeclinationY[, dcn.y := dcn]
selectColsY <- c('Date', 'p.y', 'dc.y', 'dcn.y')
dailyAspects <- merge(dailyAspects, dailyDeclinationY[, ..selectColsY], by = c('Date', 'p.y'))
selectColsX <- c('Date', 'p.x', 'dc.x', 'dcn.x')
dailyAspects <- merge(dailyAspects, dailyDeclinationX[, ..selectColsX], by = c('Date', 'p.x'))
}
# Aggregate hourly observations by day leaving the min orb.
hourlyAspectsDateAggregate <- function(hourlyAspects) {
# Aggregate aspect by day with average orb.
hourlyAspects <- hourlyAspects[, min(orb), by = list(Date, origin, aspect)]
setnames(hourlyAspects, c('Date', 'origin', 'aspect', 'orb'))
}
dailyAspectsAddOrbs <- function(dailyAspects, dailyPlanets, idCols = c('Date')) {
# Melt orbs.
dailyAspectsOrbs <- data.table::melt(
dailyPlanets, id.var = idCols, variable.name = 'origin',
value.name = 'orb', measure.var = planetsCombOrb
)
dailyAspectsOrbs[, orb := round(orb, 2)]
dailyAspectsOrbs[, origin := substr(origin, 1, 4)]
dailyAspects[, origin := substr(origin, 1, 4)]
# Join aspects & orbs.
merge(dailyAspects, dailyAspectsOrbs, by = c(idCols, 'origin'))
}
dailyAspectsAddOrbsDir <- function(dailyAspects) {
# Calculate orb direction (applicative, separative).
dailyAspects[, orbdir := round(orb - Lag(orb, k = 1), 4), by = c('origin', 'aspect')]
dailyAspects[is.na(orbdir), orbdir := 0]
dailyAspects[, type := cut(orbdir, c(-100, 0, 100), labels = (c('A', 'S')))]
}
dailyAspectsAddLongitude <- function(dailyAspects, dailyPlanets, idCols = c('Date')) {
# Melt longitudes.
dailyLongitudes <- data.table::melt(dailyPlanets, id.var = idCols, variable.name = 'origin',
value.name = 'lon', measure.var = planetsLonCols)
# Aggregate by date.
dailyLongitudes <- dailyLongitudes[, mean(lon), by = list(Date, origin)]
setnames(dailyLongitudes, c('Date', 'origin', 'lon'))
# For first planet.
dailyLongitudesX <- copy(dailyLongitudes)
dailyLongitudesX[, p.x := substr(origin, 1, 2)]
dailyLongitudesX[, lon.x := lon]
# For second planet.
dailyLongitudesY <- copy(dailyLongitudes)
dailyLongitudesY[, p.y := substr(origin, 1, 2)]
dailyLongitudesY[, lon.y := lon]
# Merge
dailyAspects[, p.x := substr(origin, 1, 2)]
dailyAspects[, p.y := substr(origin, 3, 4)]
selectColsY <- c('Date', 'p.y', 'lon.y')
dailyAspects <- merge(dailyAspects, dailyLongitudesY[, ..selectColsY], by = c('Date', 'p.y'))
selectColsX <- c('Date', 'p.x', 'lon.x')
dailyAspects <- merge(dailyAspects, dailyLongitudesX[, ..selectColsX], by = c('Date', 'p.x'))
}
dailyAspectsAddPlanetsActivation <- function(dailyAspects) {
aspCols <- paste("a", aspects, sep = "")
dailyPlanetActivation <- data.table::melt(
dailyAspects, id.var = c('Date', 'p.x'),
variable.name = 'origin', measure.var = c('p.y')
)
setnames(dailyPlanetActivation, c('Date', 'p.x', 'origin', 'p.y'))
dailyPlanetActivationCountX <- dailyPlanetActivation[,
data.table(table(p.x, p.y)), by = list(Date)
]
dailyPlanetActivationCountXWide <- dcast(
dailyPlanetActivationCountX,
Date + p.x ~ factor(p.y, levels = planetsBaseCols),
value.var = "N", fill = 0
)
setDT(dailyPlanetActivationCountXWide)
dailyPlanetActivationCountY <- dailyPlanetActivation[,
data.table(table(p.x, p.y)), by = list(Date)
]
dailyPlanetActivationCountYWide <- dcast(
dailyPlanetActivationCountY,
Date + p.y ~ factor(p.x, levels = planetsBaseCols),
value.var = "N", fill = 0
)
setDT(dailyPlanetActivationCountYWide)
# Merge X/Y planets activation counts.
dailyAspects <- merge(dailyAspects, dailyPlanetActivationCountXWide, by = c("Date", "p.x"))
dailyAspects <- merge(dailyAspects, dailyPlanetActivationCountYWide, by = c("Date", "p.y"))
# Calculate cumulative planets activation.
dailyAspects[, ME := ME.x + ME.y]
dailyAspects[, VE := VE.x + VE.y]
dailyAspects[, SU := SU.x + SU.y]
dailyAspects[, MA := MA.x + MA.y]
dailyAspects[, JU := JU.x + JU.y]
dailyAspects[, SA := SA.x + SA.y]
#dailyAspects[, UR := UR.x + UR.y]
#dailyAspects[, NE := NE.x + NE.y]
#dailyAspects[, PL := PL.x + PL.y]
}
dailyAspectsAddAspectsCount <- function(dailyAspects) {
aspCols <- paste("a", aspects, sep = "")
dailyPlanetAspects <- data.table::melt(
dailyAspects, id.var = c('Date', 'aspect'),
variable.name = 'origin', measure.var = c('p.x', 'p.y')
)
setnames(dailyPlanetAspects, c('Date', 'aspect', 'origin', 'planet'))
# Daily total aspects per planet count.
dailyAspectsPlanetCount <- dailyPlanetAspects[, data.table(table(aspect, planet)), by = list(Date)]
dailyAspectsPlanetCount[, aspect := paste("a", aspect, sep = "")]
dailyAspectsPlanetCountWide <- dcast(
dailyAspectsPlanetCount,
Date + planet ~ factor(aspect, levels = aspCols),
value.var = "N", fill = 0
)
setDT(dailyAspectsPlanetCountWide)
# Merge aspects count once per each former planets.
setnames(dailyAspectsPlanetCountWide, c('Date', 'p.x', paste(aspCols, 'x', sep = ".")))
dailyAspects <- merge(dailyAspects, dailyAspectsPlanetCountWide, by = c("Date", "p.x"))
aspColsX <- paste(aspCols, 'x', sep = ".")
#dailyAspects[, c(aspColsX) := lapply(.SD, function(x) ifelse(is.na(x), 0, x)), .SDcols = aspColsX]
setnames(dailyAspectsPlanetCountWide, c('Date', 'p.y', paste(aspCols, 'y', sep = ".")))
dailyAspects <- merge(dailyAspects, dailyAspectsPlanetCountWide, by = c("Date", "p.y"))
aspColsY <- paste(aspCols, 'y', sep = ".")
#dailyAspects[, c(aspColsY) := lapply(.SD, function(x) ifelse(is.na(x), 0, x)), .SDcols = aspColsY]
# Daily total aspects count.
dailyAspectsCount <- dailyPlanetAspects[, data.table(table(aspect)), by = list(Date)]
dailyAspectsCount[, aspect := paste("a", aspect, sep = "")]
dailyAspectsCountWide <- dcast(
dailyAspectsCount,
Date ~ factor(aspect, levels = aspCols),
value.var = "N", fill = 0
)
setDT(dailyAspectsCountWide)
setnames(dailyAspectsCountWide, c('Date', paste(aspCols, 'g', sep = ".")))
dailyAspects <- merge(dailyAspects, dailyAspectsCountWide, by = c("Date"))
aspColsT <- paste(aspCols, 'g', sep = ".")
#dailyAspects[, c(aspColsT) := lapply(.SD, function(x) ifelse(is.na(x), 0, x)), .SDcols = aspColsT]
# aggregate x/y aspects count by type.
dailyAspects[, a0 := a0.x + a0.y]
dailyAspects[, a30 := a30.x + a30.y]
dailyAspects[, a45 := a45.x + a45.y]
dailyAspects[, a60 := a60.x + a60.y]
dailyAspects[, a90 := a90.x + a90.y]
dailyAspects[, a120 := a120.x + a120.y]
dailyAspects[, a135 := a135.x + a135.y]
dailyAspects[, a150 := a150.x + a150.y]
dailyAspects[, a180 := a180.x + a180.y]
# aggregate x/y aspects count by type.
dailyAspects[, a0.d := a0.x - a0.y]
dailyAspects[, a30.d := a30.x - a30.y]
dailyAspects[, a45.d := a45.x - a45.y]
dailyAspects[, a60.d := a60.x - a60.y]
dailyAspects[, a90.d := a90.x - a90.y]
dailyAspects[, a120.d := a120.x - a120.y]
dailyAspects[, a135.d := a135.x - a135.y]
dailyAspects[, a150.d := a150.x - a150.y]
dailyAspects[, a180.d := a180.x - a180.y]
# Total aspect cumulative and daily totals count.
dailyAspects[, acx := a0.x +
a30.x +
a45.x +
a60.x +
a90.x +
a120.x +
a135.x +
a150.x +
a180.x
]
dailyAspects[, acy := a0.y +
a30.y +
a45.y +
a60.y +
a90.y +
a120.y +
a135.y +
a150.y +
a180.y
]
dailyAspects[, agt := a0.g +
a30.g +
a45.g +
a60.g +
a90.g +
a120.g +
a135.g +
a150.g +
a180.g
]
#dailyAspects[, acp := a30 + a60 + a120]
#dailyAspects[, acn := a45 + a90 + a135]
return(dailyAspects)
}
dailyPlanetAspectsCumulativeEnergyTable <- function(dailyAspects) {
aspCols <- paste("a", aspects, sep = "")
dailyPlanetAspects <- data.table::melt(
dailyAspects, id.var = c('Date', 'p.x', 'p.y', 'aspect', 'orb', 'ennow'),
variable.name = 'origin', measure.var = c('p.x', 'p.y')
)
setnames(dailyPlanetAspects, c('Date', 'p.x', 'p.y', 'aspect', 'orb', 'ennow', 'origin', 'planet'))
# Daily total energy per aspect and planet.
dailyAspectsPlanetCumEnergy <- dailyPlanetAspects[,
data.table(aggregate(ennow, list(aspect, planet), sum)), by = list(Date)
]
setnames(dailyAspectsPlanetCumEnergy, c('Date', 'aspect', 'planet', 'ennow'))
dailyAspectsPlanetCumEnergy[, aspect := paste("a", aspect, sep = "")]
dailyAspectsPlanetCumEnergyWide <- dcast(
dailyAspectsPlanetCumEnergy,
Date + planet ~ factor(aspect, levels = aspCols),
value.var = "ennow", fill = 0
)
setDT(dailyAspectsPlanetCumEnergyWide)
return(dailyAspectsPlanetCumEnergyWide)
}
dailyAspectsAddAspectsCumulativeEnergy <- function(dailyAspects) {
aspCols <- paste("a", aspects, sep = "")
dailyPlanetAspectsCumulativeEnergy <- dailyPlanetAspectsCumulativeEnergyTable(dailyAspects)
# Merge aspects cumulative energy once per each former planets.
setnames(dailyPlanetAspectsCumulativeEnergy, c('Date', 'p.x', paste(aspCols, 'x', sep = ".")))
dailyAspects <- merge(dailyAspects, dailyPlanetAspectsCumulativeEnergy, by = c("Date", "p.x"))
setnames(dailyPlanetAspectsCumulativeEnergy, c('Date', 'p.y', paste(aspCols, 'y', sep = ".")))
dailyAspects <- merge(dailyAspects, dailyPlanetAspectsCumulativeEnergy, by = c("Date", "p.y"))
# sum x/y aspects energy by type.
dailyAspects[, a0 := a0.x + a0.y]
dailyAspects[, a30 := a30.x + a30.y]
dailyAspects[, a45 := a45.x + a45.y]
dailyAspects[, a60 := a60.x + a60.y]
dailyAspects[, a90 := a90.x + a90.y]
dailyAspects[, a120 := a120.x + a120.y]
dailyAspects[, a150 := a150.x + a150.y]
dailyAspects[, a180 := a180.x + a180.y]
# diff x/y aspects energy by type.
dailyAspects[, a0.d := a0.x - a0.y]
dailyAspects[, a30.d := a30.x - a30.y]
dailyAspects[, a45.d := a45.x - a45.y]
dailyAspects[, a60.d := a60.x - a60.y]
dailyAspects[, a90.d := a90.x - a90.y]
dailyAspects[, a120.d := a120.x - a120.y]
dailyAspects[, a150.d := a150.x - a150.y]
dailyAspects[, a180.d := a180.x - a180.y]
return(dailyAspects)
}
dailyAspectsAddEnergy <- function(dailyAspects, speedDecay = 0.6, aspectsEnergyCustom = NULL) {
if (is.null(aspectsEnergyCustom)) {
aspectsEnergyCustom <- aspectsEnergy
}
aspectsEnergyIndex <- matrix(
aspectsEnergyCustom, nrow = 1, ncol = length(aspectsEnergyCustom),
byrow = T, dimnames = list(c('energy'), aspects)
)
# Calculate max and proportional energy.
dailyAspects[, enmax := aspectsEnergyIndex['energy', as.character(aspect)]]
dailyAspects[, ennow := energyDecay(enmax, orb, speedDecay)]
return(dailyAspects)
}
dailyAspectsAddEnergy2 <- function(dailyAspects, aspectsEnergyCustom = NULL) {
if (is.null(aspectsEnergyCustom)) {
aspectsEnergyCustom <- aspectsEnergy
}
aspectsEnergyIndex <- matrix(
aspectsEnergyCustom, nrow = 1, ncol = length(aspectsEnergyCustom),
byrow = T, dimnames = list(c('energy'), aspects)
)
# Calculate max and proportional energy.
dailyAspects[, enmax := aspectsEnergyIndex['energy', as.character(aspect)]]
dailyAspects[, ennow := enmax]
return(dailyAspects)
}
# Energy proportional to speed.
dailyAspectsAddEnergy3 <- function(dailyAspects, speedDecay = 0.6) {
aspectsEnergyIndex <- matrix(
aspectsEnergy, nrow = 1, ncol = length(aspectsEnergy),
byrow = T, dimnames = list(c('energy'), aspects)
)
# Calculate max and proportional energy.
dailyAspects[, enmax := aspectsEnergyIndex['energy', as.character(aspect)]]
dailyAspects[, ennow := energyDecay(enmax, orb, speedDecay) *
(1 - sp.x) *
(1 - sp.y)]
return(dailyAspects)
}
dailyAspectsAddEnergyWeightWithDecay <- function(dailyAspects, speedDecay = 0.6, aspectWeight) {
energyConstant <- 2
aspectWeightIndex <- matrix(
aspectWeight, nrow = 1, ncol = length(aspectWeight),
byrow = T, dimnames = list(c('weight'), aspects)
)
# Calculate max and proportional energy.
dailyAspects[, enmax := energyConstant * aspectWeightIndex['weight', as.character(aspect)]]
dailyAspects[, ennow := energyDecay(enmax, orb, speedDecay)]
return(dailyAspects)
}
dailyAspectsAddEnergyWeightWithDecay2 <- function(
dailyAspects,
speedDecay = 0.6,
planetWeight,
aspectWeight
) {
energyConstant <- 2
aspectWeightIndex <- matrix(
aspectWeight, nrow = 1, ncol = length(aspectWeight),
byrow = T, dimnames = list(c('weight'), aspects)
)
planetWeightIndex <- matrix(
planetWeight, nrow = 1, ncol = length(planetWeight),
byrow = T, dimnames = list(c('weight'), planetsBaseCols)
)
# Calculate max and proportional energy.
dailyAspects[, enweight := aspectWeightIndex['weight', as.character(aspect)] * planetWeightIndex['weight', p.y]]
dailyAspects[, enmax := energyConstant * enweight]
dailyAspects[, ennow := energyDecay(enmax, orb, speedDecay)]
return(dailyAspects)
}
dailyAspectsAddEnergyWeightWithDecay3 <- function(
dailyAspects,
speedDecay = 0.6,
planetWeight
) {
energyConstant <- 2
planetWeightIndex <- matrix(
planetWeight, nrow = 1, ncol = length(planetWeight),
byrow = T, dimnames = list(c('weight'), planetsBaseCols)
)
# Calculate max and proportional energy.
dailyAspects[, enweight := planetWeightIndex['weight', p.y]]
dailyAspects[, enmax := energyConstant * enweight]
dailyAspects[, ennow := energyDecay(enmax, orb, speedDecay)]
return(dailyAspects)
}
dailyAspectsAddEnergyWeightWithDecay4 <- function(
dailyAspects,
speedDecay = 0.6,
planetWeight
) {
energyConstant <- 2
planetWeightIndex <- matrix(
planetWeight, nrow = 1, ncol = length(planetWeight),
byrow = T, dimnames = list(c('weight'), planetsBaseCols)
)
# Calculate max and proportional energy.
dailyAspects[, enweight := planetWeightIndex['weight', p.x] * planetWeightIndex['weight', p.y]]
dailyAspects[, enmax := energyConstant * enweight]
dailyAspects[, ennow := energyDecay(enmax, orb, speedDecay)]
return(dailyAspects)
}
dailyAspectsAddEnergyWeightWithDecay5 <- function(
dailyAspects,
speedDecay = 0.6,
planetWeight,
aspectWeight
) {
energyConstant <- 2
aspectWeightIndex <- matrix(
aspectWeight, nrow = 1, ncol = length(aspectWeight),
byrow = T, dimnames = list(c('weight'), aspects)
)
planetWeightIndex <- matrix(
planetWeight, nrow = 1, ncol = length(planetWeight),
byrow = T, dimnames = list(c('weight'), planetsBaseCols)
)
# Calculate max and proportional energy.
dailyAspects[,
enweight :=
aspectWeightIndex['weight', as.character(aspect)] *
planetWeightIndex['weight', p.x] *
planetWeightIndex['weight', p.y]
]
dailyAspects[, enmax := energyConstant * enweight]
dailyAspects[, ennow := energyDecay(enmax, orb, speedDecay)]
return(dailyAspects)
}
dailyAspectsAddCumulativeEnergy <- function(dailyAspects) {
# Merge daily security prices with aspects.
# dailyAspectsPriceResearch <- merge(dailyAspects, securityTrain[, c('Date', 'diffPercent')], by = c('Date'))
# Calculate the historical mean aspect effect.
#aspectsEffect <- dailyAspectsPriceResearch[
# orb <= 1,
# list(round(mean(diffPercent), 4), round(median(diffPercent), 4)),
# by = c('origin', 'aspect', 'type')
#]
#setnames(aspectsEffect, c('origin', 'aspect', 'type', 'diffMean', 'diffMedian'))
#dailyAspects[, diffMean := setAspectEffect(.SD), by=c('Date', 'origin', 'aspect', 'type')]
# dailyAspectsPriceResearch <- merge(dailyAspectsPriceResearch, aspectsEffect, by = c('origin', 'aspect', 'type'))
# dailyAspects <- merge(dailyAspects, aspectsEffect, by = c('origin', 'aspect', 'type'))
# Melt involved planets current energy for body strength calculation.
dailyAspectsPlanetsEnergy <- data.table::melt(
dailyAspects, id.var = c('Date', 'ennow'),
variable.name = 'origin', value.name = 'planet',
measure.var = c('p.x', 'p.y')
)
dailyAspectsCumulativeEnergy <- dailyAspectsPlanetsEnergy[, round(sum(ennow), 2), by = c('Date', 'planet')]
setkey(dailyAspectsCumulativeEnergy, 'Date')
# Merge cumulative planets energy.
setnames(dailyAspectsCumulativeEnergy, c('Date', 'p.x', 'encum.x'))
dailyAspects <- merge(dailyAspects, dailyAspectsCumulativeEnergy, by = c('Date', 'p.x'))
setnames(dailyAspectsCumulativeEnergy, c('Date', 'p.y', 'encum.y'))
dailyAspects <- merge(dailyAspects, dailyAspectsCumulativeEnergy, by = c('Date', 'p.y'))
return(dailyAspects)
}
# Decay cumulative energy that can result in reversed polarity when cumulative and own energy both are negative
# due the fact that -1 x -1 = 1.
dailyAspectsAddEffectM1 <- function(dailyAspects) {
dailyAspects[, entot := round((encum.x + encum.y) * ennow, 2)]
dailyAspects[, effect := entot]
return(dailyAspects)
}
# Cumulative and own energy polarity converted to the mean past effect.
dailyAspectsAddEffectM2 <- function(dailyAspects) {
dailyAspects[, entot := round((encum.x + encum.y) * ennow, 2)]
dailyAspects[, effect := round(sign(diffMean) * entot, 2)]
return(dailyAspects)
}
# Decay cumulative energy disregard the executor aspect polarity.
dailyAspectsAddEffectM3 <- function(dailyAspects) {
dailyAspects[, entot := round((encum.x + encum.y) * abs(ennow), 2)]
dailyAspects[, effect := entot]
return(dailyAspects)
}
# Combinatory cumulative + own energy.
dailyAspectsAddEffectM4 <- function(dailyAspects) {
dailyAspects[, entot := round(encum.x + encum.y + ennow, 2)]
dailyAspects[, effect := entot]
return(dailyAspects)
}
# Cumulative log10 energy disregard the executor aspect polarity.
dailyAspectsAddEffectM5 <- function(dailyAspects) {
dailyAspects[, entot := round(log10((encum.x + encum.y) * abs(ennow)), 2)]
dailyAspects[, effect := entot]
return(dailyAspects)
}
dailyAspectsEffectIndex <- function(dailyAspects, idCols = c('Date')) {
# Daily aspects effect index.
dailyAspectsIndex <- dailyAspects[, round(sum(effect), 2), by = idCols]
dailyAspectsIndex[, diff := round(Delt(V1, k = 1), 2)]
setnames(dailyAspectsIndex, c('Date', 'effect', 'diff'))
dailyAspectsIndex[, effectMA := SMA(effect, 3)]
return(dailyAspectsIndex)
}
crossValidateModelReport <- function(modelId, dailyAspectsIndex, security) {
cat("VALIDATING: ", modelId, "\n")
predictSecurityModelReport(dailyAspectsIndex, security[fold == 1,])
predictSecurityModelReport(dailyAspectsIndex, security[fold == 2,])
predictSecurityModelReport(dailyAspectsIndex, security[fold == 3,])
#predictSecurityModelReport(dailyAspectsIndex, security[fold == 4,])
#predictSecurityModelReport(dailyAspectsIndex, security[fold == 5,])
}
crossValidateModelOptimization <- function(modelId, dailyAspectsIndex, security) {
cat("OPTIMIZING: ", modelId, "\n")
modelFit1 <- predictSecurityFit(dailyAspectsIndex, security[fold == 1,])
modelFit2 <- predictSecurityFit(dailyAspectsIndex, security[fold == 2,])
modelFit3 <- predictSecurityFit(dailyAspectsIndex, security[fold == 3,])
medianFit <- median(c(modelFit1, modelFit2, modelFit3))
cat("MEDIAN FIT: ", medianFit, "\n")
cat("\n")
return(medianFit)
}
predictSecurityFit <- function(dailyAspectsIndex, securityTest) {
modelTest <- merge(dailyAspectsIndex, securityTest[, c('Date', 'Mid', 'diffPercent')], by = c('Date'))
modelFit <- cor(modelTest$effect, modelTest$Mid, method = "pearson")
cat("CORRELATION EFFECT / PRICE: ", modelFit, "\n")
return(modelFit)
}
predictSecurityModelReport <- function(dailyAspectsIndex, securityTest) {
dailyAspectsIndexProjected <- dailyAspectsIndex[Date > todayDate - 60,][0:120]
modelTest <- merge(dailyAspectsIndex, securityTest[, c('Date', 'Mid', 'diffPercent')], by = c('Date'))
#cat("Today aspects:", format(todayDate, "%Y-%m-%d"), "\n")
#print(dailyAspects[Date == todayDate,][order(-entot)], topn = 200)
#cat("\n")
#
#cat("Tomorrow aspects:", format(todayDate + 1, "%Y-%m-%d"), "\n")
#print(dailyAspects[Date == todayDate + 1,][order(-entot)], topn = 200)
#cat("\n")
#
#cat("Past tomorrow aspects:", format(todayDate + 2, "%Y-%m-%d"), "\n")
#print(dailyAspects[Date == todayDate + 2,][order(-entot)], topn = 200)
#cat("\n")
#
#cat("Daily aspects effect index:\n")
#print(dailyAspectsIndexProjected, topn = 100)
#cat("\n")
# cat("Daily test period:\n")
# print(modelTest)
p1 <- ggplot(data = modelTest) +
geom_line(aes(x = Date, y = Mid), colour = "white", alpha = 0.8) +
theme_black()
p2 <- ggplot(data = modelTest) +
geom_line(aes(x = Date, y = effect), colour = "white", alpha = 0.8) +
geom_line(aes(x = Date, y = effectMA), colour = "yellow", alpha = 0.8) +
# Do the effect index log conversion per model due to some energy models has negative values.
# scale_y_log10() +
theme_black()
p3 <- ggplot(data = modelTest) +
geom_point(aes(x = abs(effect), y = abs(diffPercent)), colour = "white", alpha = 0.8) +
theme_black()
p4 <- ggplot(data = modelTest) +
geom_point(aes(x = diff, y = diffPercent), colour = "white", alpha = 0.8) +
theme_black()
p5 <- ggplot(data = dailyAspectsIndexProjected) +
geom_line(aes(x = Date, y = effect), colour = "white", alpha = 0.8) +
geom_line(aes(x = Date, y = effectMA), colour = "yellow", alpha = 0.8) +
scale_x_date(date_breaks = '7 days', date_labels = "%Y-%m-%d") +
theme_black()
pgridTop <- plot_grid(p1, p3, p2, p4)
pgrid <- plot_grid(pgridTop, p5, ncol = 1, rel_heights = c(1.7, 1))
print(pgrid)
cat("\nCORRELATION EFFECT / MOVE RANGE: ", cor(abs(modelTest$effect), abs(modelTest$diffPercent), method = "pearson"), "\n")
cat("\nCORRELATION EFFECT / PRICE: ", cor(modelTest$effect, modelTest$Mid, method = "pearson"), "\n")
cat("\nCORRELATION EFFECT MA / PRICE: ", cor(modelTest$effectMA, modelTest$Mid, method = "pearson"), "\n")
}
dailyAspectsNameCols <- function(dailyAspects) {
# Set more convenient order for analysis.
colsOrder <- c('Date', 'origin', 'p.x', 'lon.x', 'sp.x', 'spn.x', 'p.y', 'lon.y', 'sp.y', 'spn.y', 'aspect', 'type', 'orb', 'orbdir', 'enmax', 'ennow', 'encum.x', 'encum.y', 'entot', 'effect')
setcolorder(dailyAspects, colsOrder)
return(dailyAspects)
}
dailyHourlyAspectsTablePrepare <- function(hourlyPlanets, idCols, orbLimit = NULL) {
hourlyPlanetsRange <- hourlyPlanets[Date >= as.Date("2018-01-01") & Date <= as.Date("2022-12-31")]
# Melt aspects.
hourlyAspects <- data.table::melt(
hourlyPlanetsRange, id.var = idCols,
variable.name = 'origin', value.name = 'aspect',
value.factor = T, measure.var = planetsCombAsp, na.rm = T
)
# hourlyAspects[, origin := substr(origin, 1, 4)]
#setkey(hourlyAspects, 'Date', 'Hour')
hourlyAspects <- dailyAspectsAddOrbs(hourlyAspects, hourlyPlanetsRange, idCols)
if (!is.null(orbLimit)) {
hourlyAspects <- hourlyAspects[orb <= orbLimit,]
}
dailyAspects <- hourlyAspectsDateAggregate(hourlyAspects)
dailyAspects <- dailyAspectsAddOrbsDir(dailyAspects)
dailyAspects <- dailyAspectsAddLongitude(dailyAspects, hourlyPlanetsRange, idCols)
dailyAspects <- dailyAspectsAddSpeed(dailyAspects, hourlyPlanetsRange, idCols)
dailyAspects <- dailyAspectsAddDeclination(dailyAspects, hourlyPlanetsRange, idCols)
return(dailyAspects)
}
dailyAspectsTablePrepare <- function(dailyPlanets) {
dailyPlanetsRange <- dailyPlanets[Date >= as.Date("2015-01-01") & Date <= as.Date("2023-01-01")]
# Melt aspects.
dailyAspects <- data.table::melt(dailyPlanetsRange, id.var = c('Date'), variable.name = 'origin',
value.name = 'aspect', value.factor = T, measure.var = planetsCombAsp, na.rm = T)
dailyAspects[, origin := substr(origin, 1, 4)]
dailyAspects <- dailyAspectsAddOrbs(dailyAspects, dailyPlanets)
dailyAspects <- dailyAspectsAddLongitude(dailyAspects, dailyPlanets)
dailyAspects <- dailyAspectsAddSpeed(dailyAspects, dailyPlanets)
return(dailyAspects)
}
hourlyAspectsEffectIndex <- function(hourlyAspects) {
# Daily aspects effect index.
hourlyAspectsIndex <- hourlyAspects[, round(sum(effect), 2), by = c('Date', 'Hour')]
hourlyAspectsIndex[, diff := round(Delt(V1, k = 1), 2)]
setnames(hourlyAspectsIndex, c('Date', 'Hour', 'effect', 'diff'))
hourlyAspectsIndex[, effectMA := SMA(effect, 3)]
return(hourlyAspectsIndex)
}
# This model uses:
# - Daily aspects & prices.
# - Classical aspects set.
# - Includes CE but not NN or SN cycles.
# - Use common daily aspects true energy disregard the historical security effect.
# - Increase strength of 90 aspects energy by 2x.
predictSecurityModelA <- function(security) {
# Best effect correlation when using classic aspects only.
setClassicAspectsSet()
setPlanetsMEVESUMACEJUSAURNEPL()
dailyPlanets <<- openPlanets('planets_10', clear = F)
dailyAspects <- dailyAspectsTablePrepare(dailyPlanets)
dailyAspects <- dailyAspectsAddEnergy(dailyAspects, 0.5)
dailyAspects <- dailyAspectsAddCumulativeEnergy(dailyAspects)
dailyAspects <- dailyAspectsAddEffectM1(dailyAspects)
dailyAspects <- dailyAspectsNameCols(dailyAspects)
dailyAspectsIndex <- dailyAspectsEffectIndex(dailyAspects)
crossValidateModelReport("modelA", dailyAspectsIndex, security)
}
# This model uses:
# - Hourly aspects & prices.
# - Classical aspects set.
# - Don't include MO, CE and NN.
# - Use common daily aspects true energy disregard the historical security effect.
# - Increase strength of 90 aspects energy by 2x.
predictSecurityModelB <- function(security) {
# Best effect correlation when using classic aspects only.
setClassicAspectsSet()
setPlanetsMEVESUMAJUSAURNEPL()
dailyHourlyPlanets <<- openHourlyPlanets('planets_11', clear = F)
idCols <- c('Date', 'Hour')
hourlyAspects <- dailyHourlyAspectsTablePrepare(dailyHourlyPlanets, idCols)
hourlyAspects <- dailyAspectsAddEnergy(hourlyAspects, 0.5)
hourlyAspects <- dailyAspectsAddCumulativeEnergy(hourlyAspects)
hourlyAspects <- dailyAspectsAddEffectM1(hourlyAspects)
dailyAspectsIndex <- dailyAspectsEffectIndex(hourlyAspects)
crossValidateModelReport("modelB", dailyAspectsIndex, security)
}
# This model uses:
# - Hourly aspects & prices.
# - Classical aspects set.
# - Don't include CE, and include all the planets and MO.
# - Use common daily aspects true energy disregard the historical security effect.
# - Increase strength of 90 aspects energy by 2x.
predictSecurityModelC <- function(security) {
# Best effect correlation when using classic aspects only.
setClassicAspectsSet()
#setPlanetsMOMEVESUMAJUSAURNEPL()
setPlanetsMOMEVESUMAJUNNSAURNEPL()
dailyHourlyPlanets <<- openHourlyPlanets('planets_11', clear = F)
idCols <- c('Date', 'Hour')
hourlyAspects <- dailyHourlyAspectsTablePrepare(dailyHourlyPlanets, idCols)
hourlyAspects <- dailyAspectsAddEnergy(hourlyAspects, 0.5)
hourlyAspects <- dailyAspectsAddCumulativeEnergy(hourlyAspects)
hourlyAspects <- dailyAspectsAddEffectM1(hourlyAspects)
dailyAspectsIndex <- dailyAspectsEffectIndex(hourlyAspects)
crossValidateModelReport("modelC", dailyAspectsIndex, security)
}
# This model uses:
# - Hourly aspects & prices.
# - Classical aspects set but limiting energy to a set: 45, 90, 120 and 150.
# - Increase strength of 90 and 150 aspects energy by 2x.
# - Don't include CE, and include all the planets and MO.
# - Use common daily aspects true energy disregard the historical security effect.
predictSecurityModelD <- function(security) {
# Best effect correlation when using classic aspects only.
setClassicAspectsSet2()
setPlanetsMOMEVESUMAJUNNSAURNEPL()
dailyHourlyPlanets <<- openHourlyPlanets('planets_11', clear = F)
idCols <- c('Date', 'Hour')
hourlyAspects <- dailyHourlyAspectsTablePrepare(dailyHourlyPlanets, idCols)
hourlyAspects <- dailyAspectsAddEnergy(hourlyAspects, 0.5)
hourlyAspects <- dailyAspectsAddCumulativeEnergy(hourlyAspects)
hourlyAspects <- dailyAspectsAddEffectM1(hourlyAspects)
dailyAspectsIndex <- dailyAspectsEffectIndex(hourlyAspects)
crossValidateModelReport("modelD", dailyAspectsIndex, security)
}
# This model uses:
# - Hourly aspects & prices.
# - Classical aspects set with energy polarity.
# - Boost strength of 90 and 150 aspects energy over others.
# - Don't include CE, and include all the planets and MO.
# - Use common daily aspects true energy disregard the historical security effect.
predictSecurityModelE <- function(security) {
# Best effect correlation when using classic aspects only.
setClassicAspectsSet3()
setPlanetsMOMEVESUMAJUNNSAURNEPL()
dailyHourlyPlanets <<- openHourlyPlanets('planets_11', clear = F)
idCols <- c('Date', 'Hour')
hourlyAspects <- dailyHourlyAspectsTablePrepare(dailyHourlyPlanets, idCols)
hourlyAspects <- dailyAspectsAddEnergy(hourlyAspects, 0.5)
hourlyAspects <- dailyAspectsAddCumulativeEnergy(hourlyAspects)
hourlyAspects <- dailyAspectsAddEffectM1(hourlyAspects)
dailyAspectsIndex <- dailyAspectsEffectIndex(hourlyAspects)
crossValidateModelReport("modelE", dailyAspectsIndex, security)
}
# This model uses:
# - Hourly aspects & prices.
# - Classical aspects set with energy polarity.
# - Boost strength of 90 and 150 aspects energy over others.
# - Effect is the proportion to the orb from the received aspect energy accounting with the original polarity.
# - Don't include CE, and include all the planets and MO.
# - Use common daily aspects true energy disregard the historical security effect.
predictSecurityModelF <- function(security) {
# Best effect correlation when using classic aspects only.
setClassicAspectsSet3()
setPlanetsMOMEVESUMAJUSAURNEPL()
dailyHourlyPlanets <<- openHourlyPlanets('planets_11', clear = F)
idCols <- c('Date', 'Hour')
hourlyAspects <- dailyHourlyAspectsTablePrepare(dailyHourlyPlanets, idCols)
hourlyAspects <- dailyAspectsAddEnergy(hourlyAspects, 0.5)
hourlyAspects <- dailyAspectsAddCumulativeEnergy(hourlyAspects)
hourlyAspects <- dailyAspectsAddEffectM3(hourlyAspects)
dailyAspectsIndex <- dailyAspectsEffectIndex(hourlyAspects)
crossValidateModelReport("modelF", dailyAspectsIndex, security)
}
# This model uses:
# - Hourly aspects & prices.
# - Classical aspects set with energy polarity.
# - Boost strength of 90 and 150 aspects energy over others.
# - Effect is the sum of the cumulative and own energy.
# - Don't include CE, and include all the planets and MO.
# - Use common daily aspects true energy disregard the historical security effect.
predictSecurityModelG <- function(security) {
# Best effect correlation when using classic aspects only.
setClassicAspectsSet3()
setPlanetsMOMEVESUMAJUNNSAURNEPL()
dailyHourlyPlanets <<- openHourlyPlanets('planets_11', clear = F)
idCols <- c('Date', 'Hour')
hourlyAspects <- dailyHourlyAspectsTablePrepare(dailyHourlyPlanets, idCols)
hourlyAspects <- dailyAspectsAddEnergy(hourlyAspects, 0.5)
hourlyAspects <- dailyAspectsAddCumulativeEnergy(hourlyAspects)
hourlyAspects <- dailyAspectsAddEffectM4(hourlyAspects)
# hourlyAspectsIndex <- hourlyAspectsEffectIndex(hourlyAspects)
dailyAspectsIndex <- dailyAspectsEffectIndex(hourlyAspects)
crossValidateModelReport("modelG", dailyAspectsIndex, security)
}
# This model uses:
# - Hourly aspects & prices.
# - Classical aspects set with energy polarity.
# - Boost strength of 90 and 150 aspects energy over others.
# - Effect is the proportion to the orb from the received aspects cumulative energy accounting with the original polarity.
# - Don't include CE, and include all the planets and MO.
# - Orb decay energy speed is slower to 0.2.
# - MO energy only accounts as cumulative for other aspects.
# - Use common daily aspects true energy disregard the historical security effect.
predictSecurityModelH <- function(security) {
# Best effect correlation when using classic aspects only.
setClassicAspectsSet3()
setPlanetsMOMEVESUMAJUNNSAURNEPL()
dailyHourlyPlanets <<- openHourlyPlanets('planets_11', clear = F)
idCols <- c('Date', 'Hour')
hourlyAspects <- dailyHourlyAspectsTablePrepare(dailyHourlyPlanets, idCols)
hourlyAspects <- dailyAspectsAddEnergy(hourlyAspects, 0.2)
hourlyAspects <- dailyAspectsAddCumulativeEnergy(hourlyAspects)
# MO only contribute to the cumulative effect but is not a major indicator.
hourlyAspects <- hourlyAspects[p.x != 'MO',]
hourlyAspects <- dailyAspectsAddEffectM3(hourlyAspects)
dailyAspectsIndex <- dailyAspectsEffectIndex(hourlyAspects)
crossValidateModelReport("modelH", dailyAspectsIndex, security)
}
# This model uses:
# - Hourly aspects & prices.
# - Classical aspects set with energy polarity.
# - Increase strength of 90 aspects energy by 2x.
# - Effect is the proportion to the orb from the received aspects cumulative energy accounting with the original polarity.
# - Don't include CE, and include all the planets and MO.
# - Orb decay energy speed is slower to 0.2.
# - MO energy only accounts as cumulative for other aspects.
# - Ignore slow to slow planets aspects energy.
# - Use common daily aspects true energy disregard the historical security effect.
# - Changed orbs for all classic aspects to 5 degrees.
predictSecurityModelI <- function(security) {
# Best effect correlation when using classic aspects only.
setClassicAspectsSet4()
setPlanetsMOMEVESUMAJUSAURNEPL()
dailyHourlyPlanets <<- openHourlyPlanets('planets_11', clear = F)
idCols <- c('Date', 'Hour')
hourlyAspects <- dailyHourlyAspectsTablePrepare(dailyHourlyPlanets, idCols)
hourlyAspects <- dailyAspectsAddEnergy(hourlyAspects, 0.2)
# Ignore slow to slow planets aspects due we lack a complete cycle.
hourlyAspects <- hourlyAspects[p.x %ni% c('JU', 'SA', 'UR', 'NE', 'PL'),]
hourlyAspects <- dailyAspectsAddCumulativeEnergy(hourlyAspects)
# MO only contribute to the cumulative effect but is not a major indicator.
hourlyAspects <- hourlyAspects[p.x != 'MO',]
hourlyAspects <- dailyAspectsAddEffectM3(hourlyAspects)
dailyAspectsIndex <- dailyAspectsEffectIndex(hourlyAspects)
crossValidateModelReport("modelI", dailyAspectsIndex, security)
}
# Combines characteristics of Model E & H.
predictSecurityModelJ <- function(security) {
# Best effect correlation when using classic aspects only.
setClassicAspectsSet3()
setPlanetsMOMEVESUMAJUNNSAURNEPL()
dailyHourlyPlanets <<- openHourlyPlanets('planets_11', clear = F)
idCols <- c('Date', 'Hour')
hourlyAspects <- dailyHourlyAspectsTablePrepare(dailyHourlyPlanets, idCols)
hourlyAspects <- dailyAspectsAddEnergy(hourlyAspects, 0.45)
hourlyAspects <- dailyAspectsAddCumulativeEnergy(hourlyAspects)
# MO only contribute to the cumulative effect but is not a major indicator.
hourlyAspects <- hourlyAspects[p.x != 'MO',]
hourlyAspects <- dailyAspectsAddEffectM1(hourlyAspects)
dailyAspectsIndex <- dailyAspectsEffectIndex(hourlyAspects)
crossValidateModelReport("modelJ", dailyAspectsIndex, security)
}
# Based on ModelH with few variations:
# - Decrease speed from 0.2 to 0.1