R/indicator_technic.R
In scalastic/fin.backend: Financial Computations Backend
Defines functions
indicator_technic.get
Documented in
indicator_technic.get
#' Technical Indicator
#'
#' @name indicator_technic.get
#'
#' @description This function computes all technicals indicators.
#'
#' @param indice_code a character vector indicating the indice code on which computation will be done.
#' @param day_date a character vector indicating the date of the day until which computation will be done.
#' @return a data.table containing the technical indicators.
#'
#' @import data.table
#' @import tm
#' @import NLP
#' @import TTR
#' @export
indicator_technic.get <- function(indice_code, day_date) {
dt <- data_file.load_fchi()[date <= day_date, ]
# Lancement des calculs
#---------------------------------------
# Calcul du Return (specific)
#---------------------------------------
label <- "Return"
result <- diff(dt[,close], lag = 1, differences = 1)
TI_RETURN <- c(NA,result)
#---------------------------------------
# Calcul du Directional Movement Index
#---------------------------------------
label <- "DMI"
result <- ADX(dt[,.(high,low,close)], n=14)
colnames(result) <- c("DMI_dip", "DMI_din", "DMI_dx", "DMI_adx")
TI_DMI <- result
label <- "DMI7"
result <- ADX(dt[,.(high,low,close)], n=7)
colnames(result) <- c("DMI_dip7", "DMI_din7", "DMI_dx7", "DMI_adx7")
TI_DMI7 <- result
label <- "DMI21"
result <- ADX(dt[,.(high,low,close)], n=21)
colnames(result) <- c("DMI_dip21", "DMI_din21", "DMI_dx21", "DMI_adx21")
TI_DMI21 <- result
#-----------------------------------------------------------
# The Aroon indicator attempts to identify starting trends.
#-----------------------------------------------------------
label <- "aroon"
result <- aroon(dt[,.(high,low)], n=20)
colnames(result) <- c("AROON_up", "AROON_dn", "AROON_oscillator")
TI_TREND <- result
#-----------------------------------------------------------------------
# True range (TR) is a measure of volatility of a High-Low-Close series
#-----------------------------------------------------------------------
label <- "ATR"
result <- ATR(dt[,.(high,low,close)], n=14)
colnames(result) <- c("ATR_tr", "ATR_atr", "ATR_truehigh", "ATR_truelow")
TI_ATR <- result
#-----------------------------------------------------------------------
# Bollinger Bands are a way to compare a security's volatility
# and price levels over a period of time.
#-----------------------------------------------------------------------
label <- "BBands"
result <- BBands(dt[,.(high,low,close)])
colnames(result) <- c("BBANDS_dn", "BBANDS_mavg", "BBANDS_up", "BBANDS_pctb")
TI_BBANDS <- result
#-----------------------------------------------------------------------
# The Commodity Channel Index (CCI)
# attempts to identify starting and ending trends.
#-----------------------------------------------------------------------
label <- "CCI"
result <- CCI(dt[,.(high,low,close)])
TI_CCI <- result
#-----------------------------------------------------------------------
# The Chaikin Accumulation / Distribution (AD) line is a measure
# of the money flowing into or out of a security.
#-----------------------------------------------------------------------
label <- "chaikinAD"
result <- chaikinAD(dt[,.(high,low,close)], dt[,volume])
TI_CHAIKINAD <- result
#-----------------------------------------------------------------------
# Chaikin Volatility measures the rate of change of the
# security's trading range.
#-----------------------------------------------------------------------
label <- "chaikinVolatility"
result <- chaikinVolatility(dt[,.(high,low)])
TI_CHAIKINVOLATILITY <- result
#-----------------------------------------------------------------------
# The Close Location Value (CLV) relates the day's
# close to its trading range.
#-----------------------------------------------------------------------
label <- "CLV"
result <- CLV(dt[,.(high,low,close)])
TI_CLV <- result
#-----------------------------------------------------------------------
# Chaikin Money Flow compares total volume over the last
# n time periods to total volume times the Close Location
# Value (CLV) over the last n time periods.
#-----------------------------------------------------------------------
label <- "CMF"
result <- CMF(dt[,.(high,low,close)], dt[,volume])
TI_CMF <- result
#-----------------------------------------------------------------------
# The Chande Momentum Oscillator (CMO) is a modified RSI.
#-----------------------------------------------------------------------
label <- "CMO"
result <- CMO(dt[,close])
TI_CMO <- result
#-----------------------------------------------------------------------
# Donchian Channels were used to generate buy and sell
# signals for the Turtle Trading system.
# DonchianChannel(HL, n = 10, include.lag = FALSE)
#-----------------------------------------------------------------------
label <- "DonchianChannel"
result <- DonchianChannel(dt[,.(high,low)])
colnames(result) <- c("DONCHIANCHANNEL_high","DONCHIANCHANNEL_mid","DONCHIANCHANNEL_low")
TI_DONCHIANCHANNEL <- result
#-----------------------------------------------------------------------
# The DV Intermediate oscillator (DVI) is a very smooth
# momentum oscillator
#-----------------------------------------------------------------------
label <- "DVI"
result <- DVI(dt[,close])
colnames(result) <- c("DVI_mag", "DVI_str", "DVI_dvi")
TI_DVI <- result
#-----------------------------------------------------------------------
# Calculate the Guppy Multiple Moving Average of a series.
#-----------------------------------------------------------------------
label <- "GMMA"
result <- GMMA(dt[,close], short = c(3, 5, 8, 10, 12, 15, 20, 25), long = c(30, 35, 40, 45, 50, 60, 100, 150))
colnames(result) <- c("GMMA_lag3", "GMMA_lag5", "GMMA_lag8", "GMMA_lag10", "GMMA_lag12", "GMMA_lag15", "GMMA_lag20", "GMMA_lag25", "GMMA_lag30", "GMMA_lag35", "GMMA_lag40", "GMMA_lag45", "GMMA_lag50", "GMMA_lag60", "GMMA_lag100", "GMMA_lag150")
# result <- cbind(result, GMMA_diff_3_30 = result[,"GMMA_lag3"] - result[,"GMMA_lag30"])
# result <- cbind(result, GMMA_diff_5_35 = result[,"GMMA_lag5"] - result[,"GMMA_lag35"])
# result <- cbind(result, GMMA_diff_8_40 = result[,"GMMA_lag8"] - result[,"GMMA_lag40"])
# result <- cbind(result, GMMA_diff_10_45 = result[,"GMMA_lag10"] - result[,"GMMA_lag45"])
# result <- cbind(result, GMMA_diff_12_50 = result[,"GMMA_lag12"] - result[,"GMMA_lag50"])
# result <- cbind(result, GMMA_diff_15_60 = result[,"GMMA_lag15"] - result[,"GMMA_lag60"])
# result <- cbind(result, GMMA_diff_20_100 = result[,"GMMA_lag20"] - result[,"GMMA_lag100"])
# result <- cbind(result, GMMA_diff_25_150 = result[,"GMMA_lag25"] - result[,"GMMA_lag150"])
TI_GMMA <- result
#-----------------------------------------------------------------------
# The Know Sure Thing (KST) is a smooth, summed,
# rate of change indicator.
#-----------------------------------------------------------------------
label <- "KST"
result <- KST(dt[,close])
colnames(result) <- c("KST_kst", "KST_signal")
TI_KST <- result
label <- "KST4MA"
result <- KST(dt[,close], maType=list(list(SMA),list(EMA),list(DEMA),list(WMA)))
colnames(result) <- c("KST4MA_kst", "KST4MA_signal")
TI_KST4MA <- result
#-----------------------------------------------------------------------
# The MACD function compares a fast moving average (MA)
# of a series with a slow MA of the same series.
#-----------------------------------------------------------------------
label <- "MACD1"
result <- MACD(dt[,close], nFast = 12, nSlow = 26, nSig = 9, maType="EMA" )
colnames(result) <- c("MACD1_macd", "MACD1_signal")
TI_MACD1 <- result
label <- "MACD2"
result <- MACD(dt[,close], nFast = 12, nSlow = 26, nSig = 9, maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA)))
colnames(result) <- c("MACD2_macd", "MACD2_signal")
TI_MACD2 <- result
#-----------------------------------------------------------------------
# The MFI is a ratio of positive and negative money flow over time.
#-----------------------------------------------------------------------
# label <- "MFI"
# result <- MFI(dt[,.(high,low,close)], dt[,volume])
# TI_MFI <- result
#-----------------------------------------------------------------------
# On Balance Volume (OBV) is a measure of the money flowing
# into or out of a security. It is similar to Chaikin
# Accumulation / Distribution.
#-----------------------------------------------------------------------
label <- "OBV"
result <- OBV(dt[,close], dt[,volume])
TI_OBV <- result
#-----------------------------------------------------------------------
# PBands : Construct (optionally further smoothed and centered )
# volatility bands around prices
#-----------------------------------------------------------------------
label <- "PBands"
result <- PBands(dt[,close])
colnames(result) <- c("PBANDS_dn","PBANDS_center","PBANDS_up")
TI_PBANDS <- result
#-----------------------------------------------------------------------
# Calculate the (rate of) change of a series over
# n periods.
#-----------------------------------------------------------------------
label <- "ROC"
result <- ROC(dt[,close],n = 10)
TI_ROC <- result
label <- "ROC2"
result <- ROC(dt[,close])
TI_ROC2 <- result
label <- "MOM"
result <- momentum(dt[,close],n = 10)
TI_MOM <- result
label <- "MOM2"
result <- momentum(dt[,close])
TI_MOM2 <- result
#-----------------------------------------------------------------------
# The Relative Strength Index (RSI) calculates a ratio of
# the recent upward price movements to the absolute price movement.
#-----------------------------------------------------------------------
# Default case
label <- "RSI"
result <- RSI(dt[,close], n=14)
result <- cbind(result, RSI(dt[,close], n=9))
result <- cbind(result, RSI(dt[,close], n=25))
colnames(result) <- c("RSI14", "RSI9", "RSI25")
TI_RSI <- result
# Case of one 'maType' for both MAs
label <- "RSI_MA1"
result <- RSI(dt[,close], n=14, maType="WMA", wts=dt[,volume])
TI_RSI_MA1 <- result
# Case of two different 'maType's for both MAs
label <- "RSI_MA2"
result <- RSI(dt[,close], n=14, maType=list(maUp=list(EMA), maDown=list(WMA)))
TI_RSI_MA2 <- result
#-----------------------------------------------------------------------
# The Parabolic Stop-and-Reverse calculates a trailing stop.
#-----------------------------------------------------------------------
label <- "SAR"
result <- SAR(dt[,.(high,low)])
colnames(result) <- c("SAR")
TI_SAR <- result
#-----------------------------------------------------------------------
# Calculate various moving averages (MA) of a series.
#-----------------------------------------------------------------------
label <- "SMA"
result <- SMA(dt[,close], n=20)
TI_SMA <- result
label <- "SMA10"
result <- SMA(dt[,close], n=10)
TI_SMA10 <- result
label <- "EMA"
result <- EMA(dt[,close], n=20)
TI_EMA <- result
label <- "EMA10"
result <- EMA(dt[,close], n=10)
TI_EMA10 <- result
label <- "DEMA"
result <- DEMA(dt[,close], n=20)
TI_DEMA <- result
label <- "DEMA10"
result <- DEMA(dt[,close], n=10)
TI_DEMA10 <- result
label <- "WMA"
result <- WMA(dt[,close], n=20)
TI_WMA <- result
label <- "WMA10"
result <- WMA(dt[,close], n=10)
TI_WMA10 <- result
label <- "EVWMA"
result <- EVWMA(dt[,close], dt[,volume], n=20)
TI_EVWMA <- result
label <- "EVWMA10"
result <- EVWMA(dt[,close], dt[,volume], n=10)
TI_EVWMA10 <- result
label <- "ZLEMA"
result <- ZLEMA(dt[,close], n=20)
TI_ZLEMA <- result
label <- "ZLEMA10"
result <- ZLEMA(dt[,close], n=10)
TI_ZLEMA10 <- result
label <- "VWAP"
result <- VWAP(dt[,close], dt[,volume], n=20)
TI_VWAP <- result
label <- "VWAP10"
result <- VWAP(dt[,close], dt[,volume], n=10)
TI_VWAP10 <- result
label <- "HMA"
result <- HMA(dt[,close], n=20)
TI_HMA <- result
label <- "HMA10"
result <- HMA(dt[,close], n=10)
TI_HMA10 <- result
label <- "ALMA"
result <- ALMA(dt[,close], n=20)
TI_ALMA <- result
label <- "ALMA10"
result <- ALMA(dt[,close], n=10)
TI_ALMA10 <- result
#-----------------------------------------------------------------------
# The stochastic oscillator is a momentum indicator that
# relates the location of each day's close relative
# to the high/low range over the past n periods
#-----------------------------------------------------------------------
label <- "stoch"
result <- stoch(dt[,.(high,low,close)])
colnames(result) <- c("STOCH_fastk","STOCH_fastd","STOCH_slowd")
TI_STOCH <- result
label <- "SMI"
result <- SMI(dt[,.(high,low,close)],
maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA)) )
colnames(result) <- c("SMI_smi","SMI_signal")
TI_SMI <- result
label <- "stochRSI"
result <- stoch( RSI(dt[,close]) )
colnames(result) <- c("STOCHRSI_fastk","STOCHRSI_fastd","STOCHRSI_slowd")
TI_STOCHRSI <- result
label <- "WPR"
result <- WPR(dt[,.(high,low,close)])
TI_WPR <- result
#-----------------------------------------------------------------------
# Log Price
#-----------------------------------------------------------------------
label <- "log_Price"
result <- log(dt[,close])
TI_LOG_PRICE <- result
#-----------------------------------------------------------------------
# TRIX
#-----------------------------------------------------------------------
label <- "TRIX"
result <- TRIX(dt[,close], maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA), list(DEMA)))
colnames(result) <- c("TRIX_trix","TRIX_signal")
TI_TRIX <- result
#-----------------------------------------------------------------------
# TDI : The Trend Detection Index (TDI) attempts to identify starting and ending trends.
#-----------------------------------------------------------------------
label <- "TDI"
result <- TDI(dt[,close], n=20)
colnames(result) <- c("TDI_tdi","TDI_di")
TI_TDI <- result
#-----------------------------------------------------------------------
# VHF : The Vertical Horizontal Filter (VHF) attempts to identify starting and ending trends.
#-----------------------------------------------------------------------
label <- "VHF"
result <- VHF(dt[,close], n=20)
TI_VHF <- result
#-----------------------------------------------------------------------
# EMV : Arms' Ease of Movement Value (EMV) emphasizes days where the
# security moves easily and minimizes days where the security does not move easily.
#-----------------------------------------------------------------------
label <- "EMV"
result <- EMV(dt[,.(high, low)], dt[,volume])
colnames(result) <- c("EMV_emv","EMV_maemv")
TI_EMV <- result
#-----------------------------------------------------------------------
# Pivots : undocumented...
#-----------------------------------------------------------------------
# label <- "PIVOT"
# result <- TTR:::pivots(dt[,.(high, low, close)], lagts=FALSE)
# TI_PIVOT <- result
#-----------------------------------------------------------------------
# AGGREGATION DES RESULTATS
#-----------------------------------------------------------------------
TECHNICALS_INDICATORS <-
data.table(date = dt[,date],
TI_DMI, TI_DMI7, TI_DMI21, TI_TREND, TI_ATR,TI_BBANDS,TI_STOCH,TI_DONCHIANCHANNEL,TI_DVI,
TI_GMMA,TI_KST,TI_KST4MA,TI_MACD1,TI_MACD2,
TI_PBANDS,
CCI = TI_CCI,
chaikinAD = TI_CHAIKINAD,
chaikinVol = TI_CHAIKINVOLATILITY,
CLV = TI_CLV,
CMF = TI_CMF,
CMO = TI_CMO,
#MFI = TI_MFI,
OBV = TI_OBV,
ROC = TI_ROC,
ROC2 = TI_ROC2,
MOM = TI_MOM,
MOM2 = TI_MOM2,
TI_RSI,
RSI_MA1 = TI_RSI_MA1,
RSI_MA2 = TI_RSI_MA2,
TI_SAR,
SMA = TI_SMA,
SMA10 = TI_SMA10,
EMA = TI_EMA,
EMA10 = TI_EMA10,
DEMA = TI_DEMA,
DEMA10 = TI_DEMA10,
WMA = TI_WMA,
WMA10 = TI_WMA10,
EVWMA = TI_EVWMA,
EVWMA10 = TI_EVWMA10,
ZLEMA = TI_ZLEMA,
ZLEMA10 = TI_ZLEMA10,
VWAP = TI_VWAP,
VWAP10 = TI_VWAP10,
HMA = TI_HMA,
HMA10 = TI_HMA10,
#ALMA = TI_ALMA,
#ALMA10 = TI_ALMA10,
WPR = TI_WPR,
PNL = TI_RETURN,
log_Price = TI_LOG_PRICE,
TI_TRIX,
TI_TDI,
TI_SMI,
TI_STOCHRSI,
TI_EMV,
TI_VHF
)
setkey(TECHNICALS_INDICATORS, date)
return(TECHNICALS_INDICATORS)
}
scalastic/fin.backend documentation built on Nov. 18, 2022, 7:10 a.m.
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Defines functions indicator_technic.get
Documented in indicator_technic.get
#' Technical Indicator
#'
#' @name indicator_technic.get
#'
#' @description This function computes all technicals indicators.
#'
#' @param indice_code a character vector indicating the indice code on which computation will be done.
#' @param day_date a character vector indicating the date of the day until which computation will be done.
#' @return a data.table containing the technical indicators.
#'
#' @import data.table
#' @import tm
#' @import NLP
#' @import TTR
#' @export
indicator_technic.get <- function(indice_code, day_date) {
dt <- data_file.load_fchi()[date <= day_date, ]
# Lancement des calculs
#---------------------------------------
# Calcul du Return (specific)
#---------------------------------------
label <- "Return"
result <- diff(dt[,close], lag = 1, differences = 1)
TI_RETURN <- c(NA,result)
#---------------------------------------
# Calcul du Directional Movement Index
#---------------------------------------
label <- "DMI"
result <- ADX(dt[,.(high,low,close)], n=14)
colnames(result) <- c("DMI_dip", "DMI_din", "DMI_dx", "DMI_adx")
TI_DMI <- result
label <- "DMI7"
result <- ADX(dt[,.(high,low,close)], n=7)
colnames(result) <- c("DMI_dip7", "DMI_din7", "DMI_dx7", "DMI_adx7")
TI_DMI7 <- result
label <- "DMI21"
result <- ADX(dt[,.(high,low,close)], n=21)
colnames(result) <- c("DMI_dip21", "DMI_din21", "DMI_dx21", "DMI_adx21")
TI_DMI21 <- result
#-----------------------------------------------------------
# The Aroon indicator attempts to identify starting trends.
#-----------------------------------------------------------
label <- "aroon"
result <- aroon(dt[,.(high,low)], n=20)
colnames(result) <- c("AROON_up", "AROON_dn", "AROON_oscillator")
TI_TREND <- result
#-----------------------------------------------------------------------
# True range (TR) is a measure of volatility of a High-Low-Close series
#-----------------------------------------------------------------------
label <- "ATR"
result <- ATR(dt[,.(high,low,close)], n=14)
colnames(result) <- c("ATR_tr", "ATR_atr", "ATR_truehigh", "ATR_truelow")
TI_ATR <- result
#-----------------------------------------------------------------------
# Bollinger Bands are a way to compare a security's volatility
# and price levels over a period of time.
#-----------------------------------------------------------------------
label <- "BBands"
result <- BBands(dt[,.(high,low,close)])
colnames(result) <- c("BBANDS_dn", "BBANDS_mavg", "BBANDS_up", "BBANDS_pctb")
TI_BBANDS <- result
#-----------------------------------------------------------------------
# The Commodity Channel Index (CCI)
# attempts to identify starting and ending trends.
#-----------------------------------------------------------------------
label <- "CCI"
result <- CCI(dt[,.(high,low,close)])
TI_CCI <- result
#-----------------------------------------------------------------------
# The Chaikin Accumulation / Distribution (AD) line is a measure
# of the money flowing into or out of a security.
#-----------------------------------------------------------------------
label <- "chaikinAD"
result <- chaikinAD(dt[,.(high,low,close)], dt[,volume])
TI_CHAIKINAD <- result
#-----------------------------------------------------------------------
# Chaikin Volatility measures the rate of change of the
# security's trading range.
#-----------------------------------------------------------------------
label <- "chaikinVolatility"
result <- chaikinVolatility(dt[,.(high,low)])
TI_CHAIKINVOLATILITY <- result
#-----------------------------------------------------------------------
# The Close Location Value (CLV) relates the day's
# close to its trading range.
#-----------------------------------------------------------------------
label <- "CLV"
result <- CLV(dt[,.(high,low,close)])
TI_CLV <- result
#-----------------------------------------------------------------------
# Chaikin Money Flow compares total volume over the last
# n time periods to total volume times the Close Location
# Value (CLV) over the last n time periods.
#-----------------------------------------------------------------------
label <- "CMF"
result <- CMF(dt[,.(high,low,close)], dt[,volume])
TI_CMF <- result
#-----------------------------------------------------------------------
# The Chande Momentum Oscillator (CMO) is a modified RSI.
#-----------------------------------------------------------------------
label <- "CMO"
result <- CMO(dt[,close])
TI_CMO <- result
#-----------------------------------------------------------------------
# Donchian Channels were used to generate buy and sell
# signals for the Turtle Trading system.
# DonchianChannel(HL, n = 10, include.lag = FALSE)
#-----------------------------------------------------------------------
label <- "DonchianChannel"
result <- DonchianChannel(dt[,.(high,low)])
colnames(result) <- c("DONCHIANCHANNEL_high","DONCHIANCHANNEL_mid","DONCHIANCHANNEL_low")
TI_DONCHIANCHANNEL <- result
#-----------------------------------------------------------------------
# The DV Intermediate oscillator (DVI) is a very smooth
# momentum oscillator
#-----------------------------------------------------------------------
label <- "DVI"
result <- DVI(dt[,close])
colnames(result) <- c("DVI_mag", "DVI_str", "DVI_dvi")
TI_DVI <- result
#-----------------------------------------------------------------------
# Calculate the Guppy Multiple Moving Average of a series.
#-----------------------------------------------------------------------
label <- "GMMA"
result <- GMMA(dt[,close], short = c(3, 5, 8, 10, 12, 15, 20, 25), long = c(30, 35, 40, 45, 50, 60, 100, 150))
colnames(result) <- c("GMMA_lag3", "GMMA_lag5", "GMMA_lag8", "GMMA_lag10", "GMMA_lag12", "GMMA_lag15", "GMMA_lag20", "GMMA_lag25", "GMMA_lag30", "GMMA_lag35", "GMMA_lag40", "GMMA_lag45", "GMMA_lag50", "GMMA_lag60", "GMMA_lag100", "GMMA_lag150")
# result <- cbind(result, GMMA_diff_3_30 = result[,"GMMA_lag3"] - result[,"GMMA_lag30"])
# result <- cbind(result, GMMA_diff_5_35 = result[,"GMMA_lag5"] - result[,"GMMA_lag35"])
# result <- cbind(result, GMMA_diff_8_40 = result[,"GMMA_lag8"] - result[,"GMMA_lag40"])
# result <- cbind(result, GMMA_diff_10_45 = result[,"GMMA_lag10"] - result[,"GMMA_lag45"])
# result <- cbind(result, GMMA_diff_12_50 = result[,"GMMA_lag12"] - result[,"GMMA_lag50"])
# result <- cbind(result, GMMA_diff_15_60 = result[,"GMMA_lag15"] - result[,"GMMA_lag60"])
# result <- cbind(result, GMMA_diff_20_100 = result[,"GMMA_lag20"] - result[,"GMMA_lag100"])
# result <- cbind(result, GMMA_diff_25_150 = result[,"GMMA_lag25"] - result[,"GMMA_lag150"])
TI_GMMA <- result
#-----------------------------------------------------------------------
# The Know Sure Thing (KST) is a smooth, summed,
# rate of change indicator.
#-----------------------------------------------------------------------
label <- "KST"
result <- KST(dt[,close])
colnames(result) <- c("KST_kst", "KST_signal")
TI_KST <- result
label <- "KST4MA"
result <- KST(dt[,close], maType=list(list(SMA),list(EMA),list(DEMA),list(WMA)))
colnames(result) <- c("KST4MA_kst", "KST4MA_signal")
TI_KST4MA <- result
#-----------------------------------------------------------------------
# The MACD function compares a fast moving average (MA)
# of a series with a slow MA of the same series.
#-----------------------------------------------------------------------
label <- "MACD1"
result <- MACD(dt[,close], nFast = 12, nSlow = 26, nSig = 9, maType="EMA" )
colnames(result) <- c("MACD1_macd", "MACD1_signal")
TI_MACD1 <- result
label <- "MACD2"
result <- MACD(dt[,close], nFast = 12, nSlow = 26, nSig = 9, maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA)))
colnames(result) <- c("MACD2_macd", "MACD2_signal")
TI_MACD2 <- result
#-----------------------------------------------------------------------
# The MFI is a ratio of positive and negative money flow over time.
#-----------------------------------------------------------------------
# label <- "MFI"
# result <- MFI(dt[,.(high,low,close)], dt[,volume])
# TI_MFI <- result
#-----------------------------------------------------------------------
# On Balance Volume (OBV) is a measure of the money flowing
# into or out of a security. It is similar to Chaikin
# Accumulation / Distribution.
#-----------------------------------------------------------------------
label <- "OBV"
result <- OBV(dt[,close], dt[,volume])
TI_OBV <- result
#-----------------------------------------------------------------------
# PBands : Construct (optionally further smoothed and centered )
# volatility bands around prices
#-----------------------------------------------------------------------
label <- "PBands"
result <- PBands(dt[,close])
colnames(result) <- c("PBANDS_dn","PBANDS_center","PBANDS_up")
TI_PBANDS <- result
#-----------------------------------------------------------------------
# Calculate the (rate of) change of a series over
# n periods.
#-----------------------------------------------------------------------
label <- "ROC"
result <- ROC(dt[,close],n = 10)
TI_ROC <- result
label <- "ROC2"
result <- ROC(dt[,close])
TI_ROC2 <- result
label <- "MOM"
result <- momentum(dt[,close],n = 10)
TI_MOM <- result
label <- "MOM2"
result <- momentum(dt[,close])
TI_MOM2 <- result
#-----------------------------------------------------------------------
# The Relative Strength Index (RSI) calculates a ratio of
# the recent upward price movements to the absolute price movement.
#-----------------------------------------------------------------------
# Default case
label <- "RSI"
result <- RSI(dt[,close], n=14)
result <- cbind(result, RSI(dt[,close], n=9))
result <- cbind(result, RSI(dt[,close], n=25))
colnames(result) <- c("RSI14", "RSI9", "RSI25")
TI_RSI <- result
# Case of one 'maType' for both MAs
label <- "RSI_MA1"
result <- RSI(dt[,close], n=14, maType="WMA", wts=dt[,volume])
TI_RSI_MA1 <- result
# Case of two different 'maType's for both MAs
label <- "RSI_MA2"
result <- RSI(dt[,close], n=14, maType=list(maUp=list(EMA), maDown=list(WMA)))
TI_RSI_MA2 <- result
#-----------------------------------------------------------------------
# The Parabolic Stop-and-Reverse calculates a trailing stop.
#-----------------------------------------------------------------------
label <- "SAR"
result <- SAR(dt[,.(high,low)])
colnames(result) <- c("SAR")
TI_SAR <- result
#-----------------------------------------------------------------------
# Calculate various moving averages (MA) of a series.
#-----------------------------------------------------------------------
label <- "SMA"
result <- SMA(dt[,close], n=20)
TI_SMA <- result
label <- "SMA10"
result <- SMA(dt[,close], n=10)
TI_SMA10 <- result
label <- "EMA"
result <- EMA(dt[,close], n=20)
TI_EMA <- result
label <- "EMA10"
result <- EMA(dt[,close], n=10)
TI_EMA10 <- result
label <- "DEMA"
result <- DEMA(dt[,close], n=20)
TI_DEMA <- result
label <- "DEMA10"
result <- DEMA(dt[,close], n=10)
TI_DEMA10 <- result
label <- "WMA"
result <- WMA(dt[,close], n=20)
TI_WMA <- result
label <- "WMA10"
result <- WMA(dt[,close], n=10)
TI_WMA10 <- result
label <- "EVWMA"
result <- EVWMA(dt[,close], dt[,volume], n=20)
TI_EVWMA <- result
label <- "EVWMA10"
result <- EVWMA(dt[,close], dt[,volume], n=10)
TI_EVWMA10 <- result
label <- "ZLEMA"
result <- ZLEMA(dt[,close], n=20)
TI_ZLEMA <- result
label <- "ZLEMA10"
result <- ZLEMA(dt[,close], n=10)
TI_ZLEMA10 <- result
label <- "VWAP"
result <- VWAP(dt[,close], dt[,volume], n=20)
TI_VWAP <- result
label <- "VWAP10"
result <- VWAP(dt[,close], dt[,volume], n=10)
TI_VWAP10 <- result
label <- "HMA"
result <- HMA(dt[,close], n=20)
TI_HMA <- result
label <- "HMA10"
result <- HMA(dt[,close], n=10)
TI_HMA10 <- result
label <- "ALMA"
result <- ALMA(dt[,close], n=20)
TI_ALMA <- result
label <- "ALMA10"
result <- ALMA(dt[,close], n=10)
TI_ALMA10 <- result
#-----------------------------------------------------------------------
# The stochastic oscillator is a momentum indicator that
# relates the location of each day's close relative
# to the high/low range over the past n periods
#-----------------------------------------------------------------------
label <- "stoch"
result <- stoch(dt[,.(high,low,close)])
colnames(result) <- c("STOCH_fastk","STOCH_fastd","STOCH_slowd")
TI_STOCH <- result
label <- "SMI"
result <- SMI(dt[,.(high,low,close)],
maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA)) )
colnames(result) <- c("SMI_smi","SMI_signal")
TI_SMI <- result
label <- "stochRSI"
result <- stoch( RSI(dt[,close]) )
colnames(result) <- c("STOCHRSI_fastk","STOCHRSI_fastd","STOCHRSI_slowd")
TI_STOCHRSI <- result
label <- "WPR"
result <- WPR(dt[,.(high,low,close)])
TI_WPR <- result
#-----------------------------------------------------------------------
# Log Price
#-----------------------------------------------------------------------
label <- "log_Price"
result <- log(dt[,close])
TI_LOG_PRICE <- result
#-----------------------------------------------------------------------
# TRIX
#-----------------------------------------------------------------------
label <- "TRIX"
result <- TRIX(dt[,close], maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA), list(DEMA)))
colnames(result) <- c("TRIX_trix","TRIX_signal")
TI_TRIX <- result
#-----------------------------------------------------------------------
# TDI : The Trend Detection Index (TDI) attempts to identify starting and ending trends.
#-----------------------------------------------------------------------
label <- "TDI"
result <- TDI(dt[,close], n=20)
colnames(result) <- c("TDI_tdi","TDI_di")
TI_TDI <- result
#-----------------------------------------------------------------------
# VHF : The Vertical Horizontal Filter (VHF) attempts to identify starting and ending trends.
#-----------------------------------------------------------------------
label <- "VHF"
result <- VHF(dt[,close], n=20)
TI_VHF <- result
#-----------------------------------------------------------------------
# EMV : Arms' Ease of Movement Value (EMV) emphasizes days where the
# security moves easily and minimizes days where the security does not move easily.
#-----------------------------------------------------------------------
label <- "EMV"
result <- EMV(dt[,.(high, low)], dt[,volume])
colnames(result) <- c("EMV_emv","EMV_maemv")
TI_EMV <- result
#-----------------------------------------------------------------------
# Pivots : undocumented...
#-----------------------------------------------------------------------
# label <- "PIVOT"
# result <- TTR:::pivots(dt[,.(high, low, close)], lagts=FALSE)
# TI_PIVOT <- result
#-----------------------------------------------------------------------
# AGGREGATION DES RESULTATS
#-----------------------------------------------------------------------
TECHNICALS_INDICATORS <-
data.table(date = dt[,date],
TI_DMI, TI_DMI7, TI_DMI21, TI_TREND, TI_ATR,TI_BBANDS,TI_STOCH,TI_DONCHIANCHANNEL,TI_DVI,
TI_GMMA,TI_KST,TI_KST4MA,TI_MACD1,TI_MACD2,
TI_PBANDS,
CCI = TI_CCI,
chaikinAD = TI_CHAIKINAD,
chaikinVol = TI_CHAIKINVOLATILITY,
CLV = TI_CLV,
CMF = TI_CMF,
CMO = TI_CMO,
#MFI = TI_MFI,
OBV = TI_OBV,
ROC = TI_ROC,
ROC2 = TI_ROC2,
MOM = TI_MOM,
MOM2 = TI_MOM2,
TI_RSI,
RSI_MA1 = TI_RSI_MA1,
RSI_MA2 = TI_RSI_MA2,
TI_SAR,
SMA = TI_SMA,
SMA10 = TI_SMA10,
EMA = TI_EMA,
EMA10 = TI_EMA10,
DEMA = TI_DEMA,
DEMA10 = TI_DEMA10,
WMA = TI_WMA,
WMA10 = TI_WMA10,
EVWMA = TI_EVWMA,
EVWMA10 = TI_EVWMA10,
ZLEMA = TI_ZLEMA,
ZLEMA10 = TI_ZLEMA10,
VWAP = TI_VWAP,
VWAP10 = TI_VWAP10,
HMA = TI_HMA,
HMA10 = TI_HMA10,
#ALMA = TI_ALMA,
#ALMA10 = TI_ALMA10,
WPR = TI_WPR,
PNL = TI_RETURN,
log_Price = TI_LOG_PRICE,
TI_TRIX,
TI_TDI,
TI_SMI,
TI_STOCHRSI,
TI_EMV,
TI_VHF
)
setkey(TECHNICALS_INDICATORS, date)
return(TECHNICALS_INDICATORS)
}
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